Commit from Azure DevOps update Results

Qwen/Qwen3-1.7B-autoround-W4A16/results_2026-04-24-10-24-37.json

@@ -0,0 +1,88 @@
+{
+  "pipeline": "auto_quant",
+  "model_id": "Qwen/Qwen3-1.7B",
+  "artifact_name": "Qwen3-1.7B-autoround-W4A16",
+  "generated_at": "2026-04-24T10:24:37Z",
+  "source_runtime_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+  "source_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "run_dir": "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37",
+  "quant_summary": {
+    "model_id": "Qwen/Qwen3-1.7B",
+    "scheme": "W4A16",
+    "method": "RTN",
+    "export_format": "auto_round",
+    "device": "cuda",
+    "quant_num_gpus": "1",
+    "num_gpus": "1",
+    "output_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+    "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+    "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+    "status": "success",
+    "duration_seconds": 89.69453978538513,
+    "original_size_mb": 3400.0,
+    "quantized_size_mb": 1303.0159664154053,
+    "compression_ratio": 2.609331034794143,
+    "errors": [],
+    "solutions": [],
+    "output_files": [
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/chat_template.jinja",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/config.json",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/generation_config.json",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00001-of-00002.safetensors",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00002-of-00002.safetensors",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model.safetensors.index.json",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/quantization_config.json",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer.json",
+      "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer_config.json"
+    ],
+    "hf_repo": "https://huggingface.co/lvkaokao/Qwen3-1.7B-autoround-W4A16",
+    "hf_account": "lvkaokao",
+    "hf_account_id": "lvkaokao",
+    "hf_shared_ledger_enabled": false,
+    "hf_usage_file": "/root/leaderboard_Agent/tasks/lb_eval/auto_quant/hf_account_usage.json",
+    "hf_remaining_gb": 98.73,
+    "upload_time": "2026-04-24T10:24:36Z"
+  },
+  "accuracy": {
+    "model_id": "Qwen/Qwen3-1.7B",
+    "model_path": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+    "scheme": "W4A16",
+    "device": "cuda:0",
+    "num_gpus": "1",
+    "tasks": {
+      "piqa": {
+        "accuracy": 0.71,
+        "accuracy_stderr": 0.0106
+      },
+      "hellaswag": {
+        "accuracy": 0.4415,
+        "accuracy_stderr": 0.005
+      },
+      "mmlu": {
+        "accuracy": 0.5291,
+        "accuracy_stderr": 0.0041
+      },
+      "gsm8k": {
+        "accuracy": 0.5807,
+        "accuracy_stderr": 0.0136
+      }
+    },
+    "status": "success",
+    "duration_seconds": 3030.05,
+    "eval_framework": "lm_eval+hf",
+    "errors": [],
+    "eval_num_gpus": "1"
+  },
+  "copied_files": [
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/quant_summary.json",
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/accuracy.json",
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/quantize.py",
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/logs",
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/session_eval_320.jsonl",
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/session_quant_320.jsonl",
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/session_eval_320.md",
+    "results/Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/session_quant_320.md"
+  ],
+  "quant_num_gpus": "1",
+  "eval_num_gpus": "1"
+}

Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/accuracy.json

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+{
+  "model_id": "Qwen/Qwen3-1.7B",
+  "model_path": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "scheme": "W4A16",
+  "device": "cuda:0",
+  "num_gpus": "1",
+  "tasks": {
+    "piqa": {
+      "accuracy": 0.71,
+      "accuracy_stderr": 0.0106
+    },
+    "hellaswag": {
+      "accuracy": 0.4415,
+      "accuracy_stderr": 0.005
+    },
+    "mmlu": {
+      "accuracy": 0.5291,
+      "accuracy_stderr": 0.0041
+    },
+    "gsm8k": {
+      "accuracy": 0.5807,
+      "accuracy_stderr": 0.0136
+    }
+  },
+  "status": "success",
+  "duration_seconds": 3030.05,
+  "eval_framework": "lm_eval+hf",
+  "errors": [],
+  "eval_num_gpus": "1"
+}

Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/logs/eval_prompt.txt

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+You are an expert in evaluating quantized LLM models.
+You MUST follow the skill instructions in: /root/.openclaw/workspace/skills/auto_eval/SKILL.md
+
+Quantized model path: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16
+Evaluation tasks: piqa,mmlu,hellaswag,gsm8k
+Batch size: 8
+Num gpus: 1
+
+The quantized model was produced by auto_quant with scheme=W4A16, export_format=auto_round.
+A venv may already exist at /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv (created by auto_quant with --system-site-packages).
+
+CRITICAL ENVIRONMENT NOTE:
+- System Python has torch+cuda pre-installed. When creating venvs, ALWAYS use:
+    python3 -m venv --system-site-packages <path>
+  This ensures the venv inherits torch+cuda. Do NOT pip install torch inside the venv.
+- If /root/.venv exists, reuse /root/.venv before creating a new venv.
+- If a venv already exists at /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv, reuse it - just install lm_eval and vllm into it.
+- Use uv pip for dependency installation. Prefer:
+    uv pip install --python <venv>/bin/python <packages>
+- Do NOT reinstall torch or flash_attn if they already import successfully from the reused environment. Only install them when missing or incompatible.
+- Write evaluation outputs, logs, prompts, copied request/session files, and other runtime artifacts to: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16
+
+IMPORTANT - After evaluation completes, you MUST produce:
+
+/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/accuracy.json - evaluation results:
+{
+  "model_id": "Qwen/Qwen3-1.7B",
+  "model_path": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "scheme": "W4A16",
+  "device": "cuda:0",
+  "num_gpus": "1",
+  "tasks": {
+    "<task_name>": {
+      "accuracy": <float>,
+      "accuracy_stderr": <float or null>
+    }
+  },
+  "status": "success" or "failed",
+  "duration_seconds": <float>,
+  "eval_framework": "lm_eval+vllm" or "lm_eval+hf" or "manual",
+  "errors": [<list of error strings if any>]
+}
+
+The accuracy values MUST be real numbers from actual evaluation runs.
+Write as valid JSON. If evaluation fails, still write accuracy.json with status=failed.

Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/logs/quant_prompt.txt

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+You are an expert in LLM quantization using the Intel Auto-Round toolkit.
+You MUST follow the skill instructions in: /root/.openclaw/workspace/skills/auto_quant/SKILL.md
+
+Model: Qwen/Qwen3-1.7B
+Quantization: W4A16 / RTN
+Export format: auto_round
+Quantized Model Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16
+Runtime device: cuda
+Num gpus: 1
+
+Directory responsibilities:
+- Write exported model files to: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+- Write runtime artifacts such as quant_summary.json, quantize.py, logs, prompts, copied request/session files, and the venv to: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16
+
+CRITICAL SCRIPT REQUIREMENT:
+- Before starting quantization, you MUST first generate the quantization script file:
+    /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quantize.py
+- The file name must be exactly: quantize.py
+- Run quantization by executing that generated quantize.py script
+- Do not use quantize_script.py as the final artifact name
+
+CRITICAL ENVIRONMENT NOTE:
+- System Python has torch+cuda pre-installed. When creating venvs, ALWAYS use:
+    python3 -m venv --system-site-packages <path>
+  This ensures the venv inherits torch+cuda. Do NOT pip install torch inside the venv.
+- If /root/.venv exists, reuse /root/.venv before creating a new venv.
+- Use uv pip for dependency installation. Prefer:
+    uv pip install --python <venv>/bin/python <packages>
+- Do NOT reinstall torch or flash_attn if they already import successfully from the reused environment. Only install them when missing or incompatible.
+- This workflow is CUDA-focused. For AutoRound device selection:
+    - if Num gpus == 1, prefer device="cuda"
+    - if Num gpus > 1, prefer device_map="auto"
+  Do NOT default to device_map="0" or device_map="0,1,2,3" unless manual mapping is truly required after auto placement fails.
+
+IMPORTANT - After quantization completes (success or failure), you MUST produce:
+
+/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json - structured summary:
+{
+  "model_id": "Qwen/Qwen3-1.7B",
+  "scheme": "W4A16",
+  "method": "RTN",
+  "export_format": "auto_round",
+  "device": "cuda",
+  "quant_num_gpus": "1",
+  "num_gpus": "1",
+  "output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "status": "success" or "failed",
+  "duration_seconds": <float>,
+  "original_size_mb": <float or null>,
+  "quantized_size_mb": <float or null>,
+  "compression_ratio": <float or null>,
+  "errors": [<list of error strings>],
+  "solutions": [<list of solution strings>],
+  "output_files": [<list of file paths in runtime_output_dir>]
+}
+
+Write as valid JSON. If quantization fails, still write quant_summary.json with status=failed.

Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/quant_summary.json

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+{
+  "model_id": "Qwen/Qwen3-1.7B",
+  "scheme": "W4A16",
+  "method": "RTN",
+  "export_format": "auto_round",
+  "device": "cuda",
+  "quant_num_gpus": "1",
+  "num_gpus": "1",
+  "output_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "status": "success",
+  "duration_seconds": 89.69453978538513,
+  "original_size_mb": 3400.0,
+  "quantized_size_mb": 1303.0159664154053,
+  "compression_ratio": 2.609331034794143,
+  "errors": [],
+  "solutions": [],
+  "output_files": [
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/chat_template.jinja",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/config.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/generation_config.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00001-of-00002.safetensors",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00002-of-00002.safetensors",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model.safetensors.index.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/quantization_config.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer_config.json"
+  ],
+  "hf_repo": "https://huggingface.co/lvkaokao/Qwen3-1.7B-autoround-W4A16",
+  "hf_account": "lvkaokao",
+  "hf_account_id": "lvkaokao",
+  "hf_shared_ledger_enabled": false,
+  "hf_usage_file": "/root/leaderboard_Agent/tasks/lb_eval/auto_quant/hf_account_usage.json",
+  "hf_remaining_gb": 98.73,
+  "upload_time": "2026-04-24T10:24:36Z"
+}

Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/quantize.py

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+#!/usr/bin/env python3
+"""
+Auto-Round Quantization Script
+Generated by auto_quant skill
+
+Model: Qwen/Qwen3-1.7B
+Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+Scheme: W4A16
+Iterations: 0 (RTN mode)
+Samples: 128
+Format: auto_round
+"""
+
+import os
+import sys
+import time
+import json
+import traceback
+from pathlib import Path
+
+# Track start time
+start_time = time.time()
+
+# Output directories
+OUTPUT_DIR = "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16"
+RUN_DIR = "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16"
+VENV_BIN = f"{RUN_DIR}/venv/bin/python"
+
+# Track errors and solutions
+errors = []
+solutions = []
+
+print(f"Starting quantization at {time.strftime('%Y-%m-%d %H:%M:%S')}")
+print(f"Model: Qwen/Qwen3-1.7B")
+print(f"Scheme: W4A16")
+print(f"Method: RTN (iters=0)")
+print(f"Format: auto_round")
+print(f"Device: cuda (single GPU)")
+print(f"Output: {OUTPUT_DIR}")
+
+try:
+    from auto_round import AutoRound
+
+    # Configuration
+    model_name_or_path = "Qwen/Qwen3-1.7B"
+    scheme = "W4A16"
+    iters = 0  # RTN mode
+    nsamples = 128
+    format_str = "auto_round"
+
+    print(f"\nLoading model: {model_name_or_path}")
+    print(f"Scheme: {scheme}")
+    print(f"Iters: {iters} (RTN mode)")
+    print(f"nsamples: {nsamples}")
+    print(f"Format: {format_str}")
+    print(f"Device: cuda")
+
+    # Create AutoRound instance
+    ar = AutoRound(
+        model_name_or_path,
+        scheme=scheme,
+        iters=iters,
+        nsamples=nsamples,
+        device="cuda",
+    )
+
+    # Quantize and save
+    print("\nStarting quantization...")
+    ar.quantize_and_save(output_dir=OUTPUT_DIR, format=format_str)
+
+    print(f"\nQuantization complete! Output: {OUTPUT_DIR}")
+
+except Exception as e:
+    errors.append(str(e))
+    errors.append(traceback.format_exc())
+    print(f"\nQuantization failed: {e}")
+    traceback.print_exc()
+
+    # Try recovery solutions
+    try:
+        solutions.append("Attempted to use RTN mode (iters=0) for stable quantization")
+    except:
+        pass
+
+end_time = time.time()
+duration = end_time - start_time
+
+# Calculate sizes
+original_size_mb = None
+quantized_size_mb = None
+compression_ratio = None
+
+quantized_path = Path(OUTPUT_DIR)
+if quantized_path.exists():
+    total_size = sum(f.stat().st_size for f in quantized_path.rglob("*") if f.is_file())
+    quantized_size_mb = total_size / (1024 * 1024)
+
+# Estimate original model size (Qwen3-1.7B in BF16 ~3.4GB)
+original_size_mb = 3400.0  # Approximate BF16 size for 1.7B model in MB
+if quantized_size_mb and original_size_mb:
+    compression_ratio = original_size_mb / quantized_size_mb
+
+# List output files
+output_files = []
+if quantized_path.exists():
+    for f in sorted(quantized_path.rglob("*")):
+        if f.is_file() and not f.name.endswith(('.pyc', '.pyo', '__pycache__')):
+            size = f.stat().st_size
+            output_files.append(str(f))
+
+# Determine status
+status = "failed" if errors else "success"
+
+# Build summary
+summary = {
+    "model_id": "Qwen/Qwen3-1.7B",
+    "scheme": "W4A16",
+    "method": "RTN",
+    "export_format": "auto_round",
+    "device": "cuda",
+    "quant_num_gpus": "1",
+    "num_gpus": "1",
+    "output_dir": OUTPUT_DIR,
+    "runtime_output_dir": RUN_DIR,
+    "quantized_model_dir": OUTPUT_DIR,
+    "status": status,
+    "duration_seconds": duration,
+    "original_size_mb": original_size_mb,
+    "quantized_size_mb": quantized_size_mb,
+    "compression_ratio": compression_ratio,
+    "errors": errors,
+    "solutions": solutions,
+    "output_files": output_files
+}
+
+# Write summary JSON
+summary_path = Path(RUN_DIR) / "quant_summary.json"
+with open(summary_path, "w") as f:
+    json.dump(summary, f, indent=2)
+
+print(f"\nQuantization {'FAILED' if errors else 'SUCCEEDED'}")
+print(f"Duration: {duration:.2f}s ({duration/60:.2f} min)")
+print(f"Summary: {summary_path}")
+
+if errors:
+    print("\nErrors:")
+    for e in errors:
+        print(f"  - {e}")
+    sys.exit(1)
+else:
+    sys.exit(0)

Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/session_quant_320.jsonl

@@ -0,0 +1,46 @@
+{"type":"session","version":3,"id":"autoeval_quant_320","timestamp":"2026-04-24T09:27:42.775Z","cwd":"/root/.openclaw/workspace"}
+{"type":"model_change","id":"6c59d347","parentId":null,"timestamp":"2026-04-24T09:27:42.782Z","provider":"minimax","modelId":"MiniMax-M2.7"}
+{"type":"thinking_level_change","id":"a6c38878","parentId":"6c59d347","timestamp":"2026-04-24T09:27:42.782Z","thinkingLevel":"low"}
+{"type":"custom","customType":"model-snapshot","data":{"timestamp":1777022862788,"provider":"minimax","modelApi":"anthropic-messages","modelId":"MiniMax-M2.7"},"id":"d2199f65","parentId":"a6c38878","timestamp":"2026-04-24T09:27:42.788Z"}
+{"type":"message","id":"03f0ddc0","parentId":"d2199f65","timestamp":"2026-04-24T09:27:42.794Z","message":{"role":"user","content":[{"type":"text","text":"You are an expert in LLM quantization using the Intel Auto-Round toolkit.\nYou MUST follow the skill instructions in: /root/.openclaw/workspace/skills/auto_quant/SKILL.md\n\nModel: Qwen/Qwen3-1.7B\nQuantization: W4A16 / RTN\nExport format: auto_round\nQuantized Model Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\nRuntime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\nRuntime device: cuda\nNum gpus: 1\n\nDirectory responsibilities:\n- Write exported model files to: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\n- Write runtime artifacts such as quant_summary.json, quantize.py, logs, prompts, copied request/session files, and the venv to: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\n\nCRITICAL SCRIPT REQUIREMENT:\n- Before starting quantization, you MUST first generate the quantization script file:\n    /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quantize.py\n- The file name must be exactly: quantize.py\n- Run quantization by executing that generated quantize.py script\n- Do not use quantize_script.py as the final artifact name\n\nCRITICAL ENVIRONMENT NOTE:\n- System Python has torch+cuda pre-installed. When creating venvs, ALWAYS use:\n    python3 -m venv --system-site-packages <path>\n  This ensures the venv inherits torch+cuda. Do NOT pip install torch inside the venv.\n- If /root/.venv exists, reuse /root/.venv before creating a new venv.\n- Use uv pip for dependency installation. Prefer:\n    uv pip install --python <venv>/bin/python <packages>\n- Do NOT reinstall torch or flash_attn if they already import successfully from the reused environment. Only install them when missing or incompatible.\n- This workflow is CUDA-focused. For AutoRound device selection:\n    - if Num gpus == 1, prefer device=\"cuda\"\n    - if Num gpus > 1, prefer device_map=\"auto\"\n  Do NOT default to device_map=\"0\" or device_map=\"0,1,2,3\" unless manual mapping is truly required after auto placement fails.\n\nIMPORTANT - After quantization completes (success or failure), you MUST produce:\n\n/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json - structured summary:\n{\n  \"model_id\": \"Qwen/Qwen3-1.7B\",\n  \"scheme\": \"W4A16\",\n  \"method\": \"RTN\",\n  \"export_format\": \"auto_round\",\n  \"device\": \"cuda\",\n  \"quant_num_gpus\": \"1\",\n  \"num_gpus\": \"1\",\n  \"output_dir\": \"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\",\n  \"runtime_output_dir\": \"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\",\n  \"quantized_model_dir\": \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\",\n  \"status\": \"success\" or \"failed\",\n  \"duration_seconds\": <float>,\n  \"original_size_mb\": <float or null>,\n  \"quantized_size_mb\": <float or null>,\n  \"compression_ratio\": <float or null>,\n  \"errors\": [<list of error strings>],\n  \"solutions\": [<list of solution strings>],\n  \"output_files\": [<list of file paths in runtime_output_dir>]\n}\n\nWrite as valid JSON. If quantization fails, still write quant_summary.json with status=failed."}],"timestamp":1777022862791}}
+{"type":"message","id":"cc208ead","parentId":"03f0ddc0","timestamp":"2026-04-24T09:27:51.427Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the skill instructions to understand how to properly perform the quantization task.","thinkingSignature":"fb0b20a1a114f9ad939954cdd00e0c548df64e1b41a34516775104f3c950b035"},{"type":"toolCall","id":"call_f526cf62fc734be0888b3324","name":"read","arguments":{"file":"/root/.openclaw/workspace/skills/auto_quant/SKILL.md"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":11833,"output":58,"cacheRead":256,"cacheWrite":0,"totalTokens":12147,"cost":{"input":0.0035499,"output":0.0000696,"cacheRead":0.00001536,"cacheWrite":0,"total":0.0036348599999999997}},"stopReason":"toolUse","timestamp":1777022862814,"responseId":"063a6a901333078295c46a757eea5941"}}
+{"type":"message","id":"334a6948","parentId":"cc208ead","timestamp":"2026-04-24T09:27:51.468Z","message":{"role":"toolResult","toolCallId":"call_f526cf62fc734be0888b3324","toolName":"read","content":[{"type":"text","text":"---\nname: auto_quant\ndescription: Quantize Large Language Models (LLMs) using Intel's Auto-Round library with sign-gradient descent for ultra-low bit widths (2-8 bits). Supports multiple formats (AutoRound, GGUF, GPTQ, AWQ, LLM-Compressor).\nmetadata:\n  openclaw:\n    emoji: \"⚡\"\n    homepage: https://github.com/intel/auto-round\n    skillKey: auto-quant\n    requires:\n      bins: []\n      env: []\n      config: []\n---\n\n# Auto-Round Model Quantization Skill\n\nUse this skill when the user wants to quantize Large Language Models (LLMs) using Intel's Auto-Round library. This skill provides comprehensive guidance including error handling, troubleshooting, and model-specific optimizations.\n\n## Overview\n\nAutoRound is an advanced quantization toolkit for LLMs that achieves high accuracy at ultra-low bit widths (2-4 bits) using **sign-gradient descent**. It supports multiple formats (AutoRound, GGUF, GPTQ, AWQ, LLM-Compressor) and inference backends.\n\n**Key capabilities:**\n- Quantization schemes: W4A16, W8A16, W2A16, W3A16, MXFP4, MXFP8, NVFP4, GGUF:Q4_K_M, etc.\n- Export formats: auto_round, auto_gptq, auto_awq, llm_compressor, gguf\n- Inference backends: Transformers, vLLM, SGLang, IPEX, Marlin, ExLLaMAV2\n\n---\n\n## Input Parameters\n\n| Parameter | Description | Required | Default |\n|-----------|-------------|----------|---------|\n| `model_path` | HuggingFace model ID or local path | Yes | - |\n| `output_dir` | Output directory for quantized model | Yes | - |\n| `quant_type` / `scheme` | Quantization scheme | No | `W4A16` |\n| `iters` | Training iterations (0=RTN) | No | `200` |\n| `nsamples` | Calibration samples | No | `128` |\n| `format` | Export format | No | `auto_round` |\n| `device` / `device_map` | CUDA device selection for quantization | No | Single GPU: `device=\"cuda\"`; Multi-GPU: `device_map=\"auto\"` |\n\n### CUDA Device Rules (CRITICAL)\n\nThis workflow is primarily for **CUDA / NVIDIA GPU** quantization.\n\nWhen generating a quantization script for this repo, follow these rules:\n\n1. **Single GPU CUDA**: use `device=\"cuda\"` in the AutoRound API\n2. **Multi-GPU CUDA**: use `device_map=\"auto\"` in the AutoRound API\n3. **Do not default to** `device_map=\"0\"` or `device_map=\"0,1,2,3\"` in generated scripts\n4. Only use a manual explicit map or comma-separated device list when:\n   - `device_map=\"auto\"` fails\n   - or you are intentionally debugging manual placement\n\nExamples:\n\n```python\n# Single GPU (recommended default)\nar = AutoRound(..., device=\"cuda\")\n\n# Multi-GPU (recommended default)\nar = AutoRound(..., device_map=\"auto\")\n```\n\nCLI equivalents:\n\n```bash\n# Single GPU\nCUDA_VISIBLE_DEVICES=0 auto-round --model Qwen/Qwen3-0.6B --scheme W4A16 --device cuda\n\n# Multi-GPU\nCUDA_VISIBLE_DEVICES=0,1,2,3 auto-round --model Qwen/Qwen3-0.6B --scheme W4A16 --device auto\n```\n\n### Quantization Schemes\n\n| Scheme | Description | Bits | Group Size | Notes |\n|--------|-------------|------|------------|-------|\n| `W4A16` / `int4` | INT4 weight, FP16 activation | 4 | 128 | **Recommended** for production |\n| `W8A16` | INT8 weight, FP16 activation | 8 | 128 | High accuracy |\n| `W3A16` | INT3 weight, FP16 activation | 3 | 128 | Experimental |\n| `W2A16` | INT2 weight, FP16 activation | 2 | 128 | Lowest bits, use `auto-round-best` |\n| `MXFP4` | MXFP4 format | 4 | 32 | **Research only, no kernel** |\n| `MXFP8` | MXFP8 format | 8 | 32 | **Research only, no kernel** |\n| `NVFP4` | NVIDIA FP4 | 4 | 16 | Use `llm_compressor` format |\n| `GGUF:Q4_K_M` | GGUF Q4 | 4 | - | For llama.cpp |\n\n### Export Formats\n\n| Format | Schemes Supported | Best For |\n|--------|-------------------|----------|\n| `auto_round` | W4A16, W2A16, W3A16, W8A16, MXFP4, MXFP8, NVFP4 | CPU, NVIDIA GPU, CUDA, HPU |\n| `auto_gptq` | W4A16, W2A16, W3A16, W8A16 | CUDA (symmetric) |\n| `auto_awq` | W4A16 | CUDA (asymmetric) |\n| `llm_compressor` | NVFP4, MXFP4, MXFP8 | vLLM, SGLang |\n| `gguf:q4_k_m` | GGUF:Q*_K, Q*_0, Q*_1 | llama.cpp, CPU |\n\n---\n\n## Step 1: Analyze Model from HuggingFace\n\n**CRITICAL: Always fetch model information before quantization.**\n\n### Fetch Model Card and Config\n\n```bash\n# README (model card) - contains usage instructions, quantization notes\ncurl -L https://huggingface.co/{model_id}/resolve/main/README.md -o /tmp/{model_id}_README.md\n\n# config.json - architecture details (model_type, num_layers, hidden_size)\ncurl -L https://huggingface.co/{model_id}/resolve/main/config.json -o /tmp/{model_id}_config.json\n\n# tokenizer_config.json - tokenizer type and special tokens\ncurl -L https://huggingface.co/{model_id}/resolve/main/tokenizer_config.json -o /tmp/{model_id}_tokenizer.json\n```\n\nReplace `{model_id}` with HuggingFace model ID (e.g., `meta-llama/Llama-3.1-8B-Instruct`).\n\n### What to Look For\n\n1. **Architecture**: Check `config.json` → `model_type`\n   - Common types: `llama`, `qwen`, `mistral`, `gemma`, `falcon`, `deepseek_v2`, `mixtral`\n   \n2. **Quantization notes**: Search README for:\n   - \"quantize\", \"quantization\", \"AWQ\", \"GPTQ\", \"GGUF\"\n   - Special requirements or limitations\n   \n3. **Model size**: Estimate VRAM needed (~1.2-1.5x model size in BF16)\n\n4. **Special requirements**:\n   - Token required for gated models (Llama, etc.)\n   - Trust remote code requirements\n   - Special dependencies\n\n---\n\n## Step 2: Set Up Environment\n\n### Step 2.0: Check for Shared Workspace (model_info.json)\n\n**IMPORTANT: Before creating any venv, check if `auto_run` has already set up the environment for this model.**\n\nThe `auto_run` skill writes a `model_info.json` file to the shared workspace directory after environment setup. If this file exists, reuse the venv from it instead of creating a new one.\n\n**Also check for a prebuilt system venv first:**\n\n- If `/root/.venv/bin/python` exists, reuse `/root/.venv`\n- Do **not** create a new venv if `/root/.venv` is already suitable\n- Install dependencies with `uv pip`, not plain `pip install`\n- If `torch` or `flash_attn` already import successfully from the reused venv, keep them; do not reinstall them unless they are missing or incompatible\n\nThe shared workspace directory is typically the `auto_run` output directory for this model:\n- e.g., `/storage/lkk/inference/Qwen_Qwen3-0.6B/model_info.json`\n- The task prompt may explicitly specify it as `workspace_dir`\n\n```python\nimport json\nfrom pathlib import Path\n\n# Check if model_info.json exists in workspace_dir (passed via task prompt)\nworkspace_dir = \"{workspace_dir}\"   # e.g. /storage/lkk/inference/Qwen_Qwen3-0.6B\ninfo_path = Path(workspace_dir) / \"model_info.json\"\n\nif info_path.exists():\n    model_info = json.loads(info_path.read_text())\n    venv_path = model_info[\"venv_path\"]          # e.g. /storage/.../venv\n    venv_py   = f\"{venv_path}/bin/python\"\n    venv_uv   = f\"uv pip --python {venv_py}\"\n    print(f\"✅ Reusing shared venv from auto_run: {venv_path}\")\n    # → Skip Steps 2.1-2.2, go directly to Step 3\nelif Path(\"/root/.venv/bin/python\").exists():\n    venv_path = \"/root/.venv\"\n    venv_py   = f\"{venv_path}/bin/python\"\n    venv_uv   = f\"uv pip --python {venv_py}\"\n    print(f\"✅ Reusing system venv: {venv_path}\")\n    # → Skip Steps 2.1-2.2, go directly to Step 3\nelse:\n    print(\"ℹ️  No model_info.json found, will create standalone venv in output_dir\")\n    venv_path = \"{output_dir}/venv\"\n    venv_py   = f\"{venv_path}/bin/python\"\n    venv_uv   = f\"uv pip --python {venv_py}\"\n    # → Continue with Steps 2.1-2.2 below\n```\n\n### Create Isolated Virtual Environment\n\n**Only run the steps below if model_info.json was NOT found above.**\n\n```bash\n# Create output directory\nmkdir -p {output_dir}\nmkdir -p {output_dir}/logs\n\n# Create virtual environment\npython3 -m venv --system-site-packages {output_dir}/venv\n\n# Bootstrap uv in the venv and use uv pip for package installation\n{output_dir}/venv/bin/python -m pip install -U uv\nuv pip install --python {output_dir}/venv/bin/python -U pip setuptools wheel\n```\n\n### Install Auto-Round\n\n**Option A: From local source (editable - allows source modifications)**\n```bash\n# Copy source if needed\ncp -r /storage/lkk/auto-round {output_dir}/auto-round-src\n\n# Install in editable mode\nuv pip install --python {output_dir}/venv/bin/python -e {output_dir}/auto-round-src\n```\n\n**Option B: From GitHub**\n```bash\nuv pip install --python {output_dir}/venv/bin/python git+https://github.com/intel/auto-round.git\n```\n\n**Option C: From PyPI**\n```bash\nuv pip install --python {output_dir}/venv/bin/python auto-round\n```\n\n### Install Additional Dependencies\n\n```bash\n# Verify inherited CUDA packages first; keep them if they already work\n{output_dir}/venv/bin/python -c \"import torch; print('torch ok:', torch.__version__)\"\n{output_dir}/venv/bin/python -c \"import flash_attn; print('flash_attn ok')\" || true\n\n# Install or update non-CUDA packages with uv pip\nuv pip install --python {output_dir}/venv/bin/python transformers accelerate datasets\n\n# For specific formats\nuv pip install --python {output_dir}/venv/bin/python compressed-tensors  # For better compression\nuv pip install --python {output_dir}/venv/bin/python llama-cpp-python   # For GGUF inference\nuv pip install --python {output_dir}/venv/bin/python gptqmodel          # For GPTQ inference\n\n# Only if torch is missing or incompatible, install a matching CUDA wheel\n# uv pip install --python {output_dir}/venv/bin/python --index-url https://download.pytorch.org/whl/cu124 torch\n\n# Only if flash_attn is required and missing, install it explicitly\n# uv pip install --python {output_dir}/venv/bin/python flash-attn --no-build-isolation\n```\n\n---\n\n## Step 3: Generate Quantization Script\n\n### Basic Script Template\n\n```python\n#!/usr/bin/env python3\n\"\"\"\nAuto-Round Quantization Script\nGenerated by auto_quant skill\n\nModel: {model_path}\nOutput: {output_dir}\nScheme: {scheme}\nIterations: {iters}\nSamples: {nsamples}\nFormat: {format}\n\"\"\"\n\nfrom auto_round import AutoRound\n\n# Configuration\nmodel_name_or_path = \"{model_path}\"\noutput_dir = \"{output_dir}\"\nscheme = \"{scheme}\"  # e.g., \"W4A16\", \"MXFP4\", \"GGUF:Q4_K_M\"\niters = {iters}      # 0 for RTN mode, 200 for default, 1000 for best\nnsamples = {nsamples}\nformat_str = \"{format}\"  # \"auto_round\", \"llm_compressor\", \"gguf:q4_k_m\"\nnum_gpus = 1  # replace with the actual GPU count for this run\n\n# CUDA device selection rule for this repo:\n# - single GPU: device=\"cuda\"\n# - multi-GPU: device_map=\"auto\"\nautoround_device_kwargs = {\"device\": \"cuda\"} if num_gpus <= 1 else {\"device_map\": \"auto\"}\n\nprint(f\"Loading model: {{model_name_or_path}}\")\nprint(f\"Scheme: {{scheme}}\")\nprint(f\"Iters: {{iters}}\")\nprint(f\"nsamples: {{nsamples}}\")\nprint(f\"Format: {{format_str}}\")\nprint(f\"Device args: {{autoround_device_kwargs}}\")\n\n# Create AutoRound instance\nar = AutoRound(\n    model_name_or_path,\n    scheme=scheme,\n    iters=iters,\n    nsamples=nsamples,\n    # Optional optimizations\n    # enable_torch_compile=True,  # Faster quantization (PyTorch 2.6+)\n    # low_gpu_mem_usage=True,    # Lower VRAM, ~30% slower\n    # disable_opt_rtn=True,      # For GGUF: use pure RTN\n    **autoround_device_kwargs,\n)\n\n# Quantize and save\nprint(\"Starting quantization...\")\nar.quantize_and_save(output_dir=output_dir, format=format_str)\n\nprint(f\"Quantization complete! Output: {{output_dir}}\")\n```\n\n### Recipe Recommendations\n\n| Recipe | iters | nsamples | seqlen | Accuracy | Speed |\n|--------|-------|----------|--------|----------|-------|\n| `default` | 200 | 128 | 2048 | Good | Baseline |\n| `best` | 1000 | 512 | 2048 | **Best** | 4-5x slower |\n| `light` | 50 | 128 | 2048 | Slight drop | 2-3x faster |\n\n**Recommendation:**\n- **W4A16**: Use default recipe (`iters=200`)\n- **W2A16**: Use best recipe (`iters=1000`, `enable_alg_ext=True`)\n- **GGUF**: Use RTN (`iters=0`)\n\n---\n\n## Step 4: Execute and Handle Errors (CRITICAL!)\n\nWhen quantization fails, you MUST diagnose and fix. **Do NOT simply report errors without attempting solutions.**\n\n### Error Handling Workflow\n\n```\nERROR → Analyze → Search → Try Solutions → Verify → Document\n```\n\n### Common Errors and Solutions\n\n#### 1. ImportError / ModuleNotFoundError\n\n**Symptoms:**\n```\nModuleNotFoundError: No module named 'auto_round'\nImportError: cannot import name 'AutoRound' from 'auto_round'\n```\n\n**Solutions:**\n```bash\n# Reinstall auto-round\nuv pip install --python {venv}/bin/python --upgrade auto-round\n\n# Or from source\nuv pip install --python {venv}/bin/python -e /path/to/auto-round --force-reinstall\n\n# Check installation\n{venv}/bin/pip show auto-round\n```\n\n#### 2. CUDA Out of Memory (OOM)\n\n**Symptoms:**\n```\nRuntimeError: CUDA out of memory\ntorch.OutOfMemoryError: CUDA out of memory: tried to allocate X GiB\n```\n\n**Solutions (try in order):**\n```python\n# Solution A: Reduce memory usage - add to AutoRound initialization\nar = AutoRound(\n    model_name_or_path,\n    scheme=scheme,\n    iters=iters,\n    nsamples=nsamples,\n    enable_torch_compile=True,    # PyTorch 2.6+ recommended\n    low_gpu_mem_usage=True,       # Offload to CPU, ~20% more time\n    device=\"cuda\",                # Keep single-GPU CUDA explicit\n)\n\n# Solution B: Reduce batch size\n    batch_size=1,\n    gradient_accumulate_steps=8,\n\n# Solution C: Reduce seqlen (may affect accuracy)\n    seqlen=512,\n\n# Solution D: Use RTN mode (fastest, no calibration)\n    iters=0,\n    disable_opt_rtn=True,  # For GGUF format\n\n# Solution E: Use multiple GPUs\n    device_map=\"auto\",           # Recommended multi-GPU default\n```\n\n**CLI alternatives:**\n```bash\n# Use light recipe\nauto-round-light --model ... --scheme W4A16\n\n# Low memory mode\nauto-round --model ... --scheme W4A16 --low_gpu_mem_usage\n\n# Multi-GPU CUDA\nCUDA_VISIBLE_DEVICES=0,1,2,3 auto-round --model ... --scheme W4A16 --device auto\n```\n\n#### 3. Version Conflicts\n\n**Symptoms:**\n```\nImportError: cannot import name 'xxx' from 'transformers'\nAttributeError: module 'torch' has no attribute 'xxx'\nVersionConflict: transformers x.x.x is incompatible with...\n```\n\n**Solutions:**\n```bash\n# Check current versions\n{venv}/bin/pip show torch transformers accelerate\n\n# Upgrade/downgrade transformers\nuv pip install --python {venv}/bin/python \"transformers>=4.35.0\"\nuv pip install --python {venv}/bin/python \"transformers==4.40.0\"\n\n# Upgrade torch only when it is actually missing or incompatible\nuv pip install --python {venv}/bin/python \"torch>=2.5.0\"\nuv pip install --python {venv}/bin/python --index-url https://download.pytorch.org/whl/cu124 torch\n\n# Install flash-attn only if required by the model/runtime and currently missing\nuv pip install --python {venv}/bin/python flash-attn --no-build-isolation\n\n# Reinstall auto-round dependencies\nuv pip install --python {venv}/bin/python -r /path/to/auto-round/requirements.txt\n```\n\n#### 4. Model Loading Errors\n\n**Symptoms:**\n```\nOSError: Can't load tokenizer for ...\nFileNotFoundError: tokenizer_config.json not found\nValueError: xxx requires a HuggingFace token\n```\n\n**Solutions:**\n```bash\n# For gated models (Llama, etc.), set token\nimport os\nos.environ[\"HF_TOKEN\"] = \"your_token_here\"\n\n# Or use CLI\nhuggingface-cli download meta-llama/Llama-3.1-8B-Instruct --token $HF_TOKEN\n\n# Download model first\ngit lfs clone https://huggingface.co/{model_id} /local/path\n\n# Use trust_remote_code for custom models\nar = AutoRound(\n    model_name_or_path,\n    trust_remote_code=True,\n)\n```\n\n#### 5. Quantization Scheme Errors\n\n**Symptoms:**\n```\nValueError: Unsupported quantization scheme 'xxx'\nKeyError: scheme 'xxx' not found\n```\n\n**Solutions:**\n```bash\n# Check supported schemes\nauto-round list scheme\n\n# Use correct scheme name (case-sensitive)\nscheme = \"W4A16\"   # Correct\nscheme = \"w4a16\"   # May not work\n\n# For GGUF format\nscheme = \"GGUF:Q4_K_M\"  # Correct format\n```\n\n#### 6. Export Format Errors\n\n**Symptoms:**\n```\nValueError: Export format 'xxx' not supported\nRuntimeError: Failed to export to gguf format\n```\n\n**Solutions:**\n```python\n# Try different format combinations\nformat = \"auto_round\"                    # Most compatible\nformat = \"llm_compressor\"                # For NVFP4/MXFP4\nformat = \"gguf:q4_k_m\"                   # For GGUF\nformat = \"auto_gptq,auto_awq,auto_round\" # Multiple formats\n\n# For GGUF, use iters=0 (RTN)\nar = AutoRound(\n    model_name_or_path,\n    scheme=\"W4A16\",\n    iters=0,  # RTN mode\n)\n```\n\n#### 7. GPU Not Found / CUDA Errors\n\n**Symptoms:**\n```\nRuntimeError: CUDA not available\nAssertionError: CUDA device not found\n```\n\n**Solutions:**\n```bash\n# Check CUDA availability\nnvidia-smi\npython -c \"import torch; print(torch.cuda.is_available())\"\n\n# Check GPU visibility\necho $CUDA_VISIBLE_DEVICES\nCUDA_VISIBLE_DEVICES=0 python script.py\nCUDA_VISIBLE_DEVICES=0,1 python script.py\n\n# Use CPU instead\ndevice_map = \"cpu\"\n```\n\n#### 8. Calibration Dataset Errors\n\n**Symptoms:**\n```\nRuntimeError: Error loading dataset 'xxx'\nDatasetNotFoundError: Couldn't find dataset 'xxx'\n```\n\n**Solutions:**\n```python\n# Use default dataset\ndataset = \"NeelNanda/pile-10k\"\n\n# Use alternative dataset\ndataset = \"swift/pile-val-backup\"  # For China region\ndataset = \"BAAI/CCI3-HQ\"           # Chinese\ndataset = \"mbpp\"                   # Code\n\n# Use local dataset\ndataset = \"/path/to/local_dataset.json\"\n\n# Specify dataset split\ndataset = \"NeelNanda/pile-10k:train\"\ndataset = \"NeelNanda/pile-10k:train+validation\"\n```\n\n---\n\n## Step 5: Advanced Troubleshooting\n\n### When Standard Solutions Don't Work\n\n#### A. Web Search Strategy\n\nSearch for the exact error message:\n```\n# Search patterns\n\"auto-round\" \"CUDA out of memory\"\n\"auto-round\" \"ImportError\" transformers\n\"intel auto-round\" github issues\n\"auto-round\" \"ValueError\" scheme\n```\n\n#### B. Check GitHub Issues\n\n```bash\n# Search auto-round issues\ncurl -s \"https://api.github.com/search/issues?q=repo:intel/auto-round+out+of+memory\" | jq '.items[:5] | .[] | {title, url}'\n\n# Check recent issues\ncurl -s \"https://api.github.com/repos/intel/auto-round/issues?state=open\" | jq '.[:10] | .[] | {title, number}'\n```\n\n#### C. Source Code Investigation\n\nIf error is in auto-round itself:\n```bash\n# Look at auto-round source\nls /path/to/auto-round/auto_round/\n\n# Check specific module\ncat /path/to/auto-round/auto_round/autoround.py | head -100\n\n# Search for error source\ngrep -r \"error_message\" /path/to/auto-round/auto_round/\n```\n\n#### D. Try Different Approaches\n\n```python\n# Approach 1: Different scheme\nscheme = \"W4A16\"  # Instead of MXFP4\n\n# Approach 2: Different format\nformat = \"auto_round\"  # Instead of gguf\n\n# Approach 3: Different recipe\n# default → light → best\n\n# Approach 4: Use CLI instead of API\nimport subprocess\nsubprocess.run([\n    \"auto-round\",\n    \"--model\", model_path,\n    \"--scheme\", \"W4A16\",\n    \"--format\", \"auto_round\",\n    \"--output_dir\", output_dir,\n])\n```\n\n---\n\n## Step 6: Verify and Save\n\nAfter successful quantization:\n\n1. **Verify output files:**\n```bash\nls -la {output_dir}/\nls -la {output_dir}/quantized_model/  # or output_dir/\n```\n\n2. **Save script:**\n```python\n# Save the quantization script to output directory\nwith open(f\"{output_dir}/quantize_script.py\", \"w\") as f:\n    f.write(script_content)\n```\n\n3. **Document solutions (if errors occurred):**\n```markdown\n# {output_dir}/solutions.md\n\n## Error 1: [Error Description]\n- **Cause**: [Root cause]\n- **Solution**: [What worked]\n- **Command**: [Command used]\n\n## Error 2: ...\n```\n\n---\n\n## Step 6.5: Generate Summary (RECOMMENDED)\n\nAfter quantization completes (success or failure), generate a `summary.md` to document the entire process. This helps with debugging, reproducibility, and tracking issues.\n\n### Summary Template\n\n```python\n#!/usr/bin/env python3\n\"\"\"\nGenerate quantization summary\nRun this after quantization completes (success or failure)\n\"\"\"\n\nimport json\nimport os\nfrom datetime import datetime\nfrom pathlib import Path\n\ndef generate_summary(\n    output_dir: str,\n    model_path: str,\n    scheme: str,\n    iters: int,\n    nsamples: int,\n    format_str: str,\n    start_time: float,\n    errors: list = None,\n    solutions: list = None,\n    notes: str = None\n):\n    \"\"\"Generate a comprehensive summary markdown file.\"\"\"\n    \n    import time\n    end_time = time.time()\n    duration = end_time - start_time\n    \n    # Collect output files\n    output_path = Path(output_dir)\n    files_info = []\n    if output_path.exists():\n        for f in sorted(output_path.rglob(\"*\")):\n            if f.is_file() and not f.name.endswith(('.pyc', '.pyo', '__pycache__')):\n                size = f.stat().st_size\n                size_str = f\"{size/1024/1024:.2f} MB\" if size > 1024*1024 else f\"{size/1024:.2f} KB\"\n                files_info.append(f\"  - {f.relative_to(output_path)} ({size_str})\")\n    \n    # Build summary markdown\n    summary = f\"\"\"# Quantization Summary\n\nGenerated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S UTC')}\n\n## Model Information\n\n| Field | Value |\n|-------|-------|\n| Model Path | `{model_path}` |\n| Scheme | `{scheme}` |\n| Iterations | `{iters}` |\n| Calibration Samples | `{nsamples}` |\n| Export Format | `{format_str}` |\n\n## Timing\n\n| Phase | Duration |\n|-------|----------|\n| Total | {duration:.2f} seconds ({duration/60:.2f} minutes) |\n\n## Output Files\n\n```\n{chr(10).join(files_info) if files_info else \"  (no files found)\"}\n```\n\n## Errors Encountered\n\n{chr(10).join(f\"- {err}\" for err in (errors or [\"(none)\"]))}\n\n## Solutions Applied\n\n{chr(10).join(f\"- {sol}\" for sol in (solutions or [\"(none)\"]))}\n\n## Additional Notes\n\n{notes or \"(none)\"}\n\n## Environment\n\n```bash\n# Python version\npython3 --version\n\n# Key packages\npython -m pip show torch transformers auto-round\n```\n\n## Reproduce Command\n\n```bash\n# Recreate this quantization\nauto-round --model {model_path} --scheme \"{scheme}\" --format {format_str} --output_dir {output_dir} --iters {iters} --nsamples {nsamples}\n```\n\"\"\"\n    \n    # Write summary\n    summary_path = Path(output_dir) / \"summary.md\"\n    with open(summary_path, \"w\") as f:\n        f.write(summary)\n    \n    print(f\"Summary written to: {summary_path}\")\n    return summary_path\n\n# Usage example:\nif __name__ == \"__main__\":\n    import time\n    start_time = time.time()  # Set this at the beginning of quantization\n    \n    # Your quantization code here...\n    \n    # Generate summary at the end\n    generate_summary(\n        output_dir=\"/storage/quantized/llama-8b-w4a16\",\n        model_path=\"meta-llama/Llama-3.1-8B-Instruct\",\n        scheme=\"W4A16\",\n        iters=200,\n        nsamples=128,\n        format_str=\"auto_round\",\n        start_time=start_time,\n        errors=[\"CUDA OOM - tried to allocate 12GB\", \"Fixed by enable_torch_compile=True\"],\n        solutions=[\"Added low_gpu_mem_usage=True\", \"Reduced batch_size to 1\"],\n        notes=\"Model quantized successfully with minor memory optimizations\"\n    )\n```\n\n### Integration with Quantization Script\n\nAdd summary generation to your quantization script:\n\n```python\n#!/usr/bin/env python3\nimport time\nimport json\nfrom pathlib import Path\n\n# Track start time\nstart_time = time.time()\n\n# Track errors and solutions\nerrors = []\nsolutions = []\n\ntry:\n    # Your quantization code here\n    ar = AutoRound(...)\n    ar.quantize_and_save(...)\n    \nexcept Exception as e:\n    errors.append(str(e))\n    \n    # Try to recover\n    try:\n        # Attempted solution 1\n        solutions.append(\"Attempted solution description\")\n    except:\n        pass\n    \n    # Try more solutions...\n    finally:\n        # Always generate summary even if quantization failed\n        generate_summary(\n            output_dir=output_dir,\n            model_path=model_path,\n            scheme=scheme,\n            iters=iters,\n            nsamples=nsamples,\n            format_str=format_str,\n            start_time=start_time,\n            errors=errors,\n            solutions=solutions,\n            notes=\"Quantization failed, see errors above\"\n        )\n        raise\n\n# Success path - generate summary\ngenerate_summary(\n    output_dir=output_dir,\n    model_path=model_path,\n    scheme=scheme,\n    iters=iters,\n    nsamples=nsamples,\n    format_str=format_str,\n    start_time=start_time,\n    notes=\"Quantization completed successfully\"\n)\n```\n\n### Summary Output Example\n\nThe generated `summary.md` will look like:\n\n```markdown\n# Quantization Summary\n\nGenerated: 2026-03-20 00:51 UTC\n\n## Model Information\n\n| Field | Value |\n|-------|-------|\n| Model Path | `meta-llama/Llama-3.1-8B-Instruct` |\n| Scheme | `W4A16` |\n| Iterations | `200` |\n| Calibration Samples | `128` |\n| Export Format | `auto_round` |\n\n## Timing\n\n| Phase | Duration |\n|-------|----------|\n| Total | 845.32 seconds (14.09 minutes) |\n\n## Output Files\n\n```\n- quantized_model/adapter_config.json (1.23 KB)\n- quantized_model/adapter_model.safetensors (3.87 GB)\n- quantize_script.py (2.45 KB)\n- summary.md (1.89 KB)\n```\n\n## Errors Encountered\n\n- (none)\n\n## Solutions Applied\n\n- (none)\n\n## Additional Notes\n\n- Model quantized successfully with default settings\n\n## Environment\n\n```bash\n# Python version\nPython 3.10.12\n\n# Key packages\ntorch: 2.5.0\ntransformers: 4.40.0\nauto-round: 0.2.1\n```\n\n## Reproduce Command\n\n```bash\nauto-round --model meta-llama/Llama-3.1-8B-Instruct --scheme \"W4A16\" --format auto_round --output_dir /storage/quantized/llama-8b-w4a16 --iters 200 --nsamples 128\n```\n```\n\n---\n\n## Complete Example Workflow\n\n### User Request\n> Quantize meta-llama/Llama-3.1-8B-Instruct to W4A16 format, output to /storage/quantized/llama-8b-w4a16\n\n### Agent Actions\n\n**1. Query HuggingFace:**\n```bash\ncurl -L https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/resolve/main/config.json\ncurl -L https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/resolve/main/README.md\n```\n\n**2. Set up environment:**\n```bash\nmkdir -p /storage/quantized/llama-8b-w4a16/logs\nif [ -x /root/.venv/bin/python ]; then\n  VENV_PY=/root/.venv/bin/python\nelse\n  python3 -m venv --system-site-packages /storage/quantized/llama-8b-w4a16/venv\n  VENV_PY=/storage/quantized/llama-8b-w4a16/venv/bin/python\nfi\n$VENV_PY -m pip install -U uv\nuv pip install --python \"$VENV_PY\" -e /storage/lkk/auto-round\n```\n\n**3. Create quantization script:**\n```python\n# /storage/quantized/llama-8b-w4a16/quantize_script.py\nfrom auto_round import AutoRound\n\nar = AutoRound(\n    \"meta-llama/Llama-3.1-8B-Instruct\",\n    scheme=\"W4A16\",\n    iters=200,\n    nsamples=128,\n    device=\"cuda\",\n    enable_torch_compile=True,\n)\nar.quantize_and_save(output_dir=\"/storage/quantized/llama-8b-w4a16\", format=\"auto_round\")\n```\n\n**Multi-GPU CUDA example:**\n```python\nfrom auto_round import AutoRound\n\nar = AutoRound(\n    \"meta-llama/Llama-3.1-8B-Instruct\",\n    scheme=\"W4A16\",\n    iters=200,\n    nsamples=128,\n    device_map=\"auto\",\n    low_gpu_mem_usage=True,\n    enable_torch_compile=True,\n)\nar.quantize_and_save(output_dir=\"/storage/quantized/llama-8b-w4a16\", format=\"auto_round\")\n```\n\n**4. Execute:**\n```bash\ncd /storage/quantified/llama-8b-w4a16\n./venv/bin/python quantize_script.py >> logs/quantize.log 2>&1\n```\n\n**5. Handle any errors** using the error handling workflow above\n\n**6. Verify:**\n```bash\nls /storage/quantized/llama-8b-w4a16/\n```\n\n---\n\n## Reference: Auto-Round CLI\n\n```bash\n# Basic usage\nauto-round --model Qwen/Qwen3-0.6B --scheme \"W4A16\" --format auto_round\n\n# With options\nauto-round \\\n    --model Qwen/Qwen3-0.6B \\\n    --scheme \"W4A16\" \\\n    --format \"auto_gptq,auto_awq,auto_round\" \\\n    --output_dir ./output \\\n    --iters 200 \\\n    --nsamples 128\n\n# List available options\nauto-round --help\nauto-round list scheme\nauto-round list format\n```\n\n---\n\n## Quick Reference Card\n\n| Need | Solution |\n|------|----------|\n| Fast quantization | `iters=0` (RTN mode) |\n| Best accuracy | `iters=1000`, `enable_alg_ext=True` |\n| Low VRAM | `low_gpu_mem_usage=True`, `batch_size=1` |\n| GGUF format | `format=\"gguf:q4_k_m\"`, `iters=0` |\n| Single GPU CUDA | `device=\"cuda\"` |\n| Multiple GPUs | `CUDA_VISIBLE_DEVICES=0,1,2,3` + `device_map=\"auto\"` |\n| 2-bit quantization | Use `W2A16` + best recipe |\n| MXFP4/MXFP8 | Research only, no kernel |\n\n---\n\n## Notes\n\n- **VRAM Requirements**: ~10-20GB for 8B models, ~20-40GB for 70B models\n- **Time**: ~10-20 min for 8B model with default settings\n- **MXFP4/MXFP8**: Research features, no real inference kernels\n- **Gated models**: May require HuggingFace token\n- **Editable install**: Install auto-round in editable mode to modify source if needed\n"}],"isError":false,"timestamp":1777022871465}}
+{"type":"message","id":"cef7df05","parentId":"334a6948","timestamp":"2026-04-24T09:28:09.621Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me analyze the task:\n\n1. Model: Qwen/Qwen3-1.7B\n2. Quantization: W4A16 / RTN (so iters=0)\n3. Export format: auto_round\n4. Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\n5. Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\n6. Runtime device: cuda\n7. Num gpus: 1\n\nKey things to remember:\n- RTN method means iters=0\n- device=\"cuda\" for single GPU\n- Need to create quantize.py (not quantize_script.py)\n- Need to write quant_summary.json at the end\n\nLet me start by:\n1. Checking if /root/.venv exists and is usable\n2. Creating the output directories\n3. Creating the quantization script\n4. Running the quantization\n5. Generating the summary\n\nFirst, let me check the environment.","thinkingSignature":"ed64e965c5ccc1c62ab48c960425ca64cbfb4d4e9ed4247e1b4afc8d82570ba0"},{"type":"toolCall","id":"call_adc1c11596644ff2b00969a5","name":"exec","arguments":{"command":"ls -la /root/.openclaw/workspace/quantized/ 2>/dev/null; echo \"---\"; ls -la /root/.venv/bin/python 2>/dev/null || echo \"No /root/.venv\"; echo \"---\"; nvidia-smi --query-gpu=name,memory.free,memory.total --format=csv,noheader 2>/dev/null || echo \"No nvidia-smi\""}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":7877,"output":338,"cacheRead":12032,"cacheWrite":0,"totalTokens":20247,"cost":{"input":0.0023631,"output":0.0004056,"cacheRead":0.0007219199999999999,"cacheWrite":0,"total":0.00349062}},"stopReason":"toolUse","timestamp":1777022871469,"responseId":"063a6a98903db6811b4b6d0b7910883b"}}
+{"type":"message","id":"e7180b06","parentId":"cef7df05","timestamp":"2026-04-24T09:28:09.698Z","message":{"role":"toolResult","toolCallId":"call_adc1c11596644ff2b00969a5","toolName":"exec","content":[{"type":"text","text":"total 4\ndrwxr-xr-x 3 root root   26 Apr 24 09:27 .\ndrwxr-xr-x 5 root root 4096 Apr 24 09:27 ..\ndrwxr-xr-x 3 root root   43 Apr 24 09:27 runs\n---\nlrwxrwxrwx 1 root root 73 Apr 24 05:53 /root/.venv/bin/python -> /root/.local/share/uv/python/cpython-3.12-linux-x86_64-gnu/bin/python3.12\n---\nNVIDIA GeForce RTX 4090, 22683 MiB, 23028 MiB"}],"details":{"status":"completed","exitCode":0,"durationMs":38,"aggregated":"total 4\ndrwxr-xr-x 3 root root   26 Apr 24 09:27 .\ndrwxr-xr-x 5 root root 4096 Apr 24 09:27 ..\ndrwxr-xr-x 3 root root   43 Apr 24 09:27 runs\n---\nlrwxrwxrwx 1 root root 73 Apr 24 05:53 /root/.venv/bin/python -> /root/.local/share/uv/python/cpython-3.12-linux-x86_64-gnu/bin/python3.12\n---\nNVIDIA GeForce RTX 4090, 22683 MiB, 23028 MiB","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022889698}}
+{"type":"message","id":"7bd9413d","parentId":"e7180b06","timestamp":"2026-04-24T09:28:13.815Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Good, so:\n- `/root/.venv` exists and has Python\n- GPU is RTX 4090 with ~22GB free\n- I need to create the output directories and quantization script\n\nLet me create the directories and the quantization script.","thinkingSignature":"b87c4dcfab008747aa5bcd7f07ca1f3fec48c739e181c7581112b5840acaa8b3"},{"type":"toolCall","id":"call_3045f5fe88084d51b16da1dd","name":"exec","arguments":{"command":"mkdir -p /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16 /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/logs"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":265,"output":129,"cacheRead":19904,"cacheWrite":0,"totalTokens":20298,"cost":{"input":0.0000795,"output":0.0001548,"cacheRead":0.0011942399999999998,"cacheWrite":0,"total":0.0014285399999999998}},"stopReason":"toolUse","timestamp":1777022889699,"responseId":"063a6aaa14da4c7a7b050b3b937e0500"}}
+{"type":"message","id":"0f6919af","parentId":"7bd9413d","timestamp":"2026-04-24T09:28:13.827Z","message":{"role":"toolResult","toolCallId":"call_3045f5fe88084d51b16da1dd","toolName":"exec","content":[{"type":"text","text":"(no output)"}],"details":{"status":"completed","exitCode":0,"durationMs":9,"aggregated":"","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022893827}}
+{"type":"message","id":"50e7e864","parentId":"0f6919af","timestamp":"2026-04-24T09:28:18.531Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Good, directories created. Now let me check what's available in the venv and whether auto-round is installed.","thinkingSignature":"56fbf00b5e15c55807ab2533d484d387277d7813ac826eaa403ff0c2e87ab0f8"},{"type":"toolCall","id":"call_1e8e1feb47b3403ebadb0945","name":"exec","arguments":{"command":"/root/.venv/bin/python -c \"import torch; print('torch:', torch.__version__, 'cuda:', torch.cuda.is_available())\" 2>&1; /root/.venv/bin/pip show auto-round 2>&1 | head -5"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":103,"output":98,"cacheRead":20160,"cacheWrite":0,"totalTokens":20361,"cost":{"input":0.0000309,"output":0.0001176,"cacheRead":0.0012096,"cacheWrite":0,"total":0.0013580999999999999}},"stopReason":"toolUse","timestamp":1777022893828,"responseId":"063a6aae9df8fc5593e17f9eded08355"}}
+{"type":"message","id":"67c8184e","parentId":"50e7e864","timestamp":"2026-04-24T09:28:18.558Z","message":{"role":"toolResult","toolCallId":"call_1e8e1feb47b3403ebadb0945","toolName":"exec","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'torch'\nsh: 1: /root/.venv/bin/pip: not found"}],"details":{"status":"completed","exitCode":0,"durationMs":24,"aggregated":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'torch'\nsh: 1: /root/.venv/bin/pip: not found","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022898557}}
+{"type":"message","id":"ca6be7aa","parentId":"67c8184e","timestamp":"2026-04-24T09:28:22.312Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Torch is not installed in /root/.venv. The instruction says to use `--system-site-packages` when creating venv, but torch isn't installed system-wide either. Let me check what's available.","thinkingSignature":"213c0d4ff4c13e1b9c693244b8bc3a1ecc4fda889d87b7df63e7711db8c3ec1c"},{"type":"toolCall","id":"call_4aafc1c4f58c41aaa97dc6a7","name":"exec","arguments":{"command":"python3 -c \"import torch; print('torch:', torch.__version__, 'cuda:', torch.cuda.is_available())\" 2>&1; python3 -c \"import auto_round; print('auto_round ok')\" 2>&1"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":175,"output":113,"cacheRead":20224,"cacheWrite":0,"totalTokens":20512,"cost":{"input":0.000052499999999999995,"output":0.0001356,"cacheRead":0.0012134399999999999,"cacheWrite":0,"total":0.00140154}},"stopReason":"toolUse","timestamp":1777022898558,"responseId":"063a6ab29b833356569e77960f82ab94"}}
+{"type":"message","id":"87cc8c9a","parentId":"ca6be7aa","timestamp":"2026-04-24T09:28:22.344Z","message":{"role":"toolResult","toolCallId":"call_4aafc1c4f58c41aaa97dc6a7","toolName":"exec","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'torch'\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'auto_round'\n\n(Command exited with code 1)"}],"details":{"status":"completed","exitCode":1,"durationMs":29,"aggregated":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'torch'\nTraceback (most recent call last):\n  File \"<string>\", line 1, in <module>\nModuleNotFoundError: No module named 'auto_round'\n\n(Command exited with code 1)","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022902343}}
+{"type":"message","id":"7b706995","parentId":"87cc8c9a","timestamp":"2026-04-24T09:28:29.746Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"torch is not installed in system python either. I need to create a venv with --system-site-packages to inherit whatever pre-installed packages exist, but torch isn't there. Let me check what's available in the system site packages and what's pre-installed.","thinkingSignature":"9fc4e939e6287a5c84b84bb7c47eeb8a806c463d0413b3931c4874e58a1c74a3"},{"type":"toolCall","id":"call_8215f3214e8349948458d87f","name":"exec","arguments":{"command":"python3 --version; which python3; ls /usr/lib/python3* -la 2>/dev/null | head; find /usr/local/lib -name \"site-packages\" -type d 2>/dev/null | head"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":205,"output":120,"cacheRead":20352,"cacheWrite":0,"totalTokens":20677,"cost":{"input":0.00006149999999999999,"output":0.000144,"cacheRead":0.0012211199999999998,"cacheWrite":0,"total":0.0014266199999999998}},"stopReason":"toolUse","timestamp":1777022902344,"responseId":"063a6ab6aec3f7c3469ad7d39ae817c8"}}
+{"type":"message","id":"87ea720e","parentId":"7b706995","timestamp":"2026-04-24T09:28:29.918Z","message":{"role":"toolResult","toolCallId":"call_8215f3214e8349948458d87f","toolName":"exec","content":[{"type":"text","text":"Python 3.12.13\n/root/.venv/bin/python3"}],"details":{"status":"completed","exitCode":0,"durationMs":169,"aggregated":"Python 3.12.13\n/root/.venv/bin/python3","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022909917}}
+{"type":"message","id":"eee0fbdc","parentId":"87ea720e","timestamp":"2026-04-24T09:28:33.791Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The default python3 is actually from /root/.venv. Let me check the system python.","thinkingSignature":"46affa8c88c194af2d62b9986f2ebfc75c0cc666d800de02a9672b6b7d127f5c"},{"type":"toolCall","id":"call_eaf7229365ec4bf98033ea4b","name":"exec","arguments":{"command":"ls /usr/bin/python* 2>/dev/null; ls /usr/local/bin/python* 2>/dev/null; /usr/bin/python3 --version 2>/dev/null || echo \"no /usr/bin/python3\"; ls /root/.venv/lib/ 2>/dev/null"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":112,"output":102,"cacheRead":20544,"cacheWrite":0,"totalTokens":20758,"cost":{"input":0.0000336,"output":0.0001224,"cacheRead":0.00123264,"cacheWrite":0,"total":0.00138864}},"stopReason":"toolUse","timestamp":1777022909918,"responseId":"063a6abe3525a12d8c1ce7777fc1ab00"}}
+{"type":"message","id":"793bbc18","parentId":"eee0fbdc","timestamp":"2026-04-24T09:28:33.805Z","message":{"role":"toolResult","toolCallId":"call_eaf7229365ec4bf98033ea4b","toolName":"exec","content":[{"type":"text","text":"no /usr/bin/python3\npython3.12"}],"details":{"status":"completed","exitCode":0,"durationMs":11,"aggregated":"no /usr/bin/python3\npython3.12","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022913805}}
+{"type":"message","id":"5e3ed15e","parentId":"793bbc18","timestamp":"2026-04-24T09:28:39.217Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The system Python is /root/.venv/bin/python3. The user's note says torch+cuda is pre-installed in system Python, but it doesn't seem to be there. Let me create a venv with `--system-site-packages` and install torch there.\n\nLet me create the venv and install the necessary packages.","thinkingSignature":"02e4cb30cfc23abb3f8f76d82d42023299cc33d6ac305228f418f1183b643529"},{"type":"toolCall","id":"call_b86f73dbb8f64a8dbf8cc1ec","name":"exec","arguments":{"command":"ls /root/.venv/lib/python3.12/site-packages/ | head -30"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":155,"output":104,"cacheRead":20608,"cacheWrite":0,"totalTokens":20867,"cost":{"input":0.0000465,"output":0.0001248,"cacheRead":0.0012364799999999999,"cacheWrite":0,"total":0.00140778}},"stopReason":"toolUse","timestamp":1777022913806,"responseId":"063a6ac26381fbf1096cd7da57694245"}}
+{"type":"message","id":"706832d4","parentId":"5e3ed15e","timestamp":"2026-04-24T09:28:39.238Z","message":{"role":"toolResult","toolCallId":"call_b86f73dbb8f64a8dbf8cc1ec","toolName":"exec","content":[{"type":"text","text":"__pycache__\n_virtualenv.pth\n_virtualenv.py\n_yaml\nannotated_doc\nannotated_doc-0.0.4.dist-info\nannotated_types\nannotated_types-0.7.0.dist-info\nanyio\nanyio-4.13.0.dist-info\nbeautifulsoup4-4.14.3.dist-info\nbs4\ncertifi\ncertifi-2026.4.22.dist-info\nclick\nclick-8.3.3.dist-info\ncmdop\ncmdop-2026.4.7.2.dist-info\ndotenv\nfilelock\nfilelock-3.29.0.dist-info\nfsspec\nfsspec-2026.3.0.dist-info\ngoogle\ngrpc\ngrpcio-1.80.0.dist-info\nh11\nh11-0.16.0.dist-info\nhf_xet\nhf_xet-1.4.3.dist-info"}],"details":{"status":"completed","exitCode":0,"durationMs":14,"aggregated":"__pycache__\n_virtualenv.pth\n_virtualenv.py\n_yaml\nannotated_doc\nannotated_doc-0.0.4.dist-info\nannotated_types\nannotated_types-0.7.0.dist-info\nanyio\nanyio-4.13.0.dist-info\nbeautifulsoup4-4.14.3.dist-info\nbs4\ncertifi\ncertifi-2026.4.22.dist-info\nclick\nclick-8.3.3.dist-info\ncmdop\ncmdop-2026.4.7.2.dist-info\ndotenv\nfilelock\nfilelock-3.29.0.dist-info\nfsspec\nfsspec-2026.3.0.dist-info\ngoogle\ngrpc\ngrpcio-1.80.0.dist-info\nh11\nh11-0.16.0.dist-info\nhf_xet\nhf_xet-1.4.3.dist-info","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022919237}}
+{"type":"message","id":"adc0b090","parentId":"706832d4","timestamp":"2026-04-24T09:28:44.047Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"I see there's no torch in site-packages. But the system Python (python3) is symlinked to /root/.venv/bin/python3 which IS the venv. So `python3 -m venv --system-site-packages` will use the system packages from... somewhere that has torch+cuda.\n\nLet me check what the system site packages contain - where is torch actually installed?","thinkingSignature":"f4a655729cdde058c2e49e79b4342f6cc0da316bc1d3999b58a6f4eaa6e9fca8"},{"type":"toolCall","id":"call_666bd64e16884c56aeb4b5fa","name":"exec","arguments":{"command":"# Check where torch is installed system-wide\nfind / -name \"torch\" -type d 2>/dev/null | head -10; find / -name \"auto_round\" -type d 2>/dev/null | head -10","timeout":15}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":278,"output":160,"cacheRead":20736,"cacheWrite":0,"totalTokens":21174,"cost":{"input":0.0000834,"output":0.000192,"cacheRead":0.0012441599999999998,"cacheWrite":0,"total":0.0015195599999999997}},"stopReason":"toolUse","timestamp":1777022919240,"responseId":"063a6ac75373cbe6f43ac689d2d8604f"}}
+{"type":"message","id":"8eb3f80e","parentId":"adc0b090","timestamp":"2026-04-24T09:28:46.239Z","message":{"role":"toolResult","toolCallId":"call_666bd64e16884c56aeb4b5fa","toolName":"exec","content":[{"type":"text","text":"(no output)"}],"details":{"status":"completed","exitCode":0,"durationMs":2187,"aggregated":"","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022926237}}
+{"type":"message","id":"4c291960","parentId":"8eb3f80e","timestamp":"2026-04-24T09:28:49.951Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"torch and auto_round are not installed anywhere. I need to create a new venv and install everything. Let me follow the skill instructions.\n\nLet me create a venv with --system-site-packages and then install torch and auto-round.","thinkingSignature":"260bdba80e3666f33596c2b5c93692471a04fc7a86fa0a148e3dfbe06273f878"},{"type":"toolCall","id":"call_232f3596fe58449bb670ac61","name":"exec","arguments":{"command":"python3 -m venv --system-site-packages /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv 2>&1"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":120,"output":113,"cacheRead":20992,"cacheWrite":0,"totalTokens":21225,"cost":{"input":0.000036,"output":0.0001356,"cacheRead":0.0012595199999999999,"cacheWrite":0,"total":0.00143112}},"stopReason":"toolUse","timestamp":1777022926240,"responseId":"063a6aceb0f8810dfefa529aeba43ad2"}}
+{"type":"message","id":"ad2933b5","parentId":"4c291960","timestamp":"2026-04-24T09:28:51.982Z","message":{"role":"toolResult","toolCallId":"call_232f3596fe58449bb670ac61","toolName":"exec","content":[{"type":"text","text":"(no output)"}],"details":{"status":"completed","exitCode":0,"durationMs":2025,"aggregated":"","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022931981}}
+{"type":"message","id":"70faf02a","parentId":"ad2933b5","timestamp":"2026-04-24T09:28:55.378Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me set up the venv and install dependencies.","thinkingSignature":"16a9723cc73ba896cdb01734a666cc4393ee935511c5f219b4c32119aaffcffc"},{"type":"toolCall","id":"call_a671f27500974f079c6ec880","name":"exec","arguments":{"command":"VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\n$VENV/bin/python -m pip install -U uv 2>&1 | tail -3"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":137,"output":85,"cacheRead":21056,"cacheWrite":0,"totalTokens":21278,"cost":{"input":0.000041099999999999996,"output":0.000102,"cacheRead":0.0012633599999999998,"cacheWrite":0,"total":0.0014064599999999998}},"stopReason":"toolUse","timestamp":1777022931982,"responseId":"063a6ad472d6cc30a042f03415bcbb5c"}}
+{"type":"message","id":"70d7aed7","parentId":"70faf02a","timestamp":"2026-04-24T09:28:56.278Z","message":{"role":"toolResult","toolCallId":"call_a671f27500974f079c6ec880","toolName":"exec","content":[{"type":"text","text":"[notice] A new release of pip is available: 25.0.1 -> 26.0.1\n[notice] To update, run: python -m pip install --upgrade pip"}],"details":{"status":"completed","exitCode":0,"durationMs":895,"aggregated":"[notice] A new release of pip is available: 25.0.1 -> 26.0.1\n[notice] To update, run: python -m pip install --upgrade pip","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022936276}}
+{"type":"message","id":"8efa652d","parentId":"70d7aed7","timestamp":"2026-04-24T09:28:59.850Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me install torch with CUDA support and auto-round.\n","thinkingSignature":"b0511b2b16935f91737b34b6833c2f6982719d096ceef30cc3db774fba99cf55"},{"type":"toolCall","id":"call_33467c026eec451bb0330a99","name":"exec","arguments":{"command":"VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nuv pip install --python $VENV/bin/python torch --index-url https://download.pytorch.org/whl/cu124 2>&1 | tail -5","timeout":300,"yieldMs":300000}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":136,"output":121,"cacheRead":21184,"cacheWrite":0,"totalTokens":21441,"cost":{"input":0.000040799999999999996,"output":0.0001452,"cacheRead":0.00127104,"cacheWrite":0,"total":0.00145704}},"stopReason":"toolUse","timestamp":1777022936278,"responseId":"063a6ad86cf831a4bacc0497010306b7"}}
+{"type":"message","id":"69b28a4a","parentId":"8efa652d","timestamp":"2026-04-24T09:29:26.716Z","message":{"role":"toolResult","toolCallId":"call_33467c026eec451bb0330a99","toolName":"exec","content":[{"type":"text","text":"+ setuptools==70.2.0\n + sympy==1.13.1\n + torch==2.6.0+cu124\n + triton==3.2.0\n + typing-extensions==4.15.0"}],"details":{"status":"completed","exitCode":0,"durationMs":26863,"aggregated":"+ setuptools==70.2.0\n + sympy==1.13.1\n + torch==2.6.0+cu124\n + triton==3.2.0\n + typing-extensions==4.15.0","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022966716}}
+{"type":"message","id":"3dac4c6f","parentId":"69b28a4a","timestamp":"2026-04-24T09:29:31.403Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Good, torch is installed. Now let me install auto-round.","thinkingSignature":"d5e030d02b0db90483b7a3a932cd6c5d293eea774af5cc4adc85616c9d4c5f5c"},{"type":"toolCall","id":"call_be83b177c5c44b619431b1f3","name":"exec","arguments":{"command":"VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nuv pip install --python $VENV/bin/python auto-round 2>&1 | tail -10","timeout":300,"yieldMs":300000}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":182,"output":110,"cacheRead":21312,"cacheWrite":0,"totalTokens":21604,"cost":{"input":0.0000546,"output":0.00013199999999999998,"cacheRead":0.00127872,"cacheWrite":0,"total":0.00146532}},"stopReason":"toolUse","timestamp":1777022966717,"responseId":"063a6af8e8c09b9325dc2a652238c57b"}}
+{"type":"message","id":"f5fee2e4","parentId":"3dac4c6f","timestamp":"2026-04-24T09:29:34.744Z","message":{"role":"toolResult","toolCallId":"call_be83b177c5c44b619431b1f3","toolName":"exec","content":[{"type":"text","text":"+ six==1.17.0\n + threadpoolctl==3.6.0\n + tokenizers==0.22.2\n + tqdm==4.67.3\n + transformers==5.6.2\n + typer==0.24.2\n + typing-inspection==0.4.2\n + urllib3==2.6.3\n + xxhash==3.6.0\n + yarl==1.23.0"}],"details":{"status":"completed","exitCode":0,"durationMs":3337,"aggregated":"+ six==1.17.0\n + threadpoolctl==3.6.0\n + tokenizers==0.22.2\n + tqdm==4.67.3\n + transformers==5.6.2\n + typer==0.24.2\n + typing-inspection==0.4.2\n + urllib3==2.6.3\n + xxhash==3.6.0\n + yarl==1.23.0","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022974743}}
+{"type":"message","id":"28ab5508","parentId":"f5fee2e4","timestamp":"2026-04-24T09:29:38.915Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me verify torch and auto_round work properly.","thinkingSignature":"e44836b4bb5b20602ab5907220e0e7fa5c1d2acac60eedcb2f109034c17389fd"},{"type":"toolCall","id":"call_2e87e9e588c14fde9e9e3423","name":"exec","arguments":{"command":"VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\n$VENV/bin/python -c \"import torch; print('torch:', torch.__version__, 'cuda:', torch.cuda.is_available())\" 2>&1\n$VENV/bin/python -c \"import auto_round; print('auto_round ok, version:', auto_round.__version__)\" 2>&1"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":261,"output":127,"cacheRead":21440,"cacheWrite":0,"totalTokens":21828,"cost":{"input":0.00007829999999999999,"output":0.0001524,"cacheRead":0.0012863999999999998,"cacheWrite":0,"total":0.0015170999999999997}},"stopReason":"toolUse","timestamp":1777022974744,"responseId":"063a6affed4f687a4af20e71971f6d38"}}
+{"type":"message","id":"993308c0","parentId":"28ab5508","timestamp":"2026-04-24T09:29:48.932Z","message":{"role":"toolResult","toolCallId":"call_2e87e9e588c14fde9e9e3423","toolName":"exec","content":[{"type":"text","text":"Command still running (session kind-cloud, pid 542). Use process (list/poll/log/write/kill/clear/remove) for follow-up."}],"details":{"status":"running","sessionId":"kind-cloud","pid":542,"startedAt":1777022978918,"cwd":"/root/.openclaw/workspace","tail":"torch: 2.6.0+cu124 cuda: True\n"},"isError":false,"timestamp":1777022988930}}
+{"type":"message","id":"ee833745","parentId":"993308c0","timestamp":"2026-04-24T09:29:54.491Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me poll for results.","thinkingSignature":"d02c11589dac02a351bcab51a9b8df6207f977dc67690730c68d1f141967bf96"},{"type":"toolCall","id":"call_4d9d3f8fb02849ac85431d5f","name":"process","arguments":{"action":"poll","sessionId":"kind-cloud","timeout":30000}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":163,"output":54,"cacheRead":21696,"cacheWrite":0,"totalTokens":21913,"cost":{"input":0.000048899999999999996,"output":0.0000648,"cacheRead":0.00130176,"cacheWrite":0,"total":0.00141546}},"stopReason":"toolUse","timestamp":1777022988932,"responseId":"063a6b0e553514dc3435384a013a6641"}}
+{"type":"message","id":"2b4cc7fe","parentId":"ee833745","timestamp":"2026-04-24T09:29:54.499Z","message":{"role":"toolResult","toolCallId":"call_4d9d3f8fb02849ac85431d5f","toolName":"process","content":[{"type":"text","text":"torch: 2.6.0+cu124 cuda: True\nauto_round ok, version: 0.12.2\n\n\nProcess exited with code 0."}],"details":{"status":"completed","sessionId":"kind-cloud","exitCode":0,"aggregated":"torch: 2.6.0+cu124 cuda: True\nauto_round ok, version: 0.12.2\n","name":"VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv $VENV/bin/python"},"isError":false,"timestamp":1777022994498}}
+{"type":"message","id":"eccdc952","parentId":"2b4cc7fe","timestamp":"2026-04-24T09:29:57.955Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Both torch and auto_round are installed and working. Now let me install transformers and datasets as well, and then create the quantization script.","thinkingSignature":"6670971aaf44cae3fe58f5b7723b6f3627f35b44edccb4344208e7d200ce49dc"},{"type":"toolCall","id":"call_be6269c20ddf49a48f58954a","name":"exec","arguments":{"command":"VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nuv pip install --python $VENV/bin/python transformers accelerate datasets 2>&1 | tail -5","timeout":120}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":130,"output":113,"cacheRead":21824,"cacheWrite":0,"totalTokens":22067,"cost":{"input":0.000039,"output":0.0001356,"cacheRead":0.0013094399999999998,"cacheWrite":0,"total":0.0014840399999999998}},"stopReason":"toolUse","timestamp":1777022994499,"responseId":"063a6b126dd1c24ba80525773d087355"}}
+{"type":"message","id":"1ea9dbc9","parentId":"eccdc952","timestamp":"2026-04-24T09:29:58.030Z","message":{"role":"toolResult","toolCallId":"call_be6269c20ddf49a48f58954a","toolName":"exec","content":[{"type":"text","text":"Using Python 3.12.13 environment at: quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nChecked 3 packages in 49ms"}],"details":{"status":"completed","exitCode":0,"durationMs":70,"aggregated":"Using Python 3.12.13 environment at: quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nChecked 3 packages in 49ms","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777022998029}}
+{"type":"message","id":"727309d2","parentId":"1ea9dbc9","timestamp":"2026-04-24T09:30:30.597Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me create the quantization script. For RTN, we use `iters=0`. Let me create the quantize.py script.","thinkingSignature":"219eb70e6fc2cfd0b4e54887097147fa254062e633692da9b87a212c1e06c0c1"},{"type":"text","text":"\n\nNow let me write the quantization script:\n"},{"type":"toolCall","id":"call_c339cadbe0d547ff9566acad","name":"write","arguments":{"content":"#!/usr/bin/env python3\n\"\"\"\nAuto-Round Quantization Script\nGenerated by auto_quant skill\n\nModel: Qwen/Qwen3-1.7B\nOutput: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\nScheme: W4A16\nIterations: 0 (RTN mode)\nSamples: 128\nFormat: auto_round\n\"\"\"\n\nimport os\nimport sys\nimport time\nimport json\nimport traceback\nfrom pathlib import Path\n\n# Track start time\nstart_time = time.time()\n\n# Output directories\nOUTPUT_DIR = \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\"\nRUN_DIR = \"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\"\nVENV_BIN = f\"{RUN_DIR}/venv/bin/python\"\n\n# Track errors and solutions\nerrors = []\nsolutions = []\n\nprint(f\"Starting quantization at {time.strftime('%Y-%m-%d %H:%M:%S')}\")\nprint(f\"Model: Qwen/Qwen3-1.7B\")\nprint(f\"Scheme: W4A16\")\nprint(f\"Method: RTN (iters=0)\")\nprint(f\"Format: auto_round\")\nprint(f\"Device: cuda (single GPU)\")\nprint(f\"Output: {OUTPUT_DIR}\")\n\ntry:\n    from auto_round import AutoRound\n\n    # Configuration\n    model_name_or_path = \"Qwen/Qwen3-1.7B\"\n    scheme = \"W4A16\"\n    iters = 0  # RTN mode\n    nsamples = 128\n    format_str = \"auto_round\"\n\n    print(f\"\\nLoading model: {model_name_or_path}\")\n    print(f\"Scheme: {scheme}\")\n    print(f\"Iters: {iters} (RTN mode)\")\n    print(f\"nsamples: {nsamples}\")\n    print(f\"Format: {format_str}\")\n    print(f\"Device: cuda\")\n\n    # Create AutoRound instance\n    ar = AutoRound(\n        model_name_or_path,\n        scheme=scheme,\n        iters=iters,\n        nsamples=nsamples,\n        device=\"cuda\",\n    )\n\n    # Quantize and save\n    print(\"\\nStarting quantization...\")\n    ar.quantize_and_save(output_dir=OUTPUT_DIR, format=format_str)\n\n    print(f\"\\nQuantization complete! Output: {OUTPUT_DIR}\")\n\nexcept Exception as e:\n    errors.append(str(e))\n    errors.append(traceback.format_exc())\n    print(f\"\\nQuantization failed: {e}\")\n    traceback.print_exc()\n\n    # Try recovery solutions\n    try:\n        solutions.append(\"Attempted to use RTN mode (iters=0) for stable quantization\")\n    except:\n        pass\n\nend_time = time.time()\nduration = end_time - start_time\n\n# Calculate sizes\noriginal_size_mb = None\nquantized_size_mb = None\ncompression_ratio = None\n\nquantized_path = Path(OUTPUT_DIR)\nif quantized_path.exists():\n    total_size = sum(f.stat().st_size for f in quantized_path.rglob(\"*\") if f.is_file())\n    quantized_size_mb = total_size / (1024 * 1024)\n\n# Estimate original model size (Qwen3-1.7B in BF16 ~3.4GB)\noriginal_size_mb = 3400.0  # Approximate BF16 size for 1.7B model in MB\nif quantized_size_mb and original_size_mb:\n    compression_ratio = original_size_mb / quantized_size_mb\n\n# List output files\noutput_files = []\nif quantized_path.exists():\n    for f in sorted(quantized_path.rglob(\"*\")):\n        if f.is_file() and not f.name.endswith(('.pyc', '.pyo', '__pycache__')):\n            size = f.stat().st_size\n            output_files.append(str(f))\n\n# Determine status\nstatus = \"failed\" if errors else \"success\"\n\n# Build summary\nsummary = {\n    \"model_id\": \"Qwen/Qwen3-1.7B\",\n    \"scheme\": \"W4A16\",\n    \"method\": \"RTN\",\n    \"export_format\": \"auto_round\",\n    \"device\": \"cuda\",\n    \"quant_num_gpus\": \"1\",\n    \"num_gpus\": \"1\",\n    \"output_dir\": OUTPUT_DIR,\n    \"runtime_output_dir\": RUN_DIR,\n    \"quantized_model_dir\": OUTPUT_DIR,\n    \"status\": status,\n    \"duration_seconds\": duration,\n    \"original_size_mb\": original_size_mb,\n    \"quantized_size_mb\": quantized_size_mb,\n    \"compression_ratio\": compression_ratio,\n    \"errors\": errors,\n    \"solutions\": solutions,\n    \"output_files\": output_files\n}\n\n# Write summary JSON\nsummary_path = Path(RUN_DIR) / \"quant_summary.json\"\nwith open(summary_path, \"w\") as f:\n    json.dump(summary, f, indent=2)\n\nprint(f\"\\nQuantization {'FAILED' if errors else 'SUCCEEDED'}\")\nprint(f\"Duration: {duration:.2f}s ({duration/60:.2f} min)\")\nprint(f\"Summary: {summary_path}\")\n\nif errors:\n    print(\"\\nErrors:\")\n    for e in errors:\n        print(f\"  - {e}\")\n    sys.exit(1)\nelse:\n    sys.exit(0)","file":"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quantize.py"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":204,"output":1246,"cacheRead":21888,"cacheWrite":0,"totalTokens":23338,"cost":{"input":0.0000612,"output":0.0014952,"cacheRead":0.0013132799999999998,"cacheWrite":0,"total":0.0028696799999999995}},"stopReason":"toolUse","timestamp":1777022998031,"responseId":"063a6b16243dff2e7fba6ac15a4d1940"}}
+{"type":"message","id":"16a9d6e3","parentId":"727309d2","timestamp":"2026-04-24T09:30:30.602Z","message":{"role":"toolResult","toolCallId":"call_c339cadbe0d547ff9566acad","toolName":"write","content":[{"type":"text","text":"Successfully wrote 3954 bytes to /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quantize.py"}],"isError":false,"timestamp":1777023030601}}
+{"type":"message","id":"51f61700","parentId":"16a9d6e3","timestamp":"2026-04-24T09:30:35.266Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now let me run the quantization script.","thinkingSignature":"03db750c87ca29999a3f47205ec90d9b8b88e2ee2763ab6f8948b083c9dcb2ad"},{"type":"toolCall","id":"call_79ee0b8beedd4fd0a2966e69","name":"exec","arguments":{"command":"cd /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16 && ./venv/bin/python quantize.py 2>&1","timeout":600,"yieldMs":600000}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":1280,"output":93,"cacheRead":22080,"cacheWrite":0,"totalTokens":23453,"cost":{"input":0.000384,"output":0.00011159999999999999,"cacheRead":0.0013248,"cacheWrite":0,"total":0.0018203999999999998}},"stopReason":"toolUse","timestamp":1777023030603,"responseId":"063a6b37ed8f3b6ffcc297bba5850dc7"}}
+{"type":"message","id":"fb19a37c","parentId":"51f61700","timestamp":"2026-04-24T09:32:06.498Z","message":{"role":"toolResult","toolCallId":"call_79ee0b8beedd4fd0a2966e69","toolName":"exec","content":[{"type":"text","text":"Starting quantization at 2026-04-24 09:30:35\nModel: Qwen/Qwen3-1.7B\nScheme: W4A16\nMethod: RTN (iters=0)\nFormat: auto_round\nDevice: cuda (single GPU)\nOutput: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\n\nLoading model: Qwen/Qwen3-1.7B\nScheme: W4A16\nIters: 0 (RTN mode)\nnsamples: 128\nFormat: auto_round\nDevice: cuda\n404 Client Error. (Request ID: Root=1-69eb383f-3f9c26750c38c2c7775094ec;73c180a5-2b61-451d-b2a9-356cdb139351)\n\nEntry Not Found for url: https://huggingface.co/Qwen/Qwen3-1.7B/resolve/main/model_index.json.\nWarning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n[33;1m2026-04-24 09:30:48 WARNING base.py L407: `device` is deprecated, please use `device_map` instead[0m\n[38;20m2026-04-24 09:30:48 INFO base.py L473: `enable_opt_rtn` is turned on, set `--disable_opt_rtn` for higher speed at the cost of accuracy.[0m\n[38;20m2026-04-24 09:30:48 INFO base.py L517: using torch.bfloat16 for quantization tuning[0m\n\nStarting quantization...\n[33;1m2026-04-24 09:30:48 WARNING formats.py L166: some layers are skipped quantization (shape not divisible by 32): [0m\n[33;1m2026-04-24 09:30:48 WARNING modeling_utils.py L4519: `loss_type=None` was set in the config but it is unrecognized. Using the default loss: `ForCausalLMLoss`.[0m\n[38;20m2026-04-24 09:30:48 INFO base.py L1150: start to compute imatrix[0m\n\rGenerating train split:   0%|          | 0/10000 [00:00<?, ? examples/s]\rGenerating train split: 100%|██████████| 10000/10000 [00:00<00:00, 49537.37 examples/s]\n\rMap:   0%|          | 0/10000 [00:00<?, ? examples/s]\rMap: 100%|██████████| 10000/10000 [00:08<00:00, 1149.80 examples/s]\n\rFilter:   0%|          | 0/10000 [00:00<?, ? examples/s]\rFilter: 100%|██████████| 10000/10000 [00:04<00:00, 2402.99 examples/s]\n\rCasting the dataset:   0%|          | 0/1216 [00:00<?, ? examples/s]\rCasting the dataset: 100%|██████████| 1216/1216 [00:03<00:00, 387.92 examples/s]\n\r  0%|          | 0/28 [00:00<?, ?it/s]\rQuantizing model.layers.0:   0%|          | 0/28 [00:00<?, ?it/s][38;20m2026-04-24 09:31:13 INFO device.py L1692: 'peak_ram': 10.16GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.1:   4%|▎         | 1/28 [00:03<01:22,  3.06s/it][38;20m2026-04-24 09:31:15 INFO device.py L1692: 'peak_ram': 10.16GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.2:   7%|▋         | 2/28 [00:04<01:04,  2.47s/it][38;20m2026-04-24 09:31:17 INFO device.py L1692: 'peak_ram': 10.16GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.3:  11%|█         | 3/28 [00:06<00:56,  2.28s/it][38;20m2026-04-24 09:31:18 INFO device.py L1692: 'peak_ram': 10.25GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.4:  14%|█▍        | 4/28 [00:08<00:52,  2.17s/it][38;20m2026-04-24 09:31:20 INFO device.py L1692: 'peak_ram': 10.37GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.5:  18%|█▊        | 5/28 [00:10<00:48,  2.11s/it][38;20m2026-04-24 09:31:22 INFO device.py L1692: 'peak_ram': 10.48GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.6:  21%|██▏       | 6/28 [00:12<00:45,  2.07s/it][38;20m2026-04-24 09:31:24 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.7:  25%|██▌       | 7/28 [00:14<00:43,  2.05s/it][38;20m2026-04-24 09:31:26 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.8:  29%|██▊       | 8/28 [00:16<00:40,  2.02s/it][38;20m2026-04-24 09:31:28 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.9:  32%|███▏      | 9/28 [00:18<00:38,  2.01s/it][38;20m2026-04-24 09:31:30 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.10:  36%|███▌      | 10/28 [00:19<00:35,  1.98s/it][38;20m2026-04-24 09:31:31 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.11:  39%|███▉      | 11/28 [00:21<00:33,  1.97s/it][38;20m2026-04-24 09:31:33 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.12:  43%|████▎     | 12/28 [00:23<00:31,  1.96s/it][38;20m2026-04-24 09:31:35 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.13:  46%|████▋     | 13/28 [00:25<00:29,  1.95s/it][38;20m2026-04-24 09:31:37 INFO device.py L1692: 'peak_ram': 10.7GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.14:  50%|█████     | 14/28 [00:27<00:27,  1.94s/it][38;20m2026-04-24 09:31:39 INFO device.py L1692: 'peak_ram': 10.81GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.15:  54%|█████▎    | 15/28 [00:28<00:25,  1.93s/it][38;20m2026-04-24 09:31:41 INFO device.py L1692: 'peak_ram': 10.94GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.16:  57%|█████▋    | 16/28 [00:30<00:23,  1.93s/it][38;20m2026-04-24 09:31:42 INFO device.py L1692: 'peak_ram': 11.05GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.17:  61%|██████    | 17/28 [00:32<00:21,  1.92s/it][38;20m2026-04-24 09:31:44 INFO device.py L1692: 'peak_ram': 11.17GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.18:  64%|██████▍   | 18/28 [00:34<00:19,  1.92s/it][38;20m2026-04-24 09:31:46 INFO device.py L1692: 'peak_ram': 11.29GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.19:  68%|██████▊   | 19/28 [00:36<00:17,  1.91s/it][38;20m2026-04-24 09:31:48 INFO device.py L1692: 'peak_ram': 11.4GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.20:  71%|███████▏  | 20/28 [00:38<00:15,  1.91s/it][38;20m2026-04-24 09:31:50 INFO device.py L1692: 'peak_ram': 11.53GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.21:  75%|███████▌  | 21/28 [00:40<00:13,  1.91s/it][38;20m2026-04-24 09:31:52 INFO device.py L1692: 'peak_ram': 11.64GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.22:  79%|███████▊  | 22/28 [00:41<00:11,  1.90s/it][38;20m2026-04-24 09:31:53 INFO device.py L1692: 'peak_ram': 11.76GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.23:  82%|████████▏ | 23/28 [00:43<00:09,  1.90s/it][38;20m2026-04-24 09:31:55 INFO device.py L1692: 'peak_ram': 11.87GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.24:  86%|████████▌ | 24/28 [00:45<00:07,  1.90s/it][38;20m2026-04-24 09:31:57 INFO device.py L1692: 'peak_ram': 12.0GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.25:  89%|████████▉ | 25/28 [00:47<00:05,  1.90s/it][38;20m2026-04-24 09:31:59 INFO device.py L1692: 'peak_ram': 12.11GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.26:  93%|█████████▎| 26/28 [00:49<00:03,  1.90s/it][38;20m2026-04-24 09:32:01 INFO device.py L1692: 'peak_ram': 12.24GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.27:  96%|█████████▋| 27/28 [00:51<00:01,  1.90s/it][38;20m2026-04-24 09:32:03 INFO device.py L1692: 'peak_ram': 12.35GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.27: 100%|██████████| 28/28 [00:53<00:00,  1.90s/it]\n[38;20m2026-04-24 09:32:04 INFO shard_writer.py L250: model has been saved to /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/[0m\n[38;20m2026-04-24 09:32:04 INFO device.py L1692: 'peak_ram': 12.35GB, 'peak_vram': 3.98GB[0m\n\nQuantization complete! Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\n\nQuantization SUCCEEDED\nDuration: 89.69s (1.49 min)\nSummary: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json"}],"details":{"status":"completed","exitCode":0,"durationMs":91227,"aggregated":"Starting quantization at 2026-04-24 09:30:35\nModel: Qwen/Qwen3-1.7B\nScheme: W4A16\nMethod: RTN (iters=0)\nFormat: auto_round\nDevice: cuda (single GPU)\nOutput: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\n\nLoading model: Qwen/Qwen3-1.7B\nScheme: W4A16\nIters: 0 (RTN mode)\nnsamples: 128\nFormat: auto_round\nDevice: cuda\n404 Client Error. (Request ID: Root=1-69eb383f-3f9c26750c38c2c7775094ec;73c180a5-2b61-451d-b2a9-356cdb139351)\n\nEntry Not Found for url: https://huggingface.co/Qwen/Qwen3-1.7B/resolve/main/model_index.json.\nWarning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n[33;1m2026-04-24 09:30:48 WARNING base.py L407: `device` is deprecated, please use `device_map` instead[0m\n[38;20m2026-04-24 09:30:48 INFO base.py L473: `enable_opt_rtn` is turned on, set `--disable_opt_rtn` for higher speed at the cost of accuracy.[0m\n[38;20m2026-04-24 09:30:48 INFO base.py L517: using torch.bfloat16 for quantization tuning[0m\n\nStarting quantization...\n[33;1m2026-04-24 09:30:48 WARNING formats.py L166: some layers are skipped quantization (shape not divisible by 32): [0m\n[33;1m2026-04-24 09:30:48 WARNING modeling_utils.py L4519: `loss_type=None` was set in the config but it is unrecognized. Using the default loss: `ForCausalLMLoss`.[0m\n[38;20m2026-04-24 09:30:48 INFO base.py L1150: start to compute imatrix[0m\n\rGenerating train split:   0%|          | 0/10000 [00:00<?, ? examples/s]\rGenerating train split: 100%|██████████| 10000/10000 [00:00<00:00, 49537.37 examples/s]\n\rMap:   0%|          | 0/10000 [00:00<?, ? examples/s]\rMap: 100%|██████████| 10000/10000 [00:08<00:00, 1149.80 examples/s]\n\rFilter:   0%|          | 0/10000 [00:00<?, ? examples/s]\rFilter: 100%|██████████| 10000/10000 [00:04<00:00, 2402.99 examples/s]\n\rCasting the dataset:   0%|          | 0/1216 [00:00<?, ? examples/s]\rCasting the dataset: 100%|██████████| 1216/1216 [00:03<00:00, 387.92 examples/s]\n\r  0%|          | 0/28 [00:00<?, ?it/s]\rQuantizing model.layers.0:   0%|          | 0/28 [00:00<?, ?it/s][38;20m2026-04-24 09:31:13 INFO device.py L1692: 'peak_ram': 10.16GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.1:   4%|▎         | 1/28 [00:03<01:22,  3.06s/it][38;20m2026-04-24 09:31:15 INFO device.py L1692: 'peak_ram': 10.16GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.2:   7%|▋         | 2/28 [00:04<01:04,  2.47s/it][38;20m2026-04-24 09:31:17 INFO device.py L1692: 'peak_ram': 10.16GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.3:  11%|█         | 3/28 [00:06<00:56,  2.28s/it][38;20m2026-04-24 09:31:18 INFO device.py L1692: 'peak_ram': 10.25GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.4:  14%|█▍        | 4/28 [00:08<00:52,  2.17s/it][38;20m2026-04-24 09:31:20 INFO device.py L1692: 'peak_ram': 10.37GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.5:  18%|█▊        | 5/28 [00:10<00:48,  2.11s/it][38;20m2026-04-24 09:31:22 INFO device.py L1692: 'peak_ram': 10.48GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.6:  21%|██▏       | 6/28 [00:12<00:45,  2.07s/it][38;20m2026-04-24 09:31:24 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.7:  25%|██▌       | 7/28 [00:14<00:43,  2.05s/it][38;20m2026-04-24 09:31:26 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.8:  29%|██▊       | 8/28 [00:16<00:40,  2.02s/it][38;20m2026-04-24 09:31:28 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.9:  32%|███▏      | 9/28 [00:18<00:38,  2.01s/it][38;20m2026-04-24 09:31:30 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.10:  36%|███▌      | 10/28 [00:19<00:35,  1.98s/it][38;20m2026-04-24 09:31:31 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.11:  39%|███▉      | 11/28 [00:21<00:33,  1.97s/it][38;20m2026-04-24 09:31:33 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.12:  43%|████▎     | 12/28 [00:23<00:31,  1.96s/it][38;20m2026-04-24 09:31:35 INFO device.py L1692: 'peak_ram': 10.6GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.13:  46%|████▋     | 13/28 [00:25<00:29,  1.95s/it][38;20m2026-04-24 09:31:37 INFO device.py L1692: 'peak_ram': 10.7GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.14:  50%|█████     | 14/28 [00:27<00:27,  1.94s/it][38;20m2026-04-24 09:31:39 INFO device.py L1692: 'peak_ram': 10.81GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.15:  54%|█████▎    | 15/28 [00:28<00:25,  1.93s/it][38;20m2026-04-24 09:31:41 INFO device.py L1692: 'peak_ram': 10.94GB, 'peak_vram': 3.91GB[0m\n\rQuantizing model.layers.16:  57%|█████▋    | 16/28 [00:30<00:23,  1.93s/it][38;20m2026-04-24 09:31:42 INFO device.py L1692: 'peak_ram': 11.05GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.17:  61%|██████    | 17/28 [00:32<00:21,  1.92s/it][38;20m2026-04-24 09:31:44 INFO device.py L1692: 'peak_ram': 11.17GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.18:  64%|██████▍   | 18/28 [00:34<00:19,  1.92s/it][38;20m2026-04-24 09:31:46 INFO device.py L1692: 'peak_ram': 11.29GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.19:  68%|██████▊   | 19/28 [00:36<00:17,  1.91s/it][38;20m2026-04-24 09:31:48 INFO device.py L1692: 'peak_ram': 11.4GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.20:  71%|███████▏  | 20/28 [00:38<00:15,  1.91s/it][38;20m2026-04-24 09:31:50 INFO device.py L1692: 'peak_ram': 11.53GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.21:  75%|███████▌  | 21/28 [00:40<00:13,  1.91s/it][38;20m2026-04-24 09:31:52 INFO device.py L1692: 'peak_ram': 11.64GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.22:  79%|███████▊  | 22/28 [00:41<00:11,  1.90s/it][38;20m2026-04-24 09:31:53 INFO device.py L1692: 'peak_ram': 11.76GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.23:  82%|████████▏ | 23/28 [00:43<00:09,  1.90s/it][38;20m2026-04-24 09:31:55 INFO device.py L1692: 'peak_ram': 11.87GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.24:  86%|████████▌ | 24/28 [00:45<00:07,  1.90s/it][38;20m2026-04-24 09:31:57 INFO device.py L1692: 'peak_ram': 12.0GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.25:  89%|████████▉ | 25/28 [00:47<00:05,  1.90s/it][38;20m2026-04-24 09:31:59 INFO device.py L1692: 'peak_ram': 12.11GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.26:  93%|█████████▎| 26/28 [00:49<00:03,  1.90s/it][38;20m2026-04-24 09:32:01 INFO device.py L1692: 'peak_ram': 12.24GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.27:  96%|█████████▋| 27/28 [00:51<00:01,  1.90s/it][38;20m2026-04-24 09:32:03 INFO device.py L1692: 'peak_ram': 12.35GB, 'peak_vram': 3.98GB[0m\n\rQuantizing model.layers.27: 100%|██████████| 28/28 [00:53<00:00,  1.90s/it]\n[38;20m2026-04-24 09:32:04 INFO shard_writer.py L250: model has been saved to /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/[0m\n[38;20m2026-04-24 09:32:04 INFO device.py L1692: 'peak_ram': 12.35GB, 'peak_vram': 3.98GB[0m\n\nQuantization complete! Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\n\nQuantization SUCCEEDED\nDuration: 89.69s (1.49 min)\nSummary: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777023126496}}
+{"type":"message","id":"b4bb9dcc","parentId":"fb19a37c","timestamp":"2026-04-24T09:32:11.335Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The quantization succeeded. Now let me verify the output and check the actual file sizes to get accurate numbers for the summary.","thinkingSignature":"ee373a399249ed5235b4000f5c273d80693742da47d8201632ee251f370ee949"},{"type":"toolCall","id":"call_28e69ae7bc7c476e97e89bbc","name":"exec","arguments":{"command":"ls -la /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":3488,"output":76,"cacheRead":23296,"cacheWrite":0,"totalTokens":26860,"cost":{"input":0.0010463999999999998,"output":0.0000912,"cacheRead":0.0013977599999999998,"cacheWrite":0,"total":0.0025353599999999995}},"stopReason":"toolUse","timestamp":1777023126498,"responseId":"063a6b988d3c251d98cab90f3543bd24"}}
+{"type":"message","id":"360e92b0","parentId":"b4bb9dcc","timestamp":"2026-04-24T09:32:11.352Z","message":{"role":"toolResult","toolCallId":"call_28e69ae7bc7c476e97e89bbc","toolName":"exec","content":[{"type":"text","text":"total 1334316\ndrwxr-xr-x 2 root root      4096 Apr 24 09:32 .\ndrwxr-xr-x 4 root root        59 Apr 24 09:28 ..\n-rw-r--r-- 1 root root      4168 Apr 24 09:32 chat_template.jinja\n-rw-r--r-- 1 root root      1657 Apr 24 09:32 config.json\n-rw-r--r-- 1 root root       213 Apr 24 09:32 generation_config.json\n-rw-r--r-- 1 root root 732233592 Apr 24 09:32 model-00001-of-00002.safetensors\n-rw-r--r-- 1 root root 622590392 Apr 24 09:32 model-00002-of-00002.safetensors\n-rw-r--r-- 1 root root     57609 Apr 24 09:32 model.safetensors.index.json\n-rw-r--r-- 1 root root       196 Apr 24 09:32 quantization_config.json\n-rw-r--r-- 1 root root  11422749 Apr 24 09:32 tokenizer.json\n-rw-r--r-- 1 root root       694 Apr 24 09:32 tokenizer_config.json"}],"details":{"status":"completed","exitCode":0,"durationMs":12,"aggregated":"total 1334316\ndrwxr-xr-x 2 root root      4096 Apr 24 09:32 .\ndrwxr-xr-x 4 root root        59 Apr 24 09:28 ..\n-rw-r--r-- 1 root root      4168 Apr 24 09:32 chat_template.jinja\n-rw-r--r-- 1 root root      1657 Apr 24 09:32 config.json\n-rw-r--r-- 1 root root       213 Apr 24 09:32 generation_config.json\n-rw-r--r-- 1 root root 732233592 Apr 24 09:32 model-00001-of-00002.safetensors\n-rw-r--r-- 1 root root 622590392 Apr 24 09:32 model-00002-of-00002.safetensors\n-rw-r--r-- 1 root root     57609 Apr 24 09:32 model.safetensors.index.json\n-rw-r--r-- 1 root root       196 Apr 24 09:32 quantization_config.json\n-rw-r--r-- 1 root root  11422749 Apr 24 09:32 tokenizer.json\n-rw-r--r-- 1 root root       694 Apr 24 09:32 tokenizer_config.json","cwd":"/root/.openclaw/workspace"},"isError":false,"timestamp":1777023131351}}
+{"type":"message","id":"196bf8fc","parentId":"360e92b0","timestamp":"2026-04-24T09:32:15.184Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The model is quantized and saved. Let me now update the quant_summary.json with the correct file list and accurate sizes.","thinkingSignature":"5f95fbbb3d60d78ffbb43e9118e67aead2bb89dd904adbfca98b2bb728595cae"},{"type":"toolCall","id":"call_7040e60e4670416c892075de","name":"read","arguments":{"file":"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json"}}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":387,"output":78,"cacheRead":26752,"cacheWrite":0,"totalTokens":27217,"cost":{"input":0.00011609999999999999,"output":0.0000936,"cacheRead":0.0016051199999999998,"cacheWrite":0,"total":0.0018148199999999998}},"stopReason":"toolUse","timestamp":1777023131352,"responseId":"063a6b9b42afa6664ff0a9a4a68a3b17"}}
+{"type":"message","id":"bf0393e2","parentId":"196bf8fc","timestamp":"2026-04-24T09:32:15.188Z","message":{"role":"toolResult","toolCallId":"call_7040e60e4670416c892075de","toolName":"read","content":[{"type":"text","text":"{\n  \"model_id\": \"Qwen/Qwen3-1.7B\",\n  \"scheme\": \"W4A16\",\n  \"method\": \"RTN\",\n  \"export_format\": \"auto_round\",\n  \"device\": \"cuda\",\n  \"quant_num_gpus\": \"1\",\n  \"num_gpus\": \"1\",\n  \"output_dir\": \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\",\n  \"runtime_output_dir\": \"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\",\n  \"quantized_model_dir\": \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\",\n  \"status\": \"success\",\n  \"duration_seconds\": 89.69453978538513,\n  \"original_size_mb\": 3400.0,\n  \"quantized_size_mb\": 1303.0159664154053,\n  \"compression_ratio\": 2.609331034794143,\n  \"errors\": [],\n  \"solutions\": [],\n  \"output_files\": [\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/chat_template.jinja\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/config.json\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/generation_config.json\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00001-of-00002.safetensors\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00002-of-00002.safetensors\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model.safetensors.index.json\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/quantization_config.json\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer.json\",\n    \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer_config.json\"\n  ]\n}"}],"isError":false,"timestamp":1777023135187}}
+{"type":"message","id":"67198692","parentId":"bf0393e2","timestamp":"2026-04-24T09:32:24.663Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"The quantization was successful. Let me provide the summary to the user.\n\nResults:\n- Model: Qwen/Qwen3-1.7B\n- Scheme: W4A16\n- Method: RTN (Round-to-Nearest, iters=0)\n- Format: auto_round\n- Status: SUCCESS\n- Duration: ~90 seconds (1.49 min)\n- Original size: ~3400 MB (estimated BF16)\n- Quantized size: ~1303 MB\n- Compression ratio: ~2.61x\n- Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\n\n\n- Runtime artifacts: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/","thinkingSignature":"c11b5f9f5f4238d7bf71f60fff337e0df9a598321cdd07ab357c83340e2c3a8c"},{"type":"text","text":"\n\n**Quantization completed successfully!**\n\n| Field | Value |\n|-------|-------|\n| Model | Qwen/Qwen3-1.7B |\n| Scheme | W4A16 (INT4 weight / FP16 activation) |\n| Method | RTN (Round-to-Nearest, iters=0) |\n| Format | auto_round |\n| Device | cuda (RTX 4090) |\n| Duration | **89.7s (~1.5 min)** |\n| Original size | ~3,400 MB (BF16 estimate) |\n| Quantized size | **1,303 MB** |\n| Compression | **2.61x** |\n\n**Output files** → `/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/`:\n- `model-00001-of-00002.safetensors` (698 MB)\n- `model-00002-of-00002.safetensors` (594 MB)\n- `quantization_config.json`\n- `config.json`, `tokenizer.json`, `chat_template.jinja`, etc.\n\n**Runtime artifacts** → `/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/`:\n- `quantize.py` — the quantization script\n- `quant_summary.json` — structured summary\n- `venv/` — Python environment with torch 2.6.0+cu124 and auto-round 0.12.2\n\nPeak VRAM usage was ~3.98 GB, well within the RTX 4090's 24 GB."}],"api":"anthropic-messages","provider":"minimax","model":"MiniMax-M2.7","usage":{"input":671,"output":505,"cacheRead":27072,"cacheWrite":0,"totalTokens":28248,"cost":{"input":0.0002013,"output":0.000606,"cacheRead":0.0016243199999999998,"cacheWrite":0,"total":0.00243162}},"stopReason":"stop","timestamp":1777023135188,"responseId":"063a6b9fb919d07eaa736344078493ec"}}

Qwen/Qwen3-1.7B-autoround-W4A16/run_2026-04-24-10-24-37/session_quant_320.md

@@ -0,0 +1,1754 @@
+# Session: autoeval_quant_320
+
+- **Session ID:** `autoeval_quant_320`
+- **Timestamp:** 2026-04-24 09:27:42 UTC
+- **Working Dir:** `/root/.openclaw/workspace`
+
+## Step 1: Quantization
+
+### [2026-04-24 09:27:42 UTC] USER
+
+You are an expert in LLM quantization using the Intel Auto-Round toolkit.
+You MUST follow the skill instructions in: /root/.openclaw/workspace/skills/auto_quant/SKILL.md
+
+Model: Qwen/Qwen3-1.7B
+Quantization: W4A16 / RTN
+Export format: auto_round
+Quantized Model Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16
+Runtime device: cuda
+Num gpus: 1
+
+Directory responsibilities:
+- Write exported model files to: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+- Write runtime artifacts such as quant_summary.json, quantize.py, logs, prompts, copied request/session files, and the venv to: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16
+
+CRITICAL SCRIPT REQUIREMENT:
+- Before starting quantization, you MUST first generate the quantization script file:
+    /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quantize.py
+- The file name must be exactly: quantize.py
+- Run quantization by executing that generated quantize.py script
+- Do not use quantize_script.py as the final artifact name
+
+CRITICAL ENVIRONMENT NOTE:
+- System Python has torch+cuda pre-installed. When creating venvs, ALWAYS use:
+    python3 -m venv --system-site-packages <path>
+  This ensures the venv inherits torch+cuda. Do NOT pip install torch inside the venv.
+- If /root/.venv exists, reuse /root/.venv before creating a new venv.
+- Use uv pip for dependency installation. Prefer:
+    uv pip install --python <venv>/bin/python <packages>
+- Do NOT reinstall torch or flash_attn if they already import successfully from the reused environment. Only install them when missing or incompatible.
+- This workflow is CUDA-focused. For AutoRound device selection:
+    - if Num gpus == 1, prefer device="cuda"
+    - if Num gpus > 1, prefer device_map="auto"
+  Do NOT default to device_map="0" or device_map="0,1,2,3" unless manual mapping is truly required after auto placement fails.
+
+IMPORTANT - After quantization completes (success or failure), you MUST produce:
+
+/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json - structured summary:
+{
+  "model_id": "Qwen/Qwen3-1.7B",
+  "scheme": "W4A16",
+  "method": "RTN",
+  "export_format": "auto_round",
+  "device": "cuda",
+  "quant_num_gpus": "1",
+  "num_gpus": "1",
+  "output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "status": "success" or "failed",
+  "duration_seconds": <float>,
+  "original_size_mb": <float or null>,
+  "quantized_size_mb": <float or null>,
+  "compression_ratio": <float or null>,
+  "errors": [<list of error strings>],
+  "solutions": [<list of solution strings>],
+  "output_files": [<list of file paths in runtime_output_dir>]
+}
+
+Write as valid JSON. If quantization fails, still write quant_summary.json with status=failed.
+
+### [2026-04-24 09:27:51 UTC] ASSISTANT
+
+**Thinking**
+
+Let me start by reading the skill instructions to understand how to properly perform the quantization task.
+
+**Tool call:** `read`
+
+```json
+{
+  "file": "/root/.openclaw/workspace/skills/auto_quant/SKILL.md"
+}
+```
+
+### [2026-04-24 09:27:51 UTC] TOOL RESULT
+**Tool:** `read`
+
+---
+name: auto_quant
+description: Quantize Large Language Models (LLMs) using Intel's Auto-Round library with sign-gradient descent for ultra-low bit widths (2-8 bits). Supports multiple formats (AutoRound, GGUF, GPTQ, AWQ, LLM-Compressor).
+metadata:
+  openclaw:
+    emoji: "⚡"
+    homepage: https://github.com/intel/auto-round
+    skillKey: auto-quant
+    requires:
+      bins: []
+      env: []
+      config: []
+---
+
+# Auto-Round Model Quantization Skill
+
+Use this skill when the user wants to quantize Large Language Models (LLMs) using Intel's Auto-Round library. This skill provides comprehensive guidance including error handling, troubleshooting, and model-specific optimizations.
+
+## Overview
+
+AutoRound is an advanced quantization toolkit for LLMs that achieves high accuracy at ultra-low bit widths (2-4 bits) using **sign-gradient descent**. It supports multiple formats (AutoRound, GGUF, GPTQ, AWQ, LLM-Compressor) and inference backends.
+
+**Key capabilities:**
+- Quantization schemes: W4A16, W8A16, W2A16, W3A16, MXFP4, MXFP8, NVFP4, GGUF:Q4_K_M, etc.
+- Export formats: auto_round, auto_gptq, auto_awq, llm_compressor, gguf
+- Inference backends: Transformers, vLLM, SGLang, IPEX, Marlin, ExLLaMAV2
+
+---
+
+## Input Parameters
+
+| Parameter | Description | Required | Default |
+|-----------|-------------|----------|---------|
+| `model_path` | HuggingFace model ID or local path | Yes | - |
+| `output_dir` | Output directory for quantized model | Yes | - |
+| `quant_type` / `scheme` | Quantization scheme | No | `W4A16` |
+| `iters` | Training iterations (0=RTN) | No | `200` |
+| `nsamples` | Calibration samples | No | `128` |
+| `format` | Export format | No | `auto_round` |
+| `device` / `device_map` | CUDA device selection for quantization | No | Single GPU: `device="cuda"`; Multi-GPU: `device_map="auto"` |
+
+### CUDA Device Rules (CRITICAL)
+
+This workflow is primarily for **CUDA / NVIDIA GPU** quantization.
+
+When generating a quantization script for this repo, follow these rules:
+
+1. **Single GPU CUDA**: use `device="cuda"` in the AutoRound API
+2. **Multi-GPU CUDA**: use `device_map="auto"` in the AutoRound API
+3. **Do not default to** `device_map="0"` or `device_map="0,1,2,3"` in generated scripts
+4. Only use a manual explicit map or comma-separated device list when:
+   - `device_map="auto"` fails
+   - or you are intentionally debugging manual placement
+
+Examples:
+
+```python
+# Single GPU (recommended default)
+ar = AutoRound(..., device="cuda")
+
+# Multi-GPU (recommended default)
+ar = AutoRound(..., device_map="auto")
+```
+
+CLI equivalents:
+
+```bash
+# Single GPU
+CUDA_VISIBLE_DEVICES=0 auto-round --model Qwen/Qwen3-0.6B --scheme W4A16 --device cuda
+
+# Multi-GPU
+CUDA_VISIBLE_DEVICES=0,1,2,3 auto-round --model Qwen/Qwen3-0.6B --scheme W4A16 --device auto
+```
+
+### Quantization Schemes
+
+| Scheme | Description | Bits | Group Size | Notes |
+|--------|-------------|------|------------|-------|
+| `W4A16` / `int4` | INT4 weight, FP16 activation | 4 | 128 | **Recommended** for production |
+| `W8A16` | INT8 weight, FP16 activation | 8 | 128 | High accuracy |
+| `W3A16` | INT3 weight, FP16 activation | 3 | 128 | Experimental |
+| `W2A16` | INT2 weight, FP16 activation | 2 | 128 | Lowest bits, use `auto-round-best` |
+| `MXFP4` | MXFP4 format | 4 | 32 | **Research only, no kernel** |
+| `MXFP8` | MXFP8 format | 8 | 32 | **Research only, no kernel** |
+| `NVFP4` | NVIDIA FP4 | 4 | 16 | Use `llm_compressor` format |
+| `GGUF:Q4_K_M` | GGUF Q4 | 4 | - | For llama.cpp |
+
+### Export Formats
+
+| Format | Schemes Supported | Best For |
+|--------|-------------------|----------|
+| `auto_round` | W4A16, W2A16, W3A16, W8A16, MXFP4, MXFP8, NVFP4 | CPU, NVIDIA GPU, CUDA, HPU |
+| `auto_gptq` | W4A16, W2A16, W3A16, W8A16 | CUDA (symmetric) |
+| `auto_awq` | W4A16 | CUDA (asymmetric) |
+| `llm_compressor` | NVFP4, MXFP4, MXFP8 | vLLM, SGLang |
+| `gguf:q4_k_m` | GGUF:Q*_K, Q*_0, Q*_1 | llama.cpp, CPU |
+
+---
+
+## Step 1: Analyze Model from HuggingFace
+
+**CRITICAL: Always fetch model information before quantization.**
+
+### Fetch Model Card and Config
+
+```bash
+# README (model card) - contains usage instructions, quantization notes
+curl -L https://huggingface.co/{model_id}/resolve/main/README.md -o /tmp/{model_id}_README.md
+
+# config.json - architecture details (model_type, num_layers, hidden_size)
+curl -L https://huggingface.co/{model_id}/resolve/main/config.json -o /tmp/{model_id}_config.json
+
+# tokenizer_config.json - tokenizer type and special tokens
+curl -L https://huggingface.co/{model_id}/resolve/main/tokenizer_config.json -o /tmp/{model_id}_tokenizer.json
+```
+
+Replace `{model_id}` with HuggingFace model ID (e.g., `meta-llama/Llama-3.1-8B-Instruct`).
+
+### What to Look For
+
+1. **Architecture**: Check `config.json` → `model_type`
+   - Common types: `llama`, `qwen`, `mistral`, `gemma`, `falcon`, `deepseek_v2`, `mixtral`
+   
+2. **Quantization notes**: Search README for:
+   - "quantize", "quantization", "AWQ", "GPTQ", "GGUF"
+   - Special requirements or limitations
+   
+3. **Model size**: Estimate VRAM needed (~1.2-1.5x model size in BF16)
+
+4. **Special requirements**:
+   - Token required for gated models (Llama, etc.)
+   - Trust remote code requirements
+   - Special dependencies
+
+---
+
+## Step 2: Set Up Environment
+
+### Step 2.0: Check for Shared Workspace (model_info.json)
+
+**IMPORTANT: Before creating any venv, check if `auto_run` has already set up the environment for this model.**
+
+The `auto_run` skill writes a `model_info.json` file to the shared workspace directory after environment setup. If this file exists, reuse the venv from it instead of creating a new one.
+
+**Also check for a prebuilt system venv first:**
+
+- If `/root/.venv/bin/python` exists, reuse `/root/.venv`
+- Do **not** create a new venv if `/root/.venv` is already suitable
+- Install dependencies with `uv pip`, not plain `pip install`
+- If `torch` or `flash_attn` already import successfully from the reused venv, keep them; do not reinstall them unless they are missing or incompatible
+
+The shared workspace directory is typically the `auto_run` output directory for this model:
+- e.g., `/storage/lkk/inference/Qwen_Qwen3-0.6B/model_info.json`
+- The task prompt may explicitly specify it as `workspace_dir`
+
+```python
+import json
+from pathlib import Path
+
+# Check if model_info.json exists in workspace_dir (passed via task prompt)
+workspace_dir = "{workspace_dir}"   # e.g. /storage/lkk/inference/Qwen_Qwen3-0.6B
+info_path = Path(workspace_dir) / "model_info.json"
+
+if info_path.exists():
+    model_info = json.loads(info_path.read_text())
+    venv_path = model_info["venv_path"]          # e.g. /storage/.../venv
+    venv_py   = f"{venv_path}/bin/python"
+    venv_uv   = f"uv pip --python {venv_py}"
+    print(f"✅ Reusing shared venv from auto_run: {venv_path}")
+    # → Skip Steps 2.1-2.2, go directly to Step 3
+elif Path("/root/.venv/bin/python").exists():
+    venv_path = "/root/.venv"
+    venv_py   = f"{venv_path}/bin/python"
+    venv_uv   = f"uv pip --python {venv_py}"
+    print(f"✅ Reusing system venv: {venv_path}")
+    # → Skip Steps 2.1-2.2, go directly to Step 3
+else:
+    print("ℹ️  No model_info.json found, will create standalone venv in output_dir")
+    venv_path = "{output_dir}/venv"
+    venv_py   = f"{venv_path}/bin/python"
+    venv_uv   = f"uv pip --python {venv_py}"
+    # → Continue with Steps 2.1-2.2 below
+```
+
+### Create Isolated Virtual Environment
+
+**Only run the steps below if model_info.json was NOT found above.**
+
+```bash
+# Create output directory
+mkdir -p {output_dir}
+mkdir -p {output_dir}/logs
+
+# Create virtual environment
+python3 -m venv --system-site-packages {output_dir}/venv
+
+# Bootstrap uv in the venv and use uv pip for package installation
+{output_dir}/venv/bin/python -m pip install -U uv
+uv pip install --python {output_dir}/venv/bin/python -U pip setuptools wheel
+```
+
+### Install Auto-Round
+
+**Option A: From local source (editable - allows source modifications)**
+```bash
+# Copy source if needed
+cp -r /storage/lkk/auto-round {output_dir}/auto-round-src
+
+# Install in editable mode
+uv pip install --python {output_dir}/venv/bin/python -e {output_dir}/auto-round-src
+```
+
+**Option B: From GitHub**
+```bash
+uv pip install --python {output_dir}/venv/bin/python git+https://github.com/intel/auto-round.git
+```
+
+**Option C: From PyPI**
+```bash
+uv pip install --python {output_dir}/venv/bin/python auto-round
+```
+
+### Install Additional Dependencies
+
+```bash
+# Verify inherited CUDA packages first; keep them if they already work
+{output_dir}/venv/bin/python -c "import torch; print('torch ok:', torch.__version__)"
+{output_dir}/venv/bin/python -c "import flash_attn; print('flash_attn ok')" || true
+
+# Install or update non-CUDA packages with uv pip
+uv pip install --python {output_dir}/venv/bin/python transformers accelerate datasets
+
+# For specific formats
+uv pip install --python {output_dir}/venv/bin/python compressed-tensors  # For better compression
+uv pip install --python {output_dir}/venv/bin/python llama-cpp-python   # For GGUF inference
+uv pip install --python {output_dir}/venv/bin/python gptqmodel          # For GPTQ inference
+
+# Only if torch is missing or incompatible, install a matching CUDA wheel
+# uv pip install --python {output_dir}/venv/bin/python --index-url https://download.pytorch.org/whl/cu124 torch
+
+# Only if flash_attn is required and missing, install it explicitly
+# uv pip install --python {output_dir}/venv/bin/python flash-attn --no-build-isolation
+```
+
+---
+
+## Step 3: Generate Quantization Script
+
+### Basic Script Template
+
+```python
+#!/usr/bin/env python3
+"""
+Auto-Round Quantization Script
+Generated by auto_quant skill
+
+Model: {model_path}
+Output: {output_dir}
+Scheme: {scheme}
+Iterations: {iters}
+Samples: {nsamples}
+Format: {format}
+"""
+
+from auto_round import AutoRound
+
+# Configuration
+model_name_or_path = "{model_path}"
+output_dir = "{output_dir}"
+scheme = "{scheme}"  # e.g., "W4A16", "MXFP4", "GGUF:Q4_K_M"
+iters = {iters}      # 0 for RTN mode, 200 for default, 1000 for best
+nsamples = {nsamples}
+format_str = "{format}"  # "auto_round", "llm_compressor", "gguf:q4_k_m"
+num_gpus = 1  # replace with the actual GPU count for this run
+
+# CUDA device selection rule for this repo:
+# - single GPU: device="cuda"
+# - multi-GPU: device_map="auto"
+autoround_device_kwargs = {"device": "cuda"} if num_gpus <= 1 else {"device_map": "auto"}
+
+print(f"Loading model: {{model_name_or_path}}")
+print(f"Scheme: {{scheme}}")
+print(f"Iters: {{iters}}")
+print(f"nsamples: {{nsamples}}")
+print(f"Format: {{format_str}}")
+print(f"Device args: {{autoround_device_kwargs}}")
+
+# Create AutoRound instance
+ar = AutoRound(
+    model_name_or_path,
+    scheme=scheme,
+    iters=iters,
+    nsamples=nsamples,
+    # Optional optimizations
+    # enable_torch_compile=True,  # Faster quantization (PyTorch 2.6+)
+    # low_gpu_mem_usage=True,    # Lower VRAM, ~30% slower
+    # disable_opt_rtn=True,      # For GGUF: use pure RTN
+    **autoround_device_kwargs,
+)
+
+# Quantize and save
+print("Starting quantization...")
+ar.quantize_and_save(output_dir=output_dir, format=format_str)
+
+print(f"Quantization complete! Output: {{output_dir}}")
+```
+
+### Recipe Recommendations
+
+| Recipe | iters | nsamples | seqlen | Accuracy | Speed |
+|--------|-------|----------|--------|----------|-------|
+| `default` | 200 | 128 | 2048 | Good | Baseline |
+| `best` | 1000 | 512 | 2048 | **Best** | 4-5x slower |
+| `light` | 50 | 128 | 2048 | Slight drop | 2-3x faster |
+
+**Recommendation:**
+- **W4A16**: Use default recipe (`iters=200`)
+- **W2A16**: Use best recipe (`iters=1000`, `enable_alg_ext=True`)
+- **GGUF**: Use RTN (`iters=0`)
+
+---
+
+## Step 4: Execute and Handle Errors (CRITICAL!)
+
+When quantization fails, you MUST diagnose and fix. **Do NOT simply report errors without attempting solutions.**
+
+### Error Handling Workflow
+
+```
+ERROR → Analyze → Search → Try Solutions → Verify → Document
+```
+
+### Common Errors and Solutions
+
+#### 1. ImportError / ModuleNotFoundError
+
+**Symptoms:**
+```
+ModuleNotFoundError: No module named 'auto_round'
+ImportError: cannot import name 'AutoRound' from 'auto_round'
+```
+
+**Solutions:**
+```bash
+# Reinstall auto-round
+uv pip install --python {venv}/bin/python --upgrade auto-round
+
+# Or from source
+uv pip install --python {venv}/bin/python -e /path/to/auto-round --force-reinstall
+
+# Check installation
+{venv}/bin/pip show auto-round
+```
+
+#### 2. CUDA Out of Memory (OOM)
+
+**Symptoms:**
+```
+RuntimeError: CUDA out of memory
+torch.OutOfMemoryError: CUDA out of memory: tried to allocate X GiB
+```
+
+**Solutions (try in order):**
+```python
+# Solution A: Reduce memory usage - add to AutoRound initialization
+ar = AutoRound(
+    model_name_or_path,
+    scheme=scheme,
+    iters=iters,
+    nsamples=nsamples,
+    enable_torch_compile=True,    # PyTorch 2.6+ recommended
+    low_gpu_mem_usage=True,       # Offload to CPU, ~20% more time
+    device="cuda",                # Keep single-GPU CUDA explicit
+)
+
+# Solution B: Reduce batch size
+    batch_size=1,
+    gradient_accumulate_steps=8,
+
+# Solution C: Reduce seqlen (may affect accuracy)
+    seqlen=512,
+
+# Solution D: Use RTN mode (fastest, no calibration)
+    iters=0,
+    disable_opt_rtn=True,  # For GGUF format
+
+# Solution E: Use multiple GPUs
+    device_map="auto",           # Recommended multi-GPU default
+```
+
+**CLI alternatives:**
+```bash
+# Use light recipe
+auto-round-light --model ... --scheme W4A16
+
+# Low memory mode
+auto-round --model ... --scheme W4A16 --low_gpu_mem_usage
+
+# Multi-GPU CUDA
+CUDA_VISIBLE_DEVICES=0,1,2,3 auto-round --model ... --scheme W4A16 --device auto
+```
+
+#### 3. Version Conflicts
+
+**Symptoms:**
+```
+ImportError: cannot import name 'xxx' from 'transformers'
+AttributeError: module 'torch' has no attribute 'xxx'
+VersionConflict: transformers x.x.x is incompatible with...
+```
+
+**Solutions:**
+```bash
+# Check current versions
+{venv}/bin/pip show torch transformers accelerate
+
+# Upgrade/downgrade transformers
+uv pip install --python {venv}/bin/python "transformers>=4.35.0"
+uv pip install --python {venv}/bin/python "transformers==4.40.0"
+
+# Upgrade torch only when it is actually missing or incompatible
+uv pip install --python {venv}/bin/python "torch>=2.5.0"
+uv pip install --python {venv}/bin/python --index-url https://download.pytorch.org/whl/cu124 torch
+
+# Install flash-attn only if required by the model/runtime and currently missing
+uv pip install --python {venv}/bin/python flash-attn --no-build-isolation
+
+# Reinstall auto-round dependencies
+uv pip install --python {venv}/bin/python -r /path/to/auto-round/requirements.txt
+```
+
+#### 4. Model Loading Errors
+
+**Symptoms:**
+```
+OSError: Can't load tokenizer for ...
+FileNotFoundError: tokenizer_config.json not found
+ValueError: xxx requires a HuggingFace token
+```
+
+**Solutions:**
+```bash
+# For gated models (Llama, etc.), set token
+import os
+os.environ["HF_TOKEN"] = "your_token_here"
+
+# Or use CLI
+huggingface-cli download meta-llama/Llama-3.1-8B-Instruct --token $HF_TOKEN
+
+# Download model first
+git lfs clone https://huggingface.co/{model_id} /local/path
+
+# Use trust_remote_code for custom models
+ar = AutoRound(
+    model_name_or_path,
+    trust_remote_code=True,
+)
+```
+
+#### 5. Quantization Scheme Errors
+
+**Symptoms:**
+```
+ValueError: Unsupported quantization scheme 'xxx'
+KeyError: scheme 'xxx' not found
+```
+
+**Solutions:**
+```bash
+# Check supported schemes
+auto-round list scheme
+
+# Use correct scheme name (case-sensitive)
+scheme = "W4A16"   # Correct
+scheme = "w4a16"   # May not work
+
+# For GGUF format
+scheme = "GGUF:Q4_K_M"  # Correct format
+```
+
+#### 6. Export Format Errors
+
+**Symptoms:**
+```
+ValueError: Export format 'xxx' not supported
+RuntimeError: Failed to export to gguf format
+```
+
+**Solutions:**
+```python
+# Try different format combinations
+format = "auto_round"                    # Most compatible
+format = "llm_compressor"                # For NVFP4/MXFP4
+format = "gguf:q4_k_m"                   # For GGUF
+format = "auto_gptq,auto_awq,auto_round" # Multiple formats
+
+# For GGUF, use iters=0 (RTN)
+ar = AutoRound(
+    model_name_or_path,
+    scheme="W4A16",
+    iters=0,  # RTN mode
+)
+```
+
+#### 7. GPU Not Found / CUDA Errors
+
+**Symptoms:**
+```
+RuntimeError: CUDA not available
+AssertionError: CUDA device not found
+```
+
+**Solutions:**
+```bash
+# Check CUDA availability
+nvidia-smi
+python -c "import torch; print(torch.cuda.is_available())"
+
+# Check GPU visibility
+echo $CUDA_VISIBLE_DEVICES
+CUDA_VISIBLE_DEVICES=0 python script.py
+CUDA_VISIBLE_DEVICES=0,1 python script.py
+
+# Use CPU instead
+device_map = "cpu"
+```
+
+#### 8. Calibration Dataset Errors
+
+**Symptoms:**
+```
+RuntimeError: Error loading dataset 'xxx'
+DatasetNotFoundError: Couldn't find dataset 'xxx'
+```
+
+**Solutions:**
+```python
+# Use default dataset
+dataset = "NeelNanda/pile-10k"
+
+# Use alternative dataset
+dataset = "swift/pile-val-backup"  # For China region
+dataset = "BAAI/CCI3-HQ"           # Chinese
+dataset = "mbpp"                   # Code
+
+# Use local dataset
+dataset = "/path/to/local_dataset.json"
+
+# Specify dataset split
+dataset = "NeelNanda/pile-10k:train"
+dataset = "NeelNanda/pile-10k:train+validation"
+```
+
+---
+
+## Step 5: Advanced Troubleshooting
+
+### When Standard Solutions Don't Work
+
+#### A. Web Search Strategy
+
+Search for the exact error message:
+```
+# Search patterns
+"auto-round" "CUDA out of memory"
+"auto-round" "ImportError" transformers
+"intel auto-round" github issues
+"auto-round" "ValueError" scheme
+```
+
+#### B. Check GitHub Issues
+
+```bash
+# Search auto-round issues
+curl -s "https://api.github.com/search/issues?q=repo:intel/auto-round+out+of+memory" | jq '.items[:5] | .[] | {title, url}'
+
+# Check recent issues
+curl -s "https://api.github.com/repos/intel/auto-round/issues?state=open" | jq '.[:10] | .[] | {title, number}'
+```
+
+#### C. Source Code Investigation
+
+If error is in auto-round itself:
+```bash
+# Look at auto-round source
+ls /path/to/auto-round/auto_round/
+
+# Check specific module
+cat /path/to/auto-round/auto_round/autoround.py | head -100
+
+# Search for error source
+grep -r "error_message" /path/to/auto-round/auto_round/
+```
+
+#### D. Try Different Approaches
+
+```python
+# Approach 1: Different scheme
+scheme = "W4A16"  # Instead of MXFP4
+
+# Approach 2: Different format
+format = "auto_round"  # Instead of gguf
+
+# Approach 3: Different recipe
+# default → light → best
+
+# Approach 4: Use CLI instead of API
+import subprocess
+subprocess.run([
+    "auto-round",
+    "--model", model_path,
+    "--scheme", "W4A16",
+    "--format", "auto_round",
+    "--output_dir", output_dir,
+])
+```
+
+---
+
+## Step 6: Verify and Save
+
+After successful quantization:
+
+1. **Verify output files:**
+```bash
+ls -la {output_dir}/
+ls -la {output_dir}/quantized_model/  # or output_dir/
+```
+
+2. **Save script:**
+```python
+# Save the quantization script to output directory
+with open(f"{output_dir}/quantize_script.py", "w") as f:
+    f.write(script_content)
+```
+
+3. **Document solutions (if errors occurred):**
+```markdown
+# {output_dir}/solutions.md
+
+## Error 1: [Error Description]
+- **Cause**: [Root cause]
+- **Solution**: [What worked]
+- **Command**: [Command used]
+
+## Error 2: ...
+```
+
+---
+
+## Step 6.5: Generate Summary (RECOMMENDED)
+
+After quantization completes (success or failure), generate a `summary.md` to document the entire process. This helps with debugging, reproducibility, and tracking issues.
+
+### Summary Template
+
+```python
+#!/usr/bin/env python3
+"""
+Generate quantization summary
+Run this after quantization completes (success or failure)
+"""
+
+import json
+import os
+from datetime import datetime
+from pathlib import Path
+
+def generate_summary(
+    output_dir: str,
+    model_path: str,
+    scheme: str,
+    iters: int,
+    nsamples: int,
+    format_str: str,
+    start_time: float,
+    errors: list = None,
+    solutions: list = None,
+    notes: str = None
+):
+    """Generate a comprehensive summary markdown file."""
+    
+    import time
+    end_time = time.time()
+    duration = end_time - start_time
+    
+    # Collect output files
+    output_path = Path(output_dir)
+    files_info = []
+    if output_path.exists():
+        for f in sorted(output_path.rglob("*")):
+            if f.is_file() and not f.name.endswith(('.pyc', '.pyo', '__pycache__')):
+                size = f.stat().st_size
+                size_str = f"{size/1024/1024:.2f} MB" if size > 1024*1024 else f"{size/1024:.2f} KB"
+                files_info.append(f"  - {f.relative_to(output_path)} ({size_str})")
+    
+    # Build summary markdown
+    summary = f"""# Quantization Summary
+
+Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S UTC')}
+
+## Model Information
+
+| Field | Value |
+|-------|-------|
+| Model Path | `{model_path}` |
+| Scheme | `{scheme}` |
+| Iterations | `{iters}` |
+| Calibration Samples | `{nsamples}` |
+| Export Format | `{format_str}` |
+
+## Timing
+
+| Phase | Duration |
+|-------|----------|
+| Total | {duration:.2f} seconds ({duration/60:.2f} minutes) |
+
+## Output Files
+
+```
+{chr(10).join(files_info) if files_info else "  (no files found)"}
+```
+
+## Errors Encountered
+
+{chr(10).join(f"- {err}" for err in (errors or ["(none)"]))}
+
+## Solutions Applied
+
+{chr(10).join(f"- {sol}" for sol in (solutions or ["(none)"]))}
+
+## Additional Notes
+
+{notes or "(none)"}
+
+## Environment
+
+```bash
+# Python version
+python3 --version
+
+# Key packages
+python -m pip show torch transformers auto-round
+```
+
+## Reproduce Command
+
+```bash
+# Recreate this quantization
+auto-round --model {model_path} --scheme "{scheme}" --format {format_str} --output_dir {output_dir} --iters {iters} --nsamples {nsamples}
+```
+"""
+    
+    # Write summary
+    summary_path = Path(output_dir) / "summary.md"
+    with open(summary_path, "w") as f:
+        f.write(summary)
+    
+    print(f"Summary written to: {summary_path}")
+    return summary_path
+
+# Usage example:
+if __name__ == "__main__":
+    import time
+    start_time = time.time()  # Set this at the beginning of quantization
+    
+    # Your quantization code here...
+    
+    # Generate summary at the end
+    generate_summary(
+        output_dir="/storage/quantized/llama-8b-w4a16",
+        model_path="meta-llama/Llama-3.1-8B-Instruct",
+        scheme="W4A16",
+        iters=200,
+        nsamples=128,
+        format_str="auto_round",
+        start_time=start_time,
+        errors=["CUDA OOM - tried to allocate 12GB", "Fixed by enable_torch_compile=True"],
+        solutions=["Added low_gpu_mem_usage=True", "Reduced batch_size to 1"],
+        notes="Model quantized successfully with minor memory optimizations"
+    )
+```
+
+### Integration with Quantization Script
+
+Add summary generation to your quantization script:
+
+```python
+#!/usr/bin/env python3
+import time
+import json
+from pathlib import Path
+
+# Track start time
+start_time = time.time()
+
+# Track errors and solutions
+errors = []
+solutions = []
+
+try:
+    # Your quantization code here
+    ar = AutoRound(...)
+    ar.quantize_and_save(...)
+    
+except Exception as e:
+    errors.append(str(e))
+    
+    # Try to recover
+    try:
+        # Attempted solution 1
+        solutions.append("Attempted solution description")
+    except:
+        pass
+    
+    # Try more solutions...
+    finally:
+        # Always generate summary even if quantization failed
+        generate_summary(
+            output_dir=output_dir,
+            model_path=model_path,
+            scheme=scheme,
+            iters=iters,
+            nsamples=nsamples,
+            format_str=format_str,
+            start_time=start_time,
+            errors=errors,
+            solutions=solutions,
+            notes="Quantization failed, see errors above"
+        )
+        raise
+
+# Success path - generate summary
+generate_summary(
+    output_dir=output_dir,
+    model_path=model_path,
+    scheme=scheme,
+    iters=iters,
+    nsamples=nsamples,
+    format_str=format_str,
+    start_time=start_time,
+    notes="Quantization completed successfully"
+)
+```
+
+### Summary Output Example
+
+The generated `summary.md` will look like:
+
+```markdown
+# Quantization Summary
+
+Generated: 2026-03-20 00:51 UTC
+
+## Model Information
+
+| Field | Value |
+|-------|-------|
+| Model Path | `meta-llama/Llama-3.1-8B-Instruct` |
+| Scheme | `W4A16` |
+| Iterations | `200` |
+| Calibration Samples | `128` |
+| Export Format | `auto_round` |
+
+## Timing
+
+| Phase | Duration |
+|-------|----------|
+| Total | 845.32 seconds (14.09 minutes) |
+
+## Output Files
+
+```
+- quantized_model/adapter_config.json (1.23 KB)
+- quantized_model/adapter_model.safetensors (3.87 GB)
+- quantize_script.py (2.45 KB)
+- summary.md (1.89 KB)
+```
+
+## Errors Encountered
+
+- (none)
+
+## Solutions Applied
+
+- (none)
+
+## Additional Notes
+
+- Model quantized successfully with default settings
+
+## Environment
+
+```bash
+# Python version
+Python 3.10.12
+
+# Key packages
+torch: 2.5.0
+transformers: 4.40.0
+auto-round: 0.2.1
+```
+
+## Reproduce Command
+
+```bash
+auto-round --model meta-llama/Llama-3.1-8B-Instruct --scheme "W4A16" --format auto_round --output_dir /storage/quantized/llama-8b-w4a16 --iters 200 --nsamples 128
+```
+```
+
+---
+
+## Complete Example Workflow
+
+### User Request
+> Quantize meta-llama/Llama-3.1-8B-Instruct to W4A16 format, output to /storage/quantized/llama-8b-w4a16
+
+### Agent Actions
+
+**1. Query HuggingFace:**
+```bash
+curl -L https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/resolve/main/config.json
+curl -L https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct/resolve/main/README.md
+```
+
+**2. Set up environment:**
+```bash
+mkdir -p /storage/quantized/llama-8b-w4a16/logs
+if [ -x /root/.venv/bin/python ]; then
+  VENV_PY=/root/.venv/bin/python
+else
+  python3 -m venv --system-site-packages /storage/quantized/llama-8b-w4a16/venv
+  VENV_PY=/storage/quantized/llama-8b-w4a16/venv/bin/python
+fi
+$VENV_PY -m pip install -U uv
+uv pip install --python "$VENV_PY" -e /storage/lkk/auto-round
+```
+
+**3. Create quantization script:**
+```python
+# /storage/quantized/llama-8b-w4a16/quantize_script.py
+from auto_round import AutoRound
+
+ar = AutoRound(
+    "meta-llama/Llama-3.1-8B-Instruct",
+    scheme="W4A16",
+    iters=200,
+    nsamples=128,
+    device="cuda",
+    enable_torch_compile=True,
+)
+ar.quantize_and_save(output_dir="/storage/quantized/llama-8b-w4a16", format="auto_round")
+```
+
+**Multi-GPU CUDA example:**
+```python
+from auto_round import AutoRound
+
+ar = AutoRound(
+    "meta-llama/Llama-3.1-8B-Instruct",
+    scheme="W4A16",
+    iters=200,
+    nsamples=128,
+    device_map="auto",
+    low_gpu_mem_usage=True,
+    enable_torch_compile=True,
+)
+ar.quantize_and_save(output_dir="/storage/quantized/llama-8b-w4a16", format="auto_round")
+```
+
+**4. Execute:**
+```bash
+cd /storage/quantified/llama-8b-w4a16
+./venv/bin/python quantize_script.py >> logs/quantize.log 2>&1
+```
+
+**5. Handle any errors** using the error handling workflow above
+
+**6. Verify:**
+```bash
+ls /storage/quantized/llama-8b-w4a16/
+```
+
+---
+
+## Reference: Auto-Round CLI
+
+```bash
+# Basic usage
+auto-round --model Qwen/Qwen3-0.6B --scheme "W4A16" --format auto_round
+
+# With options
+auto-round \
+    --model Qwen/Qwen3-0.6B \
+    --scheme "W4A16" \
+    --format "auto_gptq,auto_awq,auto_round" \
+    --output_dir ./output \
+    --iters 200 \
+    --nsamples 128
+
+# List available options
+auto-round --help
+auto-round list scheme
+auto-round list format
+```
+
+---
+
+## Quick Reference Card
+
+| Need | Solution |
+|------|----------|
+| Fast quantization | `iters=0` (RTN mode) |
+| Best accuracy | `iters=1000`, `enable_alg_ext=True` |
+| Low VRAM | `low_gpu_mem_usage=True`, `batch_size=1` |
+| GGUF format | `format="gguf:q4_k_m"`, `iters=0` |
+| Single GPU CUDA | `device="cuda"` |
+| Multiple GPUs | `CUDA_VISIBLE_DEVICES=0,1,2,3` + `device_map="auto"` |
+| 2-bit quantization | Use `W2A16` + best recipe |
+| MXFP4/MXFP8 | Research only, no kernel |
+
+---
+
+## Notes
+
+- **VRAM Requirements**: ~10-20GB for 8B models, ~20-40GB for 70B models
+- **Time**: ~10-20 min for 8B model with default settings
+- **MXFP4/MXFP8**: Research features, no real inference kernels
+- **Gated models**: May require HuggingFace token
+- **Editable install**: Install auto-round in editable mode to modify source if needed
+
+
+### [2026-04-24 09:28:09 UTC] ASSISTANT
+
+**Thinking**
+
+Let me analyze the task:
+
+1. Model: Qwen/Qwen3-1.7B
+2. Quantization: W4A16 / RTN (so iters=0)
+3. Export format: auto_round
+4. Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+5. Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16
+6. Runtime device: cuda
+7. Num gpus: 1
+
+Key things to remember:
+- RTN method means iters=0
+- device="cuda" for single GPU
+- Need to create quantize.py (not quantize_script.py)
+- Need to write quant_summary.json at the end
+
+Let me start by:
+1. Checking if /root/.venv exists and is usable
+2. Creating the output directories
+3. Creating the quantization script
+4. Running the quantization
+5. Generating the summary
+
+First, let me check the environment.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls -la /root/.openclaw/workspace/quantized/ 2>/dev/null; echo \"---\"; ls -la /root/.venv/bin/python 2>/dev/null || echo \"No /root/.venv\"; echo \"---\"; nvidia-smi --query-gpu=name,memory.free,memory.total --format=csv,noheader 2>/dev/null || echo \"No nvidia-smi\""
+}
+```
+
+### [2026-04-24 09:28:09 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+total 4
+drwxr-xr-x 3 root root   26 Apr 24 09:27 .
+drwxr-xr-x 5 root root 4096 Apr 24 09:27 ..
+drwxr-xr-x 3 root root   43 Apr 24 09:27 runs
+---
+lrwxrwxrwx 1 root root 73 Apr 24 05:53 /root/.venv/bin/python -> /root/.local/share/uv/python/cpython-3.12-linux-x86_64-gnu/bin/python3.12
+---
+NVIDIA GeForce RTX 4090, 22683 MiB, 23028 MiB
+
+### [2026-04-24 09:28:13 UTC] ASSISTANT
+
+**Thinking**
+
+Good, so:
+- `/root/.venv` exists and has Python
+- GPU is RTX 4090 with ~22GB free
+- I need to create the output directories and quantization script
+
+Let me create the directories and the quantization script.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "mkdir -p /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16 /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/logs"
+}
+```
+
+### [2026-04-24 09:28:13 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+(no output)
+
+### [2026-04-24 09:28:18 UTC] ASSISTANT
+
+**Thinking**
+
+Good, directories created. Now let me check what's available in the venv and whether auto-round is installed.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "/root/.venv/bin/python -c \"import torch; print('torch:', torch.__version__, 'cuda:', torch.cuda.is_available())\" 2>&1; /root/.venv/bin/pip show auto-round 2>&1 | head -5"
+}
+```
+
+### [2026-04-24 09:28:18 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+Traceback (most recent call last):
+  File "<string>", line 1, in <module>
+ModuleNotFoundError: No module named 'torch'
+sh: 1: /root/.venv/bin/pip: not found
+
+### [2026-04-24 09:28:22 UTC] ASSISTANT
+
+**Thinking**
+
+Torch is not installed in /root/.venv. The instruction says to use `--system-site-packages` when creating venv, but torch isn't installed system-wide either. Let me check what's available.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "python3 -c \"import torch; print('torch:', torch.__version__, 'cuda:', torch.cuda.is_available())\" 2>&1; python3 -c \"import auto_round; print('auto_round ok')\" 2>&1"
+}
+```
+
+### [2026-04-24 09:28:22 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `1`
+
+Traceback (most recent call last):
+  File "<string>", line 1, in <module>
+ModuleNotFoundError: No module named 'torch'
+Traceback (most recent call last):
+  File "<string>", line 1, in <module>
+ModuleNotFoundError: No module named 'auto_round'
+
+(Command exited with code 1)
+
+### [2026-04-24 09:28:29 UTC] ASSISTANT
+
+**Thinking**
+
+torch is not installed in system python either. I need to create a venv with --system-site-packages to inherit whatever pre-installed packages exist, but torch isn't there. Let me check what's available in the system site packages and what's pre-installed.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "python3 --version; which python3; ls /usr/lib/python3* -la 2>/dev/null | head; find /usr/local/lib -name \"site-packages\" -type d 2>/dev/null | head"
+}
+```
+
+### [2026-04-24 09:28:29 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+Python 3.12.13
+/root/.venv/bin/python3
+
+### [2026-04-24 09:28:33 UTC] ASSISTANT
+
+**Thinking**
+
+The default python3 is actually from /root/.venv. Let me check the system python.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls /usr/bin/python* 2>/dev/null; ls /usr/local/bin/python* 2>/dev/null; /usr/bin/python3 --version 2>/dev/null || echo \"no /usr/bin/python3\"; ls /root/.venv/lib/ 2>/dev/null"
+}
+```
+
+### [2026-04-24 09:28:33 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+no /usr/bin/python3
+python3.12
+
+### [2026-04-24 09:28:39 UTC] ASSISTANT
+
+**Thinking**
+
+The system Python is /root/.venv/bin/python3. The user's note says torch+cuda is pre-installed in system Python, but it doesn't seem to be there. Let me create a venv with `--system-site-packages` and install torch there.
+
+Let me create the venv and install the necessary packages.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls /root/.venv/lib/python3.12/site-packages/ | head -30"
+}
+```
+
+### [2026-04-24 09:28:39 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+__pycache__
+_virtualenv.pth
+_virtualenv.py
+_yaml
+annotated_doc
+annotated_doc-0.0.4.dist-info
+annotated_types
+annotated_types-0.7.0.dist-info
+anyio
+anyio-4.13.0.dist-info
+beautifulsoup4-4.14.3.dist-info
+bs4
+certifi
+certifi-2026.4.22.dist-info
+click
+click-8.3.3.dist-info
+cmdop
+cmdop-2026.4.7.2.dist-info
+dotenv
+filelock
+filelock-3.29.0.dist-info
+fsspec
+fsspec-2026.3.0.dist-info
+google
+grpc
+grpcio-1.80.0.dist-info
+h11
+h11-0.16.0.dist-info
+hf_xet
+hf_xet-1.4.3.dist-info
+
+### [2026-04-24 09:28:44 UTC] ASSISTANT
+
+**Thinking**
+
+I see there's no torch in site-packages. But the system Python (python3) is symlinked to /root/.venv/bin/python3 which IS the venv. So `python3 -m venv --system-site-packages` will use the system packages from... somewhere that has torch+cuda.
+
+Let me check what the system site packages contain - where is torch actually installed?
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "# Check where torch is installed system-wide\nfind / -name \"torch\" -type d 2>/dev/null | head -10; find / -name \"auto_round\" -type d 2>/dev/null | head -10",
+  "timeout": 15
+}
+```
+
+### [2026-04-24 09:28:46 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+(no output)
+
+### [2026-04-24 09:28:49 UTC] ASSISTANT
+
+**Thinking**
+
+torch and auto_round are not installed anywhere. I need to create a new venv and install everything. Let me follow the skill instructions.
+
+Let me create a venv with --system-site-packages and then install torch and auto-round.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "python3 -m venv --system-site-packages /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv 2>&1"
+}
+```
+
+### [2026-04-24 09:28:51 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+(no output)
+
+### [2026-04-24 09:28:55 UTC] ASSISTANT
+
+**Thinking**
+
+Let me set up the venv and install dependencies.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\n$VENV/bin/python -m pip install -U uv 2>&1 | tail -3"
+}
+```
+
+### [2026-04-24 09:28:56 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+[notice] A new release of pip is available: 25.0.1 -> 26.0.1
+[notice] To update, run: python -m pip install --upgrade pip
+
+### [2026-04-24 09:28:59 UTC] ASSISTANT
+
+**Thinking**
+
+Let me install torch with CUDA support and auto-round.
+
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nuv pip install --python $VENV/bin/python torch --index-url https://download.pytorch.org/whl/cu124 2>&1 | tail -5",
+  "timeout": 300,
+  "yieldMs": 300000
+}
+```
+
+### [2026-04-24 09:29:26 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
++ setuptools==70.2.0
+ + sympy==1.13.1
+ + torch==2.6.0+cu124
+ + triton==3.2.0
+ + typing-extensions==4.15.0
+
+### [2026-04-24 09:29:31 UTC] ASSISTANT
+
+**Thinking**
+
+Good, torch is installed. Now let me install auto-round.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nuv pip install --python $VENV/bin/python auto-round 2>&1 | tail -10",
+  "timeout": 300,
+  "yieldMs": 300000
+}
+```
+
+### [2026-04-24 09:29:34 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
++ six==1.17.0
+ + threadpoolctl==3.6.0
+ + tokenizers==0.22.2
+ + tqdm==4.67.3
+ + transformers==5.6.2
+ + typer==0.24.2
+ + typing-inspection==0.4.2
+ + urllib3==2.6.3
+ + xxhash==3.6.0
+ + yarl==1.23.0
+
+### [2026-04-24 09:29:38 UTC] ASSISTANT
+
+**Thinking**
+
+Now let me verify torch and auto_round work properly.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\n$VENV/bin/python -c \"import torch; print('torch:', torch.__version__, 'cuda:', torch.cuda.is_available())\" 2>&1\n$VENV/bin/python -c \"import auto_round; print('auto_round ok, version:', auto_round.__version__)\" 2>&1"
+}
+```
+
+### [2026-04-24 09:29:48 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `running`
+
+Command still running (session kind-cloud, pid 542). Use process (list/poll/log/write/kill/clear/remove) for follow-up.
+
+### [2026-04-24 09:29:54 UTC] ASSISTANT
+
+**Thinking**
+
+Let me poll for results.
+
+**Tool call:** `process`
+
+```json
+{
+  "action": "poll",
+  "sessionId": "kind-cloud",
+  "timeout": 30000
+}
+```
+
+### [2026-04-24 09:29:54 UTC] TOOL RESULT
+**Tool:** `process` | **Status:** `completed` | **Exit code:** `0`
+
+torch: 2.6.0+cu124 cuda: True
+auto_round ok, version: 0.12.2
+
+
+Process exited with code 0.
+
+### [2026-04-24 09:29:57 UTC] ASSISTANT
+
+**Thinking**
+
+Both torch and auto_round are installed and working. Now let me install transformers and datasets as well, and then create the quantization script.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "VENV=/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv\nuv pip install --python $VENV/bin/python transformers accelerate datasets 2>&1 | tail -5",
+  "timeout": 120
+}
+```
+
+### [2026-04-24 09:29:58 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+Using Python 3.12.13 environment at: quantized/runs/Qwen_Qwen3-1.7B-W4A16/venv
+Checked 3 packages in 49ms
+
+### [2026-04-24 09:30:30 UTC] ASSISTANT
+
+**Thinking**
+
+Now let me create the quantization script. For RTN, we use `iters=0`. Let me create the quantize.py script.
+
+
+
+Now let me write the quantization script:
+
+
+**Tool call:** `write`
+
+```json
+{
+  "content": "#!/usr/bin/env python3\n\"\"\"\nAuto-Round Quantization Script\nGenerated by auto_quant skill\n\nModel: Qwen/Qwen3-1.7B\nOutput: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\nScheme: W4A16\nIterations: 0 (RTN mode)\nSamples: 128\nFormat: auto_round\n\"\"\"\n\nimport os\nimport sys\nimport time\nimport json\nimport traceback\nfrom pathlib import Path\n\n# Track start time\nstart_time = time.time()\n\n# Output directories\nOUTPUT_DIR = \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16\"\nRUN_DIR = \"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16\"\nVENV_BIN = f\"{RUN_DIR}/venv/bin/python\"\n\n# Track errors and solutions\nerrors = []\nsolutions = []\n\nprint(f\"Starting quantization at {time.strftime('%Y-%m-%d %H:%M:%S')}\")\nprint(f\"Model: Qwen/Qwen3-1.7B\")\nprint(f\"Scheme: W4A16\")\nprint(f\"Method: RTN (iters=0)\")\nprint(f\"Format: auto_round\")\nprint(f\"Device: cuda (single GPU)\")\nprint(f\"Output: {OUTPUT_DIR}\")\n\ntry:\n    from auto_round import AutoRound\n\n    # Configuration\n    model_name_or_path = \"Qwen/Qwen3-1.7B\"\n    scheme = \"W4A16\"\n    iters = 0  # RTN mode\n    nsamples = 128\n    format_str = \"auto_round\"\n\n    print(f\"\\nLoading model: {model_name_or_path}\")\n    print(f\"Scheme: {scheme}\")\n    print(f\"Iters: {iters} (RTN mode)\")\n    print(f\"nsamples: {nsamples}\")\n    print(f\"Format: {format_str}\")\n    print(f\"Device: cuda\")\n\n    # Create AutoRound instance\n    ar = AutoRound(\n        model_name_or_path,\n        scheme=scheme,\n        iters=iters,\n        nsamples=nsamples,\n        device=\"cuda\",\n    )\n\n    # Quantize and save\n    print(\"\\nStarting quantization...\")\n    ar.quantize_and_save(output_dir=OUTPUT_DIR, format=format_str)\n\n    print(f\"\\nQuantization complete! Output: {OUTPUT_DIR}\")\n\nexcept Exception as e:\n    errors.append(str(e))\n    errors.append(traceback.format_exc())\n    print(f\"\\nQuantization failed: {e}\")\n    traceback.print_exc()\n\n    # Try recovery solutions\n    try:\n        solutions.append(\"Attempted to use RTN mode (iters=0) for stable quantization\")\n    except:\n        pass\n\nend_time = time.time()\nduration = end_time - start_time\n\n# Calculate sizes\noriginal_size_mb = None\nquantized_size_mb = None\ncompression_ratio = None\n\nquantized_path = Path(OUTPUT_DIR)\nif quantized_path.exists():\n    total_size = sum(f.stat().st_size for f in quantized_path.rglob(\"*\") if f.is_file())\n    quantized_size_mb = total_size / (1024 * 1024)\n\n# Estimate original model size (Qwen3-1.7B in BF16 ~3.4GB)\noriginal_size_mb = 3400.0  # Approximate BF16 size for 1.7B model in MB\nif quantized_size_mb and original_size_mb:\n    compression_ratio = original_size_mb / quantized_size_mb\n\n# List output files\noutput_files = []\nif quantized_path.exists():\n    for f in sorted(quantized_path.rglob(\"*\")):\n        if f.is_file() and not f.name.endswith(('.pyc', '.pyo', '__pycache__')):\n            size = f.stat().st_size\n            output_files.append(str(f))\n\n# Determine status\nstatus = \"failed\" if errors else \"success\"\n\n# Build summary\nsummary = {\n    \"model_id\": \"Qwen/Qwen3-1.7B\",\n    \"scheme\": \"W4A16\",\n    \"method\": \"RTN\",\n    \"export_format\": \"auto_round\",\n    \"device\": \"cuda\",\n    \"quant_num_gpus\": \"1\",\n    \"num_gpus\": \"1\",\n    \"output_dir\": OUTPUT_DIR,\n    \"runtime_output_dir\": RUN_DIR,\n    \"quantized_model_dir\": OUTPUT_DIR,\n    \"status\": status,\n    \"duration_seconds\": duration,\n    \"original_size_mb\": original_size_mb,\n    \"quantized_size_mb\": quantized_size_mb,\n    \"compression_ratio\": compression_ratio,\n    \"errors\": errors,\n    \"solutions\": solutions,\n    \"output_files\": output_files\n}\n\n# Write summary JSON\nsummary_path = Path(RUN_DIR) / \"quant_summary.json\"\nwith open(summary_path, \"w\") as f:\n    json.dump(summary, f, indent=2)\n\nprint(f\"\\nQuantization {'FAILED' if errors else 'SUCCEEDED'}\")\nprint(f\"Duration: {duration:.2f}s ({duration/60:.2f} min)\")\nprint(f\"Summary: {summary_path}\")\n\nif errors:\n    print(\"\\nErrors:\")\n    for e in errors:\n        print(f\"  - {e}\")\n    sys.exit(1)\nelse:\n    sys.exit(0)",
+  "file": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quantize.py"
+}
+```
+
+### [2026-04-24 09:30:30 UTC] TOOL RESULT
+**Tool:** `write`
+
+Successfully wrote 3954 bytes to /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quantize.py
+
+### [2026-04-24 09:30:35 UTC] ASSISTANT
+
+**Thinking**
+
+Now let me run the quantization script.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "cd /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16 && ./venv/bin/python quantize.py 2>&1",
+  "timeout": 600,
+  "yieldMs": 600000
+}
+```
+
+### [2026-04-24 09:32:06 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+Starting quantization at 2026-04-24 09:30:35
+Model: Qwen/Qwen3-1.7B
+Scheme: W4A16
+Method: RTN (iters=0)
+Format: auto_round
+Device: cuda (single GPU)
+Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+
+Loading model: Qwen/Qwen3-1.7B
+Scheme: W4A16
+Iters: 0 (RTN mode)
+nsamples: 128
+Format: auto_round
+Device: cuda
+404 Client Error. (Request ID: Root=1-69eb383f-3f9c26750c38c2c7775094ec;73c180a5-2b61-451d-b2a9-356cdb139351)
+
+Entry Not Found for url: https://huggingface.co/Qwen/Qwen3-1.7B/resolve/main/model_index.json.
+Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
+[33;1m2026-04-24 09:30:48 WARNING base.py L407: `device` is deprecated, please use `device_map` instead[0m
+[38;20m2026-04-24 09:30:48 INFO base.py L473: `enable_opt_rtn` is turned on, set `--disable_opt_rtn` for higher speed at the cost of accuracy.[0m
+[38;20m2026-04-24 09:30:48 INFO base.py L517: using torch.bfloat16 for quantization tuning[0m
+
+Starting quantization...
+[33;1m2026-04-24 09:30:48 WARNING formats.py L166: some layers are skipped quantization (shape not divisible by 32): [0m
+[33;1m2026-04-24 09:30:48 WARNING modeling_utils.py L4519: `loss_type=None` was set in the config but it is unrecognized. Using the default loss: `ForCausalLMLoss`.[0m
+[38;20m2026-04-24 09:30:48 INFO base.py L1150: start to compute imatrix[0m
+
+
+
+
+
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+
+
+
+
+
+
+
+
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+
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+
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+
+
+
+
+
+
+
+
+
+
+
+
+
+[38;20m2026-04-24 09:32:04 INFO shard_writer.py L250: model has been saved to /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/[0m
+[38;20m2026-04-24 09:32:04 INFO device.py L1692: 'peak_ram': 12.35GB, 'peak_vram': 3.98GB[0m
+
+Quantization complete! Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+
+Quantization SUCCEEDED
+Duration: 89.69s (1.49 min)
+Summary: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json
+
+### [2026-04-24 09:32:11 UTC] ASSISTANT
+
+**Thinking**
+
+The quantization succeeded. Now let me verify the output and check the actual file sizes to get accurate numbers for the summary.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls -la /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/"
+}
+```
+
+### [2026-04-24 09:32:11 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+total 1334316
+drwxr-xr-x 2 root root      4096 Apr 24 09:32 .
+drwxr-xr-x 4 root root        59 Apr 24 09:28 ..
+-rw-r--r-- 1 root root      4168 Apr 24 09:32 chat_template.jinja
+-rw-r--r-- 1 root root      1657 Apr 24 09:32 config.json
+-rw-r--r-- 1 root root       213 Apr 24 09:32 generation_config.json
+-rw-r--r-- 1 root root 732233592 Apr 24 09:32 model-00001-of-00002.safetensors
+-rw-r--r-- 1 root root 622590392 Apr 24 09:32 model-00002-of-00002.safetensors
+-rw-r--r-- 1 root root     57609 Apr 24 09:32 model.safetensors.index.json
+-rw-r--r-- 1 root root       196 Apr 24 09:32 quantization_config.json
+-rw-r--r-- 1 root root  11422749 Apr 24 09:32 tokenizer.json
+-rw-r--r-- 1 root root       694 Apr 24 09:32 tokenizer_config.json
+
+### [2026-04-24 09:32:15 UTC] ASSISTANT
+
+**Thinking**
+
+The model is quantized and saved. Let me now update the quant_summary.json with the correct file list and accurate sizes.
+
+**Tool call:** `read`
+
+```json
+{
+  "file": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/quant_summary.json"
+}
+```
+
+### [2026-04-24 09:32:15 UTC] TOOL RESULT
+**Tool:** `read`
+
+{
+  "model_id": "Qwen/Qwen3-1.7B",
+  "scheme": "W4A16",
+  "method": "RTN",
+  "export_format": "auto_round",
+  "device": "cuda",
+  "quant_num_gpus": "1",
+  "num_gpus": "1",
+  "output_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16",
+  "status": "success",
+  "duration_seconds": 89.69453978538513,
+  "original_size_mb": 3400.0,
+  "quantized_size_mb": 1303.0159664154053,
+  "compression_ratio": 2.609331034794143,
+  "errors": [],
+  "solutions": [],
+  "output_files": [
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/chat_template.jinja",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/config.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/generation_config.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00001-of-00002.safetensors",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model-00002-of-00002.safetensors",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/model.safetensors.index.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/quantization_config.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer.json",
+    "/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/tokenizer_config.json"
+  ]
+}
+
+### [2026-04-24 09:32:24 UTC] ASSISTANT
+
+**Thinking**
+
+The quantization was successful. Let me provide the summary to the user.
+
+Results:
+- Model: Qwen/Qwen3-1.7B
+- Scheme: W4A16
+- Method: RTN (Round-to-Nearest, iters=0)
+- Format: auto_round
+- Status: SUCCESS
+- Duration: ~90 seconds (1.49 min)
+- Original size: ~3400 MB (estimated BF16)
+- Quantized size: ~1303 MB
+- Compression ratio: ~2.61x
+- Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16
+
+
+- Runtime artifacts: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/
+
+
+
+**Quantization completed successfully!**
+
+| Field | Value |
+|-------|-------|
+| Model | Qwen/Qwen3-1.7B |
+| Scheme | W4A16 (INT4 weight / FP16 activation) |
+| Method | RTN (Round-to-Nearest, iters=0) |
+| Format | auto_round |
+| Device | cuda (RTX 4090) |
+| Duration | **89.7s (~1.5 min)** |
+| Original size | ~3,400 MB (BF16 estimate) |
+| Quantized size | **1,303 MB** |
+| Compression | **2.61x** |
+
+**Output files** → `/root/.openclaw/workspace/quantized/Qwen_Qwen3-1.7B-W4A16/`:
+- `model-00001-of-00002.safetensors` (698 MB)
+- `model-00002-of-00002.safetensors` (594 MB)
+- `quantization_config.json`
+- `config.json`, `tokenizer.json`, `chat_template.jinja`, etc.
+
+**Runtime artifacts** → `/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-1.7B-W4A16/`:
+- `quantize.py` — the quantization script
+- `quant_summary.json` — structured summary
+- `venv/` — Python environment with torch 2.6.0+cu124 and auto-round 0.12.2
+
+Peak VRAM usage was ~3.98 GB, well within the RTX 4090's 24 GB.