Commit from Azure DevOps update Results

Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/results_2026-04-24-10-48-08.json

@@ -0,0 +1,83 @@
+{
+  "pipeline": "auto_quant",
+  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+  "artifact_name": "Qwen3-4B-Instruct-2507-autoround-W4A16",
+  "generated_at": "2026-04-24T10:48:08Z",
+  "source_runtime_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "source_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "run_dir": "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08",
+  "quant_summary": {
+    "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+    "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_Qwen3-4B-Instruct-2507-W4A16",
+    "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+    "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+    "status": "success",
+    "duration_seconds": 127.88244771957397,
+    "original_size_mb": 7687.490051269531,
+    "quantized_size_mb": 2553.4904956817627,
+    "compression_ratio": 0.3321617951570647,
+    "errors": [],
+    "solutions": [],
+    "output_files": [
+      "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/logs/auto.log",
+      "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/logs/quant_prompt.txt",
+      "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quantize.py",
+      "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/request.json"
+    ],
+    "hf_repo": "https://huggingface.co/lvkaokao/Qwen3-4B-Instruct-2507-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": 97.51,
+    "upload_time": "2026-04-24T10:48:07Z"
+  },
+  "accuracy": {
+    "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+    "model_path": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+    "scheme": "W4A16",
+    "device": "cuda:0",
+    "num_gpus": "1",
+    "tasks": {
+      "piqa": {
+        "accuracy": 1.0,
+        "accuracy_stderr": 0.0
+      },
+      "hellaswag": {
+        "accuracy": 0.628361,
+        "accuracy_stderr": 0.0
+      },
+      "gsm8k": {
+        "accuracy": 0.861259,
+        "accuracy_stderr": 0.0
+      },
+      "mmlu": {
+        "accuracy": 0.715,
+        "accuracy_stderr": 0.0
+      }
+    },
+    "status": "success",
+    "duration_seconds": 308.13,
+    "eval_framework": "custom_vllm",
+    "errors": [],
+    "eval_num_gpus": "1"
+  },
+  "copied_files": [
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/quant_summary.json",
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/accuracy.json",
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/quantize.py",
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/logs",
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/session_eval_270.jsonl",
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/session_quant_270.jsonl",
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/session_eval_270.md",
+    "results/Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/session_quant_270.md"
+  ],
+  "quant_num_gpus": "1",
+  "eval_num_gpus": "1"
+}

Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/accuracy.json

@@ -0,0 +1,30 @@
+{
+  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+  "model_path": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "scheme": "W4A16",
+  "device": "cuda:0",
+  "num_gpus": "1",
+  "tasks": {
+    "piqa": {
+      "accuracy": 1.0,
+      "accuracy_stderr": 0.0
+    },
+    "hellaswag": {
+      "accuracy": 0.628361,
+      "accuracy_stderr": 0.0
+    },
+    "gsm8k": {
+      "accuracy": 0.861259,
+      "accuracy_stderr": 0.0
+    },
+    "mmlu": {
+      "accuracy": 0.715,
+      "accuracy_stderr": 0.0
+    }
+  },
+  "status": "success",
+  "duration_seconds": 308.13,
+  "eval_framework": "custom_vllm",
+  "errors": [],
+  "eval_num_gpus": "1"
+}

Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/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_Qwen3-4B-Instruct-2507-W4A16
+Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-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_Qwen3-4B-Instruct-2507-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_Qwen3-4B-Instruct-2507-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_Qwen3-4B-Instruct-2507-W4A16
+
+IMPORTANT - After evaluation completes, you MUST produce:
+
+/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/accuracy.json - evaluation results:
+{
+  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+  "model_path": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-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/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/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/Qwen3-4B-Instruct-2507
+Quantization: W4A16 / RTN
+Export format: auto_round
+Quantized Model Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16
+Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16
+Runtime device: cuda
+Num gpus: 1
+
+Directory responsibilities:
+- Write exported model files to: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-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_Qwen3-4B-Instruct-2507-W4A16
+
+CRITICAL SCRIPT REQUIREMENT:
+- Before starting quantization, you MUST first generate the quantization script file:
+    /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-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_Qwen3-4B-Instruct-2507-W4A16/quant_summary.json - structured summary:
+{
+  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+  "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_Qwen3-4B-Instruct-2507-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-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/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/quant_summary.json

@@ -0,0 +1,32 @@
+{
+  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+  "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_Qwen3-4B-Instruct-2507-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "status": "success",
+  "duration_seconds": 127.88244771957397,
+  "original_size_mb": 7687.490051269531,
+  "quantized_size_mb": 2553.4904956817627,
+  "compression_ratio": 0.3321617951570647,
+  "errors": [],
+  "solutions": [],
+  "output_files": [
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/logs/auto.log",
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/logs/quant_prompt.txt",
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quantize.py",
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/request.json"
+  ],
+  "hf_repo": "https://huggingface.co/lvkaokao/Qwen3-4B-Instruct-2507-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": 97.51,
+  "upload_time": "2026-04-24T10:48:07Z"
+}

Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/quantize.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+"""
+Auto-Round Quantization Script
+Generated by auto_quant skill
+
+Model: Qwen/Qwen3-4B-Instruct-2507
+Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16
+Scheme: W4A16
+Iterations: 0 (RTN mode)
+Samples: 128
+Format: auto_round
+"""
+
+import time
+import json
+import os
+from pathlib import Path
+
+start_time = time.time()
+errors = []
+solutions = []
+
+VENV_PY = "/root/.venv/bin/python"
+OUTPUT_DIR = "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16"
+MODEL_OUTPUT_DIR = "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16"
+
+# Ensure output dirs exist
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+os.makedirs(MODEL_OUTPUT_DIR, exist_ok=True)
+
+print(f"Loading auto_round...")
+from auto_round import AutoRound
+
+# Configuration
+model_name_or_path = "Qwen/Qwen3-4B-Instruct-2507"
+scheme = "W4A16"
+iters = 0      # RTN mode
+nsamples = 128
+format_str = "auto_round"
+num_gpus = 1  # 1 GPU → device="cuda"
+
+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}")
+
+try:
+    # Create AutoRound instance
+    ar = AutoRound(
+        model_name_or_path,
+        scheme=scheme,
+        iters=iters,
+        nsamples=nsamples,
+        **autoround_device_kwargs,
+    )
+
+    # Quantize and save
+    print("Starting quantization...")
+    ar.quantize_and_save(output_dir=MODEL_OUTPUT_DIR, format=format_str)
+
+    print(f"Quantization complete! Output: {MODEL_OUTPUT_DIR}")
+
+except Exception as e:
+    errors.append(str(e))
+    import traceback
+    errors.append(traceback.format_exc())
+    print(f"ERROR: {e}")
+    print(traceback.format_exc())
+
+    # Try RTN fallback
+    try:
+        solutions.append("Attempted RTN fallback with disable_opt_rtn=True")
+        print("Trying RTN fallback...")
+        ar2 = AutoRound(
+            model_name_or_path,
+            scheme=scheme,
+            iters=0,
+            nsamples=nsamples,
+            disable_opt_rtn=True,
+            **autoround_device_kwargs,
+        )
+        ar2.quantize_and_save(output_dir=MODEL_OUTPUT_DIR, format=format_str)
+        print(f"RTN fallback succeeded! Output: {MODEL_OUTPUT_DIR}")
+    except Exception as e2:
+        errors.append(f"Fallback also failed: {e2}")
+        import traceback
+        errors.append(traceback.format_exc())
+        print(f"Fallback ERROR: {e2}")
+
+# Generate quant_summary.json
+end_time = time.time()
+duration = end_time - start_time
+
+# Get file sizes
+original_size_mb = None
+quantized_size_mb = None
+compression_ratio = None
+
+try:
+    import subprocess
+    # Get original model size (downloaded HF cache)
+    result = subprocess.run(
+        [VENV_PY, "-c", 
+         "from huggingface_hub import snapshot_download; "
+         "from pathlib import Path; "
+         "p = Path(snapshot_download('Qwen/Qwen3-4B-Instruct-2507')); "
+         "total = sum(f.stat().st_size for f in p.rglob('*') if f.is_file()); "
+         f"print(total / 1024 / 1024)"],
+        capture_output=True, text=True, timeout=60
+    )
+    if result.returncode == 0 and result.stdout.strip():
+        original_size_mb = float(result.stdout.strip())
+except Exception as e:
+    print(f"Could not get original size: {e}")
+
+try:
+    qpath = Path(MODEL_OUTPUT_DIR)
+    if qpath.exists():
+        total_q = sum(f.stat().st_size for f in qpath.rglob("*") if f.is_file())
+        quantized_size_mb = total_q / 1024 / 1024
+        if original_size_mb and original_size_mb > 0:
+            compression_ratio = quantized_size_mb / original_size_mb
+except Exception as e:
+    print(f"Could not get quantized size: {e}")
+
+output_files = []
+try:
+    for f in sorted(Path(OUTPUT_DIR).rglob("*")):
+        if f.is_file():
+            output_files.append(str(f))
+except:
+    pass
+
+summary = {
+    "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+    "scheme": "W4A16",
+    "method": "RTN",
+    "export_format": "auto_round",
+    "device": "cuda",
+    "quant_num_gpus": "1",
+    "num_gpus": "1",
+    "output_dir": OUTPUT_DIR,
+    "runtime_output_dir": OUTPUT_DIR,
+    "quantized_model_dir": MODEL_OUTPUT_DIR,
+    "status": "failed" if errors else "success",
+    "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
+}
+
+summary_path = Path(OUTPUT_DIR) / "quant_summary.json"
+with open(summary_path, "w") as f:
+    json.dump(summary, f, indent=2)
+
+print(f"\nSummary written to: {summary_path}")
+print(f"Status: {summary['status']}")
+print(f"Duration: {duration:.2f}s")

Qwen/Qwen3-4B-Instruct-2507-autoround-W4A16/run_2026-04-24-10-48-08/session_quant_270.md

@@ -0,0 +1,1791 @@
+# Session: autoeval_quant_270
+
+- **Session ID:** `autoeval_quant_270`
+- **Timestamp:** 2026-04-24 09:43:38 UTC
+- **Working Dir:** `/root/.openclaw/workspace`
+
+## Step 1: Quantization
+
+### [2026-04-24 09:43:38 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/Qwen3-4B-Instruct-2507
+Quantization: W4A16 / RTN
+Export format: auto_round
+Quantized Model Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16
+Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16
+Runtime device: cuda
+Num gpus: 1
+
+Directory responsibilities:
+- Write exported model files to: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-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_Qwen3-4B-Instruct-2507-W4A16
+
+CRITICAL SCRIPT REQUIREMENT:
+- Before starting quantization, you MUST first generate the quantization script file:
+    /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-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_Qwen3-4B-Instruct-2507-W4A16/quant_summary.json - structured summary:
+{
+  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+  "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_Qwen3-4B-Instruct-2507-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-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 09:43:43 UTC] ASSISTANT
+
+**Thinking**
+
+Let me start by reading the skill instructions for auto_quant to understand how to proceed.
+
+**Tool call:** `read`
+
+```json
+{
+  "file": "/root/.openclaw/workspace/skills/auto_quant/SKILL.md"
+}
+```
+
+### [2026-04-24 09:43:43 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 09:43:58 UTC] ASSISTANT
+
+**Thinking**
+
+Now I understand the task. I need to quantize the Qwen/Qwen3-4B-Instruct-2507 model using Intel Auto-Round with:
+- Scheme: W4A16
+- Method: RTN (it=0)
+- Export format: auto_round
+- Output directory: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16
+- Runtime artifact directory: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16
+- Device: cuda
+- Num gpus: 1
+
+Key steps:
+1. First, generate the quantization script at /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quantize.py
+2. Run the quantization
+3. Write quant_summary.json
+
+Let me start by checking if there's a pre-existing venv or model_info.json I should reuse, and also check the directories.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "mkdir -p /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/logs && ls /root/.venv/bin/python 2>/dev/null && ls /root/.openclaw/workspace/quantized/runs/ 2>/dev/null || echo \"no runs dir yet\""
+}
+```
+
+### [2026-04-24 09:43:58 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+/root/.venv/bin/python
+Qwen_Qwen3-4B-Instruct-2507-W4A16
+
+### [2026-04-24 09:44:03 UTC] ASSISTANT
+
+**Thinking**
+
+The system venv exists at `/root/.venv/bin/python`. I'll use that instead of creating a new venv. Let me check if torch works from it.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "/root/.venv/bin/python -c \"import torch; print('torch:', torch.__version__); print('cuda available:', torch.cuda.is_available()); print('gpu:', torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'none')\" 2>&1"
+}
+```
+
+### [2026-04-24 09:44:03 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'
+
+(Command exited with code 1)
+
+### [2026-04-24 09:44:07 UTC] ASSISTANT
+
+**Thinking**
+
+torch is not in /root/.venv. Let me check system Python.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "python3 -c \"import torch; print('torch:', torch.__version__); print('cuda available:', torch.cuda.is_available())\" 2>&1"
+}
+```
+
+### [2026-04-24 09:44:07 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'
+
+(Command exited with code 1)
+
+### [2026-04-24 09:44:11 UTC] ASSISTANT
+
+**Thinking**
+
+The system Python doesn't have torch. The instructions say "System Python has torch+cuda pre-installed" but it doesn't. Let me check what's available and install torch if needed.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "pip3 show torch 2>&1 || pip show torch 2>&1; which pip pip3; ls /usr/local/lib/python*/dist-packages/torch 2>/dev/null || echo \"no system torch\""
+}
+```
+
+### [2026-04-24 09:44:11 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+sh: 1: pip3: not found
+sh: 1: pip: not found
+no system torch
+
+### [2026-04-24 09:44:14 UTC] ASSISTANT
+
+**Thinking**
+
+Hmm, pip is not found. Let me check what's available.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls /usr/lib/python3*/dist-packages/torch 2>/dev/null || echo \"no usr/lib torch\"; ls /usr/local/lib/python3*/site-packages/torch 2>/dev/null || echo \"no site-packages torch\"; python3 --version; which python3; ls /root/.local/lib/ 2>/dev/null || echo \"no .local\""
+}
+```
+
+### [2026-04-24 09:44:14 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+no usr/lib torch
+no site-packages torch
+Python 3.12.13
+/root/.venv/bin/python3
+no .local
+
+### [2026-04-24 09:44:17 UTC] ASSISTANT
+
+**Thinking**
+
+Let me check what packages are in the venv and the system Python path. The skill says "System Python has torch+cuda pre-installed" but I can't find it. Let me check more thoroughly.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls /root/.venv/lib/python3.12/site-packages/ | head -50"
+}
+```
+
+### [2026-04-24 09:44:17 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
+httpcore
+httpcore-1.0.9.dist-info
+httpx
+httpx-0.28.1.dist-info
+huggingface_hub
+huggingface_hub-1.11.0.dist-info
+idna
+idna-3.13.dist-info
+linkify_it
+linkify_it_py-2.1.0.dist-info
+markdown_it
+markdown_it_py-4.0.0.dist-info
+mdit_py_plugins
+mdit_py_plugins-0.5.0.dist-info
+mdurl
+mdurl-0.1.2.dist-info
+openclaw
+openclaw-2026.3.20.dist-info
+packaging
+packaging-26.1.dist-info
+
+### [2026-04-24 09:44:23 UTC] ASSISTANT
+
+**Thinking**
+
+No torch in the venv. I need to install torch. The skill says to use `--system-site-packages` but that doesn't help since torch isn't in system site packages. I'll need to install torch.
+
+The skill says:
+- "Do NOT reinstall torch or flash_attn if they already import successfully from the reused environment. Only install them when missing or incompatible."
+- "Only if torch is missing or incompatible, install a matching CUDA wheel"
+- Use `uv pip install --python <venv>/bin/python --index-url https://download.pytorch.org/whl/cu124 torch`
+
+Let me install torch first.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "/root/.venv/bin/python -m pip install -U uv && uv pip install --python /root/.venv/bin/python --index-url https://download.pytorch.org/whl/cu124 torch 2>&1 | tail -20",
+  "timeout": 300
+}
+```
+
+### [2026-04-24 09:44:23 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `1`
+
+/root/.venv/bin/python: No module named pip
+
+(Command exited with code 1)
+
+### [2026-04-24 09:44:37 UTC] ASSISTANT
+
+**Thinking**
+
+The venv doesn't have pip. The skill says "When creating venvs, ALWAYS use: python3 -m venv --system-site-packages <path>". But the /root/.venv doesn't seem to have torch installed and doesn't have pip.
+
+Let me check if there's a way to bootstrap pip in the venv, or if I should create a fresh venv in the output directory.
+
+Actually, re-reading the skill: "If /root/.venv exists, reuse /root/.venv before creating a new venv."
+
+But /root/.venv doesn't have torch. So I need to either:
+1. Bootstrap pip in /root/.venv and install torch
+2. Create a new venv in the output directory
+
+
+3. Try using ensurepip to bootstrap pip into the existing venv, then install torch from the PyPI index.
+
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "/root/.venv/bin/python -m ensurepip --upgrade 2>&1 | tail -5"
+}
+```
+
+### [2026-04-24 09:44:38 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+Looking in links: /tmp/tmpn_0u4crk
+Processing /tmp/tmpn_0u4crk/pip-25.0.1-py3-none-any.whl
+Installing collected packages: pip
+Successfully installed pip-25.0.1
+
+### [2026-04-24 09:44:41 UTC] ASSISTANT
+
+**Thinking**
+
+Good, now pip is installed. Let me install torch via uv.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "uv pip install --python /root/.venv/bin/python --index-url https://download.pytorch.org/whl/cu124 torch 2>&1 | tail -20",
+  "timeout": 300
+}
+```
+
+### [2026-04-24 09:44:51 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `running`
+
+Command still running (session good-meadow, pid 343). Use process (list/poll/log/write/kill/clear/remove) for follow-up.
+
+### [2026-04-24 09:44:58 UTC] ASSISTANT
+
+**Thinking**
+
+Let me wait for this to complete.
+
+**Tool call:** `process`
+
+```json
+{
+  "action": "poll",
+  "sessionId": "good-meadow",
+  "timeout": 300000
+}
+```
+
+### [2026-04-24 09:45:05 UTC] TOOL RESULT
+**Tool:** `process` | **Status:** `completed` | **Exit code:** `0`
+
++ markupsafe==3.0.3
+ + mpmath==1.3.0
+ + networkx==3.6.1
+ + nvidia-cublas-cu12==12.4.5.8
+ + nvidia-cuda-cupti-cu12==12.4.127
+ + nvidia-cuda-nvrtc-cu12==12.4.127
+ + nvidia-cuda-runtime-cu12==12.4.127
+ + nvidia-cudnn-cu12==9.1.0.70
+ + nvidia-cufft-cu12==11.2.1.3
+ + nvidia-curand-cu12==10.3.5.147
+ + nvidia-cusolver-cu12==11.6.1.9
+ + nvidia-cusparse-cu12==12.3.1.170
+ + nvidia-cusparselt-cu12==0.6.2
+ + nvidia-nccl-cu12==2.21.5
+ + nvidia-nvjitlink-cu12==12.4.127
+ + nvidia-nvtx-cu12==12.4.127
+ + setuptools==70.2.0
+ + sympy==1.13.1
+ + torch==2.6.0+cu124
+ + triton==3.2.0
+
+Process exited with code 0.
+
+### [2026-04-24 09:45:10 UTC] ASSISTANT
+
+**Thinking**
+
+Good, torch is installed. Now let me install auto-round and other dependencies.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "uv pip install --python /root/.venv/bin/python transformers accelerate datasets 2>&1 | tail -10",
+  "timeout": 300
+}
+```
+
+### [2026-04-24 09:45:12 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
++ python-dateutil==2.9.0.post0
+ + regex==2026.4.4
+ + requests==2.33.1
+ + safetensors==0.7.0
+ + six==1.17.0
+ + tokenizers==0.22.2
+ + transformers==5.6.2
+ + urllib3==2.6.3
+ + xxhash==3.6.0
+ + yarl==1.23.0
+
+### [2026-04-24 09:45:16 UTC] ASSISTANT
+
+**Thinking**
+
+Now install auto-round. Let me check if the system has auto-round source available.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls /storage/lkk/ 2>/dev/null || echo \"no /storage/lkk\"; find / -name \"auto-round\" -type d 2>/dev/null | head -5"
+}
+```
+
+### [2026-04-24 09:45:17 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+no /storage/lkk
+
+### [2026-04-24 09:45:21 UTC] ASSISTANT
+
+**Thinking**
+
+No auto-round source available. I'll install from GitHub.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "uv pip install --python /root/.venv/bin/python git+https://github.com/intel/auto-round.git 2>&1 | tail -20",
+  "timeout": 300,
+  "yieldMs": 240000
+}
+```
+
+### [2026-04-24 09:45:25 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+Using Python 3.12.13 environment at: /root/.venv
+   Updating https://github.com/intel/auto-round.git (HEAD)
+    Updated https://github.com/intel/auto-round.git (71f7946cf651f2a4e5e1cbe9625fd2f6a6939c56)
+Resolved 76 packages in 3.45s
+   Building auto-round @ git+https://github.com/intel/auto-round.git@71f7946cf651f2a4e5e1cbe9625fd2f6a6939c56
+      Built auto-round @ git+https://github.com/intel/auto-round.git@71f7946cf651f2a4e5e1cbe9625fd2f6a6939c56
+Prepared 3 packages in 248ms
+Installed 3 packages in 33ms
+ + auto-round==0.13.0.dev359+g71f7946 (from git+https://github.com/intel/auto-round.git@71f7946cf651f2a4e5e1cbe9625fd2f6a6939c56)
+ + py-cpuinfo==9.0.0
+ + threadpoolctl==3.6.0
+
+### [2026-04-24 09:45:56 UTC] ASSISTANT
+
+**Thinking**
+
+Good, auto-round is installed. Now let me create the quantization script. The model is Qwen/Qwen3-4B-Instruct-2507, scheme is W4A16, method is RTN (iters=0), export format is auto_round, device is cuda, num_gpus is 1.
+
+Let me create the quantize.py script and run it.
+
+
+
+Now I'll create the quantization script and run it.
+
+
+**Tool call:** `write`
+
+```json
+{
+  "content": "#!/usr/bin/env python3\n\"\"\"\nAuto-Round Quantization Script\nGenerated by auto_quant skill\n\nModel: Qwen/Qwen3-4B-Instruct-2507\nOutput: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16\nScheme: W4A16\nIterations: 0 (RTN mode)\nSamples: 128\nFormat: auto_round\n\"\"\"\n\nimport time\nimport json\nimport os\nfrom pathlib import Path\n\nstart_time = time.time()\nerrors = []\nsolutions = []\n\nVENV_PY = \"/root/.venv/bin/python\"\nOUTPUT_DIR = \"/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16\"\nMODEL_OUTPUT_DIR = \"/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16\"\n\n# Ensure output dirs exist\nos.makedirs(OUTPUT_DIR, exist_ok=True)\nos.makedirs(MODEL_OUTPUT_DIR, exist_ok=True)\n\nprint(f\"Loading auto_round...\")\nfrom auto_round import AutoRound\n\n# Configuration\nmodel_name_or_path = \"Qwen/Qwen3-4B-Instruct-2507\"\nscheme = \"W4A16\"\niters = 0      # RTN mode\nnsamples = 128\nformat_str = \"auto_round\"\nnum_gpus = 1  # 1 GPU → device=\"cuda\"\n\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\ntry:\n    # Create AutoRound instance\n    ar = AutoRound(\n        model_name_or_path,\n        scheme=scheme,\n        iters=iters,\n        nsamples=nsamples,\n        **autoround_device_kwargs,\n    )\n\n    # Quantize and save\n    print(\"Starting quantization...\")\n    ar.quantize_and_save(output_dir=MODEL_OUTPUT_DIR, format=format_str)\n\n    print(f\"Quantization complete! Output: {MODEL_OUTPUT_DIR}\")\n\nexcept Exception as e:\n    errors.append(str(e))\n    import traceback\n    errors.append(traceback.format_exc())\n    print(f\"ERROR: {e}\")\n    print(traceback.format_exc())\n\n    # Try RTN fallback\n    try:\n        solutions.append(\"Attempted RTN fallback with disable_opt_rtn=True\")\n        print(\"Trying RTN fallback...\")\n        ar2 = AutoRound(\n            model_name_or_path,\n            scheme=scheme,\n            iters=0,\n            nsamples=nsamples,\n            disable_opt_rtn=True,\n            **autoround_device_kwargs,\n        )\n        ar2.quantize_and_save(output_dir=MODEL_OUTPUT_DIR, format=format_str)\n        print(f\"RTN fallback succeeded! Output: {MODEL_OUTPUT_DIR}\")\n    except Exception as e2:\n        errors.append(f\"Fallback also failed: {e2}\")\n        import traceback\n        errors.append(traceback.format_exc())\n        print(f\"Fallback ERROR: {e2}\")\n\n# Generate quant_summary.json\nend_time = time.time()\nduration = end_time - start_time\n\n# Get file sizes\noriginal_size_mb = None\nquantized_size_mb = None\ncompression_ratio = None\n\ntry:\n    import subprocess\n    # Get original model size (downloaded HF cache)\n    result = subprocess.run(\n        [VENV_PY, \"-c\", \n         \"from huggingface_hub import snapshot_download; \"\n         \"from pathlib import Path; \"\n         \"p = Path(snapshot_download('Qwen/Qwen3-4B-Instruct-2507')); \"\n         \"total = sum(f.stat().st_size for f in p.rglob('*') if f.is_file()); \"\n         f\"print(total / 1024 / 1024)\"],\n        capture_output=True, text=True, timeout=60\n    )\n    if result.returncode == 0 and result.stdout.strip():\n        original_size_mb = float(result.stdout.strip())\nexcept Exception as e:\n    print(f\"Could not get original size: {e}\")\n\ntry:\n    qpath = Path(MODEL_OUTPUT_DIR)\n    if qpath.exists():\n        total_q = sum(f.stat().st_size for f in qpath.rglob(\"*\") if f.is_file())\n        quantized_size_mb = total_q / 1024 / 1024\n        if original_size_mb and original_size_mb > 0:\n            compression_ratio = quantized_size_mb / original_size_mb\nexcept Exception as e:\n    print(f\"Could not get quantized size: {e}\")\n\noutput_files = []\ntry:\n    for f in sorted(Path(OUTPUT_DIR).rglob(\"*\")):\n        if f.is_file():\n            output_files.append(str(f))\nexcept:\n    pass\n\nsummary = {\n    \"model_id\": \"Qwen/Qwen3-4B-Instruct-2507\",\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\": OUTPUT_DIR,\n    \"quantized_model_dir\": MODEL_OUTPUT_DIR,\n    \"status\": \"failed\" if errors else \"success\",\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\nsummary_path = Path(OUTPUT_DIR) / \"quant_summary.json\"\nwith open(summary_path, \"w\") as f:\n    json.dump(summary, f, indent=2)\n\nprint(f\"\\nSummary written to: {summary_path}\")\nprint(f\"Status: {summary['status']}\")\nprint(f\"Duration: {duration:.2f}s\")",
+  "file": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quantize.py"
+}
+```
+
+### [2026-04-24 09:45:56 UTC] TOOL RESULT
+**Tool:** `write`
+
+Successfully wrote 4809 bytes to /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quantize.py
+
+### [2026-04-24 09:45:59 UTC] ASSISTANT
+
+**Thinking**
+
+Now let me run the quantization script.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "cd /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16 && /root/.venv/bin/python quantize.py 2>&1",
+  "timeout": 1800,
+  "yieldMs": 600000
+}
+```
+
+### [2026-04-24 09:47:59 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `running`
+
+Command still running (session mellow-trail, pid 538). Use process (list/poll/log/write/kill/clear/remove) for follow-up.
+
+### [2026-04-24 09:48:03 UTC] ASSISTANT
+
+**Thinking**
+
+Let me poll for the result.
+
+**Tool call:** `process`
+
+```json
+{
+  "action": "poll",
+  "sessionId": "mellow-trail",
+  "timeout": 1200000
+}
+```
+
+### [2026-04-24 09:48:09 UTC] TOOL RESULT
+**Tool:** `process` | **Status:** `completed` | **Exit code:** `0`
+
+Loading auto_round...
+Loading model: Qwen/Qwen3-4B-Instruct-2507
+Scheme: W4A16
+Iters: 0
+nsamples: 128
+Format: auto_round
+Device args: {'device': 'cuda'}
+Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
+404 Client Error. (Request ID: Root=1-69eb3bde-0b3356a47bae08ca3d95e28f;dfb1a671-968f-4919-8ecd-8046085c4a8f)
+
+Entry Not Found for url: https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507/resolve/main/model_index.json.
+[33;1m2026-04-24 09:46:21 WARNING base.py L410: `device` is deprecated, please use `device_map` instead[0m
+[38;20m2026-04-24 09:46:21 INFO base.py L476: `enable_opt_rtn` is turned on, set `--disable_opt_rtn` for higher speed at the cost of accuracy.[0m
+[38;20m2026-04-24 09:46:21 INFO base.py L520: using torch.bfloat16 for quantization tuning[0m
+Starting quantization...
+[33;1m2026-04-24 09:46:21 WARNING formats.py L171: some layers are skipped quantization (shape not divisible by 32): [0m
+[33;1m2026-04-24 09:46:21 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 09:46:22 INFO base.py L1177: start to compute imatrix[0m
+[38;20m2026-04-24 09:46:22 INFO calib_dataset.py L912: Preprocessing calibration dataset in a subprocess to avoid memory leaks...[0m
+
+
+
+
+[38;20m2026-04-24 09:46:39 INFO calib_dataset.py L912: Preprocessing calibration dataset in a subprocess to avoid memory leaks...[0m
+
  0%|          | 0/36 [00:00<?, ?it/s]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+[38;20m2026-04-24 09:48:07 INFO shard_writer.py L293: model has been saved to /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16/[0m
+[38;20m2026-04-24 09:48:07 INFO device.py L1699: 'peak_ram': 9.36GB, 'peak_vram': 6.08GB[0m
+Quantization complete! Output: /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16
+
+Summary written to: /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quant_summary.json
+Status: success
+Duration: 127.88s
+
+Process exited with code 0.
+
+### [2026-04-24 09:48:13 UTC] ASSISTANT
+
+**Thinking**
+
+The quantization completed successfully. Let me verify the output files and update the quant_summary.json with the correct file sizes.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls -la /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16/ && echo \"---\" && du -sh /root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16/"
+}
+```
+
+### [2026-04-24 09:48:13 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+total 2614800
+drwxr-xr-x 2 root root       4096 Apr 24 09:48 .
+drwxr-xr-x 4 root root         59 Apr 24 09:45 ..
+-rw-r--r-- 1 root root       2630 Apr 24 09:48 chat_template.jinja
+-rw-r--r-- 1 root root       1834 Apr 24 09:48 config.json
+-rw-r--r-- 1 root root        212 Apr 24 09:48 generation_config.json
+-rw-r--r-- 1 root root 1062346016 Apr 24 09:47 model-00001-of-00003.safetensors
+-rw-r--r-- 1 root root  825359728 Apr 24 09:48 model-00002-of-00003.safetensors
+-rw-r--r-- 1 root root  778320832 Apr 24 09:48 model-00003-of-00003.safetensors
+-rw-r--r-- 1 root root      74057 Apr 24 09:48 model.safetensors.index.json
+-rw-r--r-- 1 root root        196 Apr 24 09:48 quantization_config.json
+-rw-r--r-- 1 root root   11422650 Apr 24 09:48 tokenizer.json
+-rw-r--r-- 1 root root        695 Apr 24 09:48 tokenizer_config.json
+---
+2.5G	/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16/
+
+### [2026-04-24 09:48:17 UTC] ASSISTANT
+
+**Thinking**
+
+The quantization succeeded. The output directory is 2.5GB. Let me also check the runtime output directory and verify the quant_summary.json is correct.
+
+**Tool call:** `exec`
+
+```json
+{
+  "command": "ls -la /root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/"
+}
+```
+
+### [2026-04-24 09:48:17 UTC] TOOL RESULT
+**Tool:** `exec` | **Status:** `completed` | **Exit code:** `0`
+
+total 16
+drwxr-xr-x 3 root root  103 Apr 24 09:48 .
+drwxr-xr-x 3 root root   55 Apr 24 09:43 ..
+drwxr-xr-x 2 root root   58 Apr 24 09:43 logs
+-rw-r--r-- 1 root root 1118 Apr 24 09:48 quant_summary.json
+-rw-r--r-- 1 root root 4811 Apr 24 09:45 quantize.py
+-rw-r--r-- 1 root root 1058 Apr 24 09:43 request.json
+
+### [2026-04-24 09:48:20 UTC] ASSISTANT
+
+**Thinking**
+
+The quantization completed successfully. Let me read and verify the quant_summary.json content.
+
+**Tool call:** `read`
+
+```json
+{
+  "file": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quant_summary.json"
+}
+```
+
+### [2026-04-24 09:48:20 UTC] TOOL RESULT
+**Tool:** `read`
+
+{
+  "model_id": "Qwen/Qwen3-4B-Instruct-2507",
+  "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_Qwen3-4B-Instruct-2507-W4A16",
+  "runtime_output_dir": "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "quantized_model_dir": "/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16",
+  "status": "success",
+  "duration_seconds": 127.88244771957397,
+  "original_size_mb": 7687.490051269531,
+  "quantized_size_mb": 2553.4904956817627,
+  "compression_ratio": 0.3321617951570647,
+  "errors": [],
+  "solutions": [],
+  "output_files": [
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/logs/auto.log",
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/logs/quant_prompt.txt",
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quantize.py",
+    "/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/request.json"
+  ]
+}
+
+### [2026-04-24 09:48:31 UTC] ASSISTANT
+
+**Thinking**
+
+The quantization completed successfully. Let me also copy the quantize.py to the logs directory and check if there's a request.json that needs to be included in output_files. The summary looks good. Let me also copy the quantize.py to the runtime output dir and make sure the output files list is complete.
+
+Wait, looking at the output_files in the summary, it only has files from the runtime_output_dir. The quantized model files are in a separate dir (quantized_model_dir). The summary already has the correct structure.
+
+Let me also add the venv to the runtime output dir as specified. Actually, the skill says "Write runtime artifacts such as quant_summary.json, quantize.py, logs, prompts, copied request/session files, and the venv to" the runtime dir. The venv is shared (/root/.venv) so we shouldn't copy it.
+
+
+Now I need to verify the summary.json was properly written with all the quantized model files and make sure the compressed model size is reflected correctly in the output. The original size in MB checks out at 7687.49 MB, confirming the 404 error during download didn't prevent the quantization from proceeding successfully.
+
+
+
+Quantization completed successfully. Here's a summary:
+
+**Results:**
+- **Status:** ✅ Success
+- **Duration:** ~128 seconds (RTN mode)
+- **Original model:** ~7.7 GB (BF16)
+- **Quantized model:** ~2.5 GB (W4A16)
+- **Compression ratio:** ~33% (2.67x smaller)
+
+**Output files:**
+- Quantized model: `/root/.openclaw/workspace/quantized/Qwen_Qwen3-4B-Instruct-2507-W4A16/` (3x safetensors shards + config)
+- Runtime artifacts: `/root/.openclaw/workspace/quantized/runs/Qwen_Qwen3-4B-Instruct-2507-W4A16/quant_summary.json`
+
+**Key details:**
+- Model: Qwen/Qwen3-4B-Instruct-2507
+- Scheme: W4A16 (INT4 weight, FP16 activation)
+- Method: RTN (Round-to-Nearest, `iters=0`)
+- Format: auto_round
+- Device: cuda (single GPU)
+- Peak VRAM: ~6.08 GB
+- Peak RAM: ~9.36 GB