Text Generation
Transformers
Safetensors
jirack_ternary
ternary
bitnet
1.58bit
llama
ternary-transformer
quantized
70b
jirack
amd-rocm
vram-optimized
Eval Results (legacy)
2-bit
Instructions to use kgrabko/JiRackTernary_70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kgrabko/JiRackTernary_70b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kgrabko/JiRackTernary_70b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kgrabko/JiRackTernary_70b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kgrabko/JiRackTernary_70b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kgrabko/JiRackTernary_70b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kgrabko/JiRackTernary_70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kgrabko/JiRackTernary_70b
- SGLang
How to use kgrabko/JiRackTernary_70b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "kgrabko/JiRackTernary_70b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kgrabko/JiRackTernary_70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "kgrabko/JiRackTernary_70b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kgrabko/JiRackTernary_70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kgrabko/JiRackTernary_70b with Docker Model Runner:
docker model run hf.co/kgrabko/JiRackTernary_70b
Create merge_70b_shards_v2.py
Browse files
prepared_sft_data/merge_70b_shards_v2.py
ADDED
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# ==============================================================================
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# COPYRIGHT (C) 2025-2026 KONSTANTIN VLADIMIROVICH GRABKO. ALL RIGHTS RESERVED.
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# PATENT PENDING | CMS MANHATTAN JIRACK TECHNOLOGY
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# ==============================================================================
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import os
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import torch
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import glob
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from safetensors.torch import load_file, save_file
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from tqdm import tqdm
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# --- ПУТИ ---
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# Твой обученный чекпоинт (результат Full SFT)
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SFT_CHECKPOINT_PATH = "/content/full_checkpoints_70b/jirack_70b_full_step_200.safetensors"
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# Папка с твоими оригинальными 30 шардами
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ORIGINAL_SHARDS_DIR = "/content/JiRack_BitNet_70B_Packed/checkpoints/checkpoint-220000"
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# Куда сохранить результат
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OUTPUT_DIR = "/content/JiRack_70B_SFT_Merged"
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os.makedirs(OUTPUT_DIR, exist_ok=True)
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def merge_shards():
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print(f"🚀 Загрузка SFT чекпоинта: {SFT_CHECKPOINT_PATH}")
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sft_weights = load_file(SFT_CHECKPOINT_PATH, device="cpu")
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# Получаем список всех шардов оригинальной модели
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shard_files = sorted(glob.glob(f"{ORIGINAL_SHARDS_DIR}/*.safetensors"))
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print(f"📦 Найдено шардов для обработки: {len(shard_files)}")
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for shard_path in tqdm(shard_files, desc="Merging Shards"):
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shard_name = os.path.basename(shard_path)
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# Загружаем оригинальный шард
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current_shard = load_file(shard_path, device="cpu")
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updated_shard = {}
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merge_count = 0
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for key, weight in current_shard.items():
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# Убираем префикс 'model.', если он есть в ключах чекпоинта, но нет в шардах (или наоборот)
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# Мы ищем точное совпадение ключа в sft_weights
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# Проверяем ключ как есть
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if key in sft_weights:
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updated_shard[key] = sft_weights[key]
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merge_count += 1
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# Проверяем с учетом возможной разницы в префиксах (model.layers... vs layers...)
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elif key.replace("model.", "") in sft_weights:
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updated_shard[key] = sft_weights[key.replace("model.", "")]
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merge_count += 1
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else:
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# Если веса не обучались (не попали в SFT чекпоинт), оставляем оригинал
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updated_shard[key] = weight
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# Сохраняем обновленный шард
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save_path = os.path.join(OUTPUT_DIR, shard_name)
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save_file(updated_shard, save_path)
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# print(f"✅ {shard_name}: обновлено {merge_count} тензоров")
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print(f"\n✨ Мердж завершен! Готовая модель здесь: {OUTPUT_DIR}")
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if __name__ == "__main__":
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merge_shards()
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