Instructions to use naver/cocom-v1-128-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver/cocom-v1-128-mistral-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver/cocom-v1-128-mistral-7b", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("naver/cocom-v1-128-mistral-7b", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 669 Bytes
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"_name_or_path": "/scratch/1/user/sclincha/cocom_release/cocom-v1-128-mistral-7b",
"architectures": [
"COCOM"
],
"auto_map": {
"AutoConfig": "modeling_cocom.COCOMConfig",
"AutoModel": "modeling_cocom.COCOM",
"AutoModelForCausalLM": "modeling_cocom.COCOM"
},
"compr_linear_type": "concat",
"compr_model_name": null,
"compr_rate": 128,
"decoder_model_name": "mistralai/Mistral-7B-Instruct-v0.2",
"generation_top_k": 5,
"lora": true,
"lora_r": 16,
"max_new_tokens": 128,
"model_type": "COCOM",
"quantization": "no",
"sep": true,
"torch_dtype": "bfloat16",
"training_form": "both",
"transformers_version": "4.45.2"
}
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