Instructions to use ranchomacho/finally-mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ranchomacho/finally-mistral with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "ranchomacho/finally-mistral") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"gate_proj",
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"o_proj",
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"q_proj",
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"lm_head"
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"task_type": "CAUSAL_LM"
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}
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"gate_proj",
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"q_proj",
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"lm_head",
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"o_proj",
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"k_proj",
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"up_proj",
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"v_proj"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 609389712
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version https://git-lfs.github.com/spec/v1
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oid sha256:8541a7b8608caf182954bc3727e668da799c86fc744699cdb3b50957b1634e69
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size 609389712
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