Instructions to use MusYW/MNLP_M3_mcqa_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MusYW/MNLP_M3_mcqa_model_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-1.7B-Base") model = PeftModel.from_pretrained(base_model, "MusYW/MNLP_M3_mcqa_model_2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Training in progress, step 500
Browse files- adapter_config.json +3 -3
- adapter_model.safetensors +2 -2
adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "unsloth/Qwen3-
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"rank_pattern": {},
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"target_modules": [
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "unsloth/Qwen3-1.7B-Base",
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"bias": "none",
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"corda_config": null,
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"eva_config": null,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"q_proj"
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],
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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version https://git-lfs.github.com/spec/v1
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oid sha256:bb00a1cd817bf4a59e7d3f553d8bf0a297583f078c2f970fede029a4c8ec60e7
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size 12859984
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