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
Model save
Browse files
README.md
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library_name: peft
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license: apache-2.0
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base_model: unsloth/Qwen3-
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# MNLP_M3_mcqa_model_2
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This model is a fine-tuned version of [unsloth/Qwen3-
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## Model description
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---
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library_name: peft
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license: apache-2.0
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base_model: unsloth/Qwen3-1.7B-Base
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tags:
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# MNLP_M3_mcqa_model_2
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This model is a fine-tuned version of [unsloth/Qwen3-1.7B-Base](https://huggingface.co/unsloth/Qwen3-1.7B-Base) on an unknown dataset.
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## Model description
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last_checkpoint/README.md
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base_model: unsloth/Qwen3-1.7B-Base
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library_name: peft
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last_checkpoint/adapter_config.json
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"base_model_name_or_path": "unsloth/Qwen3-
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"bias": "none",
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"task_type": "CAUSAL_LM",
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"alpha_pattern": {},
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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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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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last_checkpoint/adapter_model.safetensors
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size 12859984
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