Instructions to use MusYW/MNLP_M3_mcqa_model_teacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MusYW/MNLP_M3_mcqa_model_teacher 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_teacher") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
File size: 1,310 Bytes
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library_name: peft
license: apache-2.0
base_model: unsloth/Qwen3-1.7B-Base
tags:
- unsloth
- generated_from_trainer
model-index:
- name: MNLP_M3_mcqa_model_teacher
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MNLP_M3_mcqa_model_teacher
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.0 |