---
base_model: mistralai/Mistral-7B-v0.1
datasets:
- siqi00/mistral_ultrafeedback_unhelpful_chatprompt_0.7_1.0_50_320
library_name: transformers
license: apache-2.0
tags:
- alignment-handbook
- generated_from_trainer
pipeline_tag: text-generation
model-index:
- name: mistral-feedbuhcp2-dft-lr2e-6-tau1.0-u_init0-s2-e2-gamma0.85
  results: []
---

# Mistral-7B-DFT

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the siqi00/mistral_ultrafeedback_unhelpful_chatprompt_0.7_1.0_50_320 dataset. It was finetuned as part of the paper [Discriminative Finetuning of Generative Large Language Models without Reward Models and Preference Data](https://arxiv.org/abs/2502.18679)

The code is available at https://github.com/PenGuln/DFT.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

### Framework versions

- Transformers 4.45.2
- Pytorch 2.1.2+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1

### Usage Example

The model can be used for text generation tasks. A basic example using the `transformers` library is shown below:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch

model_id = "siqi00/Mistral-7B-DFT"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

prompt = "What is the capital of France?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
generation_config = GenerationConfig(max_new_tokens=20, temperature=0.7)
outputs = model.generate(inputs["input_ids"], generation_config=generation_config)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```

Remember to install the necessary libraries (`pip install transformers`) and adjust parameters like `temperature` and `max_new_tokens` to fine-tune generation.

## Citation

```bibtex
@misc{guo2025discriminativefinetuninggenerativelarge,
      title={Discriminative Finetuning of Generative Large Language Models without Reward Models and Preference Data}, 
      author={Siqi Guo and Ilgee Hong and Vicente Balmaseda and Tuo Zhao and Tianbao Yang},
      year={2025},
      eprint={2502.18679},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2502.18679}, 
}
```