Add pipeline tag, link to code and paper (#1)

- Add pipeline tag, link to code and paper (9ed7049e49cccf9a11e9c2c2f9e45e3c0210164b)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,23 +1,23 @@
 ---
+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
-base_model: mistralai/Mistral-7B-v0.1
 tags:
 - alignment-handbook
 - generated_from_trainer
-datasets:
-- siqi00/mistral_ultrafeedback_unhelpful_chatprompt_0.7_1.0_50_320
+pipeline_tag: text-generation
 model-index:
 - name: mistral-feedbuhcp2-dft-lr2e-6-tau1.0-u_init0-s2-e2-gamma0.85
   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. -->
-
 # 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.
+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
 
@@ -42,3 +42,39 @@ The following hyperparameters were used during training:
 - 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}, 
+}
+```