Update README.md

README.md

@@ -3,7 +3,7 @@ library_name: transformers
 tags: []
 ---
 
-# Model Card for Model ID
+# gemma-2-9b-it-SimPO Model Card
 
 SimPO (Simple Preference Optimization) is an offline preference optimization algorithm designed to enhance the training of large language models (LLMs) with preference optimization datasets. SimPO aligns the reward function with the generation likelihood, eliminating the need for a reference model and incorporating a target reward margin to boost performance. Please refer to our [preprint](https://arxiv.org/pdf/2405.14734) and [github repo](https://github.com/princeton-nlp/SimPO) for more details.
 
@@ -12,10 +12,11 @@ SimPO (Simple Preference Optimization) is an offline preference optimization alg
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+We fine-tuned [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on with the SimPO objective. 
+, a preference optimization dataset where the prompts are from [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) 
 
 
-- **Developed by:** Yu Meng, Mengzhou Xia
+- **Developed by:** Yu Meng, Mengzhou Xia, Danqi Chen
 - **Model type:** Causal Language Model
 - **License:** gemma
 - **Finetuned from model:** [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
@@ -30,40 +31,41 @@ SimPO (Simple Preference Optimization) is an offline preference optimization alg
 
 
 ## How to Get Started with the Model
-
-
+```
+import torch
+from transformers import pipeline
+import json
+import warnings
+
+model_id = "princeton-nlp/gemma-2-9b-it-SimPO"
+
+generator = pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device="cuda",
+)
+generator.tokenizer.chat_template = template
+outputs = generator([{"role": "user", "content": "What's the difference between llamas and alpacas?"}], do_sample=False, max_new_tokens=200)
+print(outputs[0]['generated_text'])
+```
 
 ## Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
+We use 
 
 #### Training Hyperparameters
 
 - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times
 
-[More Information Needed]
+Fine-tuning the [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on  takes around 100 mins to finish on 8xH100 GPUs.
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
 ### Testing Data, Factors & Metrics
 
 #### Testing Data
@@ -92,68 +94,48 @@ SimPO (Simple Preference Optimization) is an offline preference optimization alg
 
 
 
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
+The model architecture is based on [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it). We use the SimPO training objective proposed in our [preprint](https://arxiv.org/pdf/2405.14734).
 
 #### Hardware
 
-[More Information Needed]
+We used 8xH100 GPUs for model training.
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+Training was done using the [alignment-handbook](https://github.com/huggingface/alignment-handbook) library.
+
+## Citation
+
+@article{gemma_2024,
+    title={Gemma},
+    url={https://www.kaggle.com/m/3301},
+    DOI={10.34740/KAGGLE/M/3301},
+    publisher={Kaggle},
+    author={Gemma Team},
+    year={2024}
+}
+
+@article{meng2024simpo,
+  title={{SimPO}: Simple preference optimization with a reference-free reward},
+  author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
+  journal={arXiv preprint arXiv:2405.14734},
+  year={2024}
+}
+
+@article{cui2023ultrafeedback,
+  title={{UltraFeedback}: Boosting language models with high-quality feedback},
+  author={Cui, Ganqu and Yuan, Lifan and Ding, Ning and Yao, Guanming and Zhu, Wei and Ni, Yuan and Xie, Guotong and Liu, Zhiyuan and Sun, Maosong},
+  journal={arXiv preprint arXiv:2310.01377},
+  year={2023}
+}
+
+@article{wang2024interpretable,
+  title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts},
+  author={Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong},
+  journal={arXiv preprint arXiv:2406.12845},
+  year={2024}
+}