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README.md

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 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Haoxiang Wang
+- **Model type:** Special Sequence Classifier
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model [optional]:** https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- **Repository:** https://github.com/Haoxiang-Wang/directional-preference-alignment
+- **Paper [optional]:** https://arxiv.org/abs/2402.18571
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
-[More Information Needed]
-
-## 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. -->
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-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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. -->
-
-[More Information Needed]
 
++ System Prompt:
+  + Template: `"You are a helpful, respectful, and honest assistant who always responds to the user in a harmless way. Your response should maximize weighted rating = helpfulness*{weight_helpfulness} + verbosity*{weight_verbosity}"`
+  + Value Choices: `weight_helpfulness` is an integer from 0 to 100 and `(weight_verbosity/100)**2 + (weight_helpfulness/100)**2 == 1`
+    + The maximum `weight_helpfulness` is 100 the lowest suggested value is 71.
+
+We suggest starting with a ratio of `weight_verbosity/weight_helpfulness` first. For instance, considering `weight_verbosity/weight_helpfulness` is equal to `tan(-15°)`
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import numpy as np
+
+# Here we show how to use the DPA model to generate a response to a user prompt.
+device = "cuda:2"
+model = AutoModelForCausalLM.from_pretrained("Haoxiang-Wang/DPA-v1-Mistral-7B", torch_dtype=torch.bfloat16, device_map=device)
+tokenizer = AutoTokenizer.from_pretrained("Haoxiang-Wang/DPA-v1-Mistral-7B")
+degree = -15 # weight_verbosity/weight_helpfulness = tan(-15°)
+rad = np.radians(degree) # convert from degree to radian
+weight_helpfulness = np.round((np.cos(rad) * 100)).astype(int) # compute weight_helpfulness, scale it by 100x, and round it to an integer
+weight_verbosity  = np.round((np.sin(rad) * 100)).astype(int) # compute weight_verbosity, scale it by 100x, and round it to an integer
+## Now (weight_helpfulness/100)**2 + (weight_verbosity/100)**2 ≈ 1 - it is not an exact equivalence due to the round() operations above 
+sys_prompt = f"You are a helpful, respectful, and honest assistant who always responds to the user in a harmless way. Your response should maximize weighted rating = helpfulness*{weight_helpfulness} + verbosity*{weight_verbosity}"
+user_prompt = "Write a summary of Romeo and Juliet."
+messages = [
+        {"role": "system", "content": sys_prompt},
+        {"role": "user", "content": user_prompt},
+    ]
+input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(device)
+output = model.generate(input_ids=input_ids, max_new_tokens=2048,temperature=0.7)
+prompt_len = input_ids.shape[-1]
+generated_response = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
+print(generated_response)
+# 'Romeo and Juliet is a tragic love story written by William Shakespeare, believed to have been written between 1591 and 1595. The play is based on an Italian tale called "The Tragical History of Romeus and Juliet" by Arthur Brooke, which was published in 1562.\n\nThe story revolves around two young star-crossed lovers, Romeo Montague and Juliet Capulet, from rival families in Verona, Italy. Their love is forbidden by their families, who have a long-standing feud. Despite the obstacles, Romeo and Juliet marry in secret and spend a few blissful days together before fate intervenes.\n\nA series of misunderstandings, miscommunications, and tragic events lead to the deaths of both Romeo and Juliet. Romeo believes that Juliet is dead, and in a fit of despair, he takes his own life. Juliet, who is actually still alive, awakens to find Romeo dead and takes her own life in grief.\n\nThe play explores themes of love, hate, fate, and the consequences of actions. It is known for its iconic characters, including the passionate Romeo, the fiery Juliet, and the noble Friar Lawrence, who tries to help the young lovers.\n\nRomeo and Juliet has been adapted into numerous films, stage productions, and other media over the years, and it remains a beloved and tragic tale of forbidden love.'
+```
+## Training
+
+![image/png](https://github.com/Haoxiang-Wang/directional-preference-alignment/raw/main/assets/preference-conflict.jpg)
+
+![image/png](https://github.com/Haoxiang-Wang/directional-preference-alignment/raw/main/assets/algo-illustration.jpg)
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/638fb8cf2380ffd99caf8c2a/IEO1xFOzopiOEWrxlDCem.png)
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## 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]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### 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. -->
+## Citation
 
 **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]
+If you find this work useful to your research, please consider citing our paper
+```
+@article{wang2024arithmetic,
+      title={Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards}, 
+      author={Haoxiang Wang and Yong Lin and Wei Xiong and Rui Yang and Shizhe Diao and Shuang Qiu and Han Zhao and Tong Zhang},
+      year={2024},
+      eprint={2402.18571},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```
+
+## Model Card Authors
+
+Haoxiang Wang
 
 ## Model Card Contact
 
-[More Information Needed]
+hwang264@illinois.edu