Update README.md

README.md

@@ -1,13 +1,17 @@
 ---
 library_name: transformers
-tags: []
+license: mit
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a saved checkpoint from fine-tuning a meta-llama/Meta-Llama-3.1-8B-Instruct model using supervised fine-tuning for our paper [**"Training a Generally Curious Agent"**](https://arxiv.org/abs/2502.17543). In our work, we introduce PAPRIKA, a finetuning framework for teaching large language models (LLMs) strategic exploration.
 
+# NOTE:
 
+In PAPRIKA, our training process consists of two stages. The first stage is supervised finetuning, and then we run preference finetuning using the RPO objective on top of the checkpoint obtained from supervised finetuning. 
+
+We previously released the final checkpoint (after SFT followed by RPO finetuning). Due to [community request](https://github.com/tajwarfahim/paprika/issues/4), we are also releasing the checkpoint obtained after the first stage of SFT (i.e., the checkpoint before running RPO).
 
 ## Model Details
 
@@ -15,63 +19,17 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+This is the model card of a meta-llama/Meta-Llama-3.1-8B-Instruct model fine-tuned using PAPRIKA.
 
-- **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]
+- **Finetuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct
 
-### Model Sources [optional]
+### Model Sources
 
 <!-- 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **Repository:** [Official Code Release for the paper "Training a Generally Curious Agent"](https://github.com/tajwarfahim/paprika)
+- **Paper:** [Training a Generally Curious Agent](https://arxiv.org/abs/2502.17543)
+- **Project Website:** [Project Website](https://paprika-llm.github.io)
 
 ## Training Details
 
@@ -79,121 +37,44 @@ Use the code below to get started with the model.
 
 <!-- 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]
+Our training dataset for supervised fine-tuning can be found here: [SFT dataset](https://huggingface.co/datasets/ftajwar/paprika_SFT_dataset)
 
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+Similarly, the training dataset for preference fine-tuning can be found here: [Preference learning dataset](https://huggingface.co/datasets/ftajwar/paprika_preference_dataset)
 
-#### Preprocessing [optional]
+### Training Procedure
 
-[More Information Needed]
+The [attached Wandb link](https://wandb.ai/llm_exploration/paprika_more_data?nw=nwusertajwar) shows the training loss per gradient step during both supervised fine-tuning and preference fine-tuning.
 
 
 #### 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]
-
-## 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]
+For supervised fine-tuning, we use the AdamW optimizer with learning rate 1e-6, batch size 32, cosine annealing learning rate decay with warmup ratio 0.04, and we train on a total of 17,181 trajectories.
 
-#### Factors
-
-<!-- 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]
+For preference fine-tuning, we use the RPO objective, AdamW optimizer with learning rate 2e-7, batch size 32, cosine annealing learning rate decay with warmup ratio 0.04, and we train on a total of 5260 (preferred, dispreferred) trajectory pairs.
 
 #### Hardware
 
-[More Information Needed]
+This model has been finetuned using 8 NVIDIA L40S GPUs.
 
-#### Software
 
-[More Information Needed]
-
-## Citation [optional]
+## Citation
 
 <!-- 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]
+```
+@misc{tajwar2025traininggenerallycuriousagent,
+      title={Training a Generally Curious Agent}, 
+      author={Fahim Tajwar and Yiding Jiang and Abitha Thankaraj and Sumaita Sadia Rahman and J Zico Kolter and Jeff Schneider and Ruslan Salakhutdinov},
+      year={2025},
+      eprint={2502.17543},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2502.17543}, 
+}
+```
 
 ## Model Card Contact
 
-[More Information Needed]
+[Fahim Tajwar](mailto:tajwarfahim932@gmail.com)