Update README.md

README.md

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-# Model Card for Model ID
+# Model Card for nano-aha-moment-3b
 
 See: https://github.com/McGill-NLP/nano-aha-moment
 
-
-
 ## Model Details
 
 ### Model Description
 
-<!-- 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.
-
-- **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]
+This is a 3B parameter language model trained using reinforcement learning to solve mathematical reasoning tasks, specifically the Countdown game. The model is based on Qwen2.5-3B and has been fine-tuned with GRPO using nanoAhaMoment codebase.
 
-### Model Sources [optional]
+- **Developed by:** McGill-NLP Lab
+- **Model type:** Causal Language Model
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** Qwen/Qwen2.5-3B
 
-<!-- Provide the basic links for the model. -->
+### Model Sources
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/McGill-NLP/nano-aha-moment
+- **Demo:** Available in the repository's checkpoint playground notebook
 
 ## 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]
+The model is designed to solve mathematical reasoning tasks, specifically the Countdown game where it needs to create equations using a set of numbers to reach a target value. The model shows its reasoning process in `<think>` tags and provides the final answer in `<answer>` tags.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+You can interactively test the model's reasoning capabilities using the [checkpoint playground notebook](https://github.com/McGill-NLP/nano-aha-moment/blob/main/notebooks/checkpoint_playground.ipynb) in the repository.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is specifically trained for mathematical reasoning tasks and may not perform well on general language tasks or other domains outside its training scope.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+The model has been trained on a specific mathematical reasoning task and may have limitations in:
+1. General language understanding and generation
+2. Handling complex mathematical problems outside the Countdown game format
+3. Maintaining consistent reasoning across different problem types
 
 ### 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]
+Users should:
+1. Use the model specifically for the Countdown game task it was trained on
+2. Be aware of the model's focus on mathematical reasoning
+3. Consider the model's limitations when applying it to other tasks
 
 ## 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]
+The model was trained on the Countdown-Tasks-3to4 dataset, which contains problem statements for the Countdown game where the goal is to reach a target number using a set of available numbers and basic arithmetic operations.
 
 ### 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]
+#### Preprocessing
 
+The training data was preprocessed to include:
+- System message for reasoning guidance
+- Structured prompt template for the Countdown game
+- Special tags for reasoning steps and answers
 
 #### 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]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+- **Training regime:** bf16 mixed precision
+- **Learning rate:** 1e-6
+- **Batch size:** 64 episodes per iteration
+- **Optimizer:** AdamW
+- **KL coefficient:** 0.001
+- **Temperature:** 1.0
 
-[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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model is based on the Qwen2.5-3B architecture and uses:
+- Flash Attention 2 for efficient attention computation
+- DeepSpeed ZeRO Stage 2 for memory optimization
+- vLLM for efficient inference
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
+- PyTorch 2.5.1
+- Transformers 4.48.3
+- DeepSpeed 0.16.4
+- vLLM 0.7.3
+- Flash Attention 2.7.2
 
-<!-- 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:**
+```bibtex
+@misc{Kazemnejad2025:NanoAhaMoment,
+  author       = {Amirhossein Kazemnejad and Milad Aghajohari and Alessandro Sordoni and Aaron Courville and Siva Reddy},
+  title        = {Nano Aha! Moment: Single File "RL for LLM" Library},
+  year         = {2025},
+  howpublished = {\url{https://github.com/McGill-NLP/nano-aha-moment}},
+  note         = {GitHub repository}
+}
+```
 
-[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]
+## Model Card Authors
 
-[More Information Needed]
+McGill-NLP Lab
 
 ## Model Card Contact
 
-[More Information Needed]
+For questions about this model card, please contact the McGill-NLP Lab.