Update README.md

README.md

@@ -1,202 +1,236 @@
 ---
 base_model: google/gemma-3-1b-it
-library_name: peft
+library_name: transformers
+model_name: Sahibsingh12/gemma3-1b-thinking
+tags:
+- generated_from_trainer
+- trl
+- grpo
+- peft
+- adapter
+licence: license
+datasets:
+- openai/gsm8k
 ---
 
-# Model Card for Model ID
+## Model Information
 
-<!-- Provide a quick summary of what the model is/does. -->
+This project uses `Sahibsingh12/gemma3-1b-thinking`, which is a PEFT (Parameter-Efficient Fine-Tuning) adapter for `google/gemma-3-1b-it`. Unlike a full model, this is a lightweight adapter that works alongside the base model, making it easier to distribute and use with limited resources.
 
+The model was trained using [TRL](https://github.com/huggingface/trl) with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
 
+### Training Approach
 
-## Model Details
+This adapter was fine-tuned with Reinforcement Learning to enhance reasoning capabilities:
 
-### Model Description
+- Used reasoning chains from [OpenAI's GSM8K dataset](https://huggingface.co/datasets/openai/gsm8k)
+- Implemented GRPO reward functions
+- Based on [Will Brown's approach](https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb)
+- Training implementation from [Ben Burtenshaw's Colab](https://x.com/ben_burtenshaw/status/1900202583056068663)
 
-<!-- Provide a longer summary of what this model is. -->
+The adapter is available on HuggingFace: [vinhnx90/gemma3-1b-thinking](https://huggingface.co/vinhnx90/gemma3-1b-thinking)
 
+### Training Details
 
+- **Base Model**: google/gemma-3-1b-it
+- **Library**: transformers
+- **Training Method**: GRPO (from DeepSeekMath paper)
+- **PEFT Method**: LoRA (Low-Rank Adaptation)
+- **Framework Versions**:
+  - TRL: 0.16.0.dev0
+  - Transformers: 4.50.0.dev0
+  - PEFT: 0.9.0
+  - Pytorch: 2.5.1+cu124
+  - Datasets: 3.3.2
+  - Tokenizers: 0.21.0
+
+## Requirements
+
+```
+torch
+transformers
+peft
+```
 
-- **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]
+## Installation
 
-### 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.
-
-## 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. -->
-
-#### 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]
-
-## 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. -->
-
-[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. -->
-
-**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]
-### Framework versions
-
-- PEFT 0.14.0
+1. Clone this repository or download the script
+2. Install the required packages:
+
+```bash
+pip install torch transformers peft
+```
+
+## Usage
+
+### Running with PEFT Adapter
+
+Since this is a PEFT adapter, you need to load both the base model and the adapter:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+# Load the base model and tokenizer
+base_model_id = "google/gemma-3-1b-it"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",  # Automatically determine the device
+    torch_dtype="auto"  # Use the appropriate precision
+)
+
+# Load the PEFT adapter
+adapter_model_id = "Sahibsingh12/gemma3-1b-thinking"
+model = PeftModel.from_pretrained(model, adapter_model_id)
+
+# Generate text
+prompt = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    inputs,
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Chat Format Example
+
+For chat-formatted inputs:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Load the base model and tokenizer
+base_model_id = "google/gemma-3-1b-it"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype="auto"
+)
+
+# Load the PEFT adapter
+adapter_model_id = "Sahibsingh12/gemma3-1b-thinking"
+model = PeftModel.from_pretrained(model, adapter_model_id)
+
+# Prepare chat messages
+messages = [
+    {"role": "user", "content": "Calculate the area of a circle with radius 5cm"}
+]
+
+# Format messages for the model
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Generate response
+inputs = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    inputs,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Using the Pipeline API
+
+For a simpler approach (note: this may download the full adapter model):
+
+```python
+from transformers import pipeline
+
+# Initialize the pipeline with the adapter model
+generator = pipeline(
+    "text-generation",
+    model="Sahibsingh12/gemma3-1b-thinking",
+    model_kwargs={"device_map": "auto", "torch_dtype": "auto"}
+)
+
+# Generate text
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+output = generator(
+    [{"role": "user", "content": question}],
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.7,
+    return_full_text=False
+)[0]
+
+print(output["generated_text"])
+```
+
+## Available Command-Line Arguments
+
+If you use the command-line script, the following arguments are available:
+
+| Argument | Description | Default |
+|----------|-------------|---------|
+| `--prompt` | Input text for generation | "If you had a time machine..." |
+| `--base-model` | Hugging Face base model name | "google/gemma-3-1b-it" |
+| `--adapter` | Hugging Face adapter model name | "vinhnx90/gemma3-1b-thinking" |
+| `--device` | Computing device (cpu, cuda, mps, or auto) | "auto" |
+| `--max-tokens` | Maximum number of new tokens to generate | 128 |
+| `--temperature` | Sampling temperature | 0.7 |
+| `--top-p` | Top-p sampling parameter | 0.9 |
+
+## Citations
+
+### Implementation References
+
+- **Will Brown's Approach**: [GitHub Gist](https://gist.github.com/willccbb/4676755236bb08cab5f4e54a0475d6fb)
+- **Ben Burtenshaw's Implementation**: [Twitter/X Post](https://x.com/ben_burtenshaw/status/1900202583056068663)
+
+### GRPO
+```bibtex
+@article{zhihong2024deepseekmath,
+    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
+    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
+    year         = 2024,
+    eprint       = {arXiv:2402.03300},
+}
+```
+
+### TRL
+```bibtex
+@misc{vonwerra2022trl,
+    title        = {{TRL: Transformer Reinforcement Learning}},
+    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+    year         = 2020,
+    journal      = {GitHub repository},
+    publisher    = {GitHub},
+    howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```
+
+### PEFT
+```bibtex
+@misc{peft,
+    title        = {{PEFT: Parameter-Efficient Fine-Tuning of Billion-Scale Models on Low-Resource Hardware}},
+    author       = {Younes Belkada and Thomas Wang and Yasmine Manar and Ajay Brahmakshatriya and Huu Nguyen and Yongwei Zhou and Soumya Batra and Neil Band and Romi Ponciano and Suraj Patil and Colin Raffel and Siddhartha Kamalakara and Enrico Shippole and Vesselin Popov and Lewis Tunstall and Brian Mugo and Patrick von Platen and Clémentine Fourrier and Surya Dantuluri and Luke Vilnis and Adam P. Saxton},
+    year         = 2023,
+    journal      = {GitHub repository},
+    publisher    = {GitHub},
+    howpublished = {\url{https://github.com/huggingface/peft}}
+}
+```
+
+## License
+
+This project is licensed under the same license as the base model.
+
+## Contributing
+
+Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.