Update README.md

README.md

@@ -8,14 +8,84 @@ tags:
 license: apache-2.0
 language:
 - en
+datasets:
+- microsoft/rStar-Coder
 ---
 
-# Uploaded finetuned  model
+# GPT-OSS-Coder-20B
 
-- **Developed by:** yasserrmd
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
+<img src="banner.png" width="800" />
 
-This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+This model is a fine-tuned version of OpenAI's **GPT-OSS-20B**, optimized for code generation tasks. The fine-tuning leveraged the **Unsloth** library to enable efficient low-bit quantized training and inference.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## Model Details
+
+* **Base Model:** [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)
+* **Training Framework:** Hugging Face's TRL library combined with [Unsloth](https://github.com/Unsloth-org/Unsloth) optimizations.
+* **Training Data:** 1 million randomly generated records, trained for 150 steps
+
+## Intended Use
+
+This model is designed to assist with:
+
+* Code generation and completion
+* Programming query answering
+* Code summarization
+
+## About `reasoning_effort`
+
+The `reasoning_effort` parameter influences the model's focus during text generation:
+
+* **`low`**: Produces straightforward, concise answers suitable for simple coding tasks.
+* **`medium`**: Balances speed and detail, suitable for moderate complexity tasks.
+* **`high`**: Encourages detailed and complex reasoning, useful for advanced code generation or explanations.
+
+Adjusting this parameter allows you to control the depth of the model's reasoning process, balancing between performance and response complexity.
+
+## Usage Example
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("yasserrmd/gpt-oss-coder-20b")
+model = AutoModelForCausalLM.from_pretrained("yasserrmd/gpt-oss-coder-20b")
+
+messages = [
+    {"role": "system", "content": "You are a helpful coding assistant."},
+    {"role": "user", "content": "Using Python to connect MySQL and retrieve table 'employee' where empno is 1234."},
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    return_dict=True,
+    reasoning_effort="low",
+).to(model.device)
+
+streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, max_new_tokens=512, streamer=streamer)
+```
+
+## Training Overview
+
+The fine-tuning process adapted GPT-OSS-20B to better assist with coding tasks by fine-tuning on a dataset of 1 million random records. The training utilized **only the Unsloth** library for efficient low-bit quantized fine-tuning.
+
+## Citation
+
+```bibtex
+@misc{yasserrmd2025gptosscoder20b,
+  author = {Yasser RMD},
+  title = {GPT-OSS-Coder-20B},
+  year = {2025},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Model Hub},
+  url = {https://huggingface.co/yasserrmd/gpt-oss-coder-20b}
+}
+```
+
+
+
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)