Update README.md

README.md

@@ -1,58 +1,26 @@
 ---
 base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
-library_name: transformers
-model_name: my-python-coder
+library_name: peft
 tags:
-- generated_from_trainer
-- sft
-- trl
-licence: license
+- code
+- python
+- lora
+- qwen
 ---
 
-# Model Card for my-python-coder
+# my-python-coder
 
-This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+A LoRA fine-tune of Qwen2.5-Coder-1.5B-Instruct specialized for Python code generation.
 
-## Quick start
+## Training Details
+- **Base model:** Qwen/Qwen2.5-Coder-1.5B-Instruct
+- **Dataset:** iamtarun/python_code_instructions_18k_alpaca (first 1,500 examples)
+- **Method:** LoRA (r=16, alpha=32, target_modules=all-linear)
+- **Steps:** 200
+- **Hardware:** Google Colab T4
 
-```python
-from transformers import pipeline
+## Usage
+[insert example code snippet]
 
-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?"
-generator = pipeline("text-generation", model="SathishKumar89/my-python-coder", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
-
- 
-
-
-
-This model was trained with SFT.
-
-### Framework versions
-
-- TRL: 1.13.0
-- Transformers: 5.16.1
-- Pytorch: 2.11.0+cu128
-- Datasets: 4.8.5
-- Tokenizers: 0.23.1
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
-  license = {Apache-2.0},
-  url     = {https://github.com/huggingface/trl},
-  year    = {2020}
-}
-```
+## Limitations
+Trained on a small subset; may not generalize to all Python tasks.