Update README.md

README.md

@@ -35,8 +35,8 @@ All examples follow a consistent format with "### Task:" instruction and "### So
 
 The fine-tuning process involved multiple stages:
 
-1. Base model: Stentor-30M pre-trained checkpoint
-2. Initial fine-tuning on 50k examples (checkpoint-1000 selected as best)
+1. Base model: Stentor-30M 
+2. Initial fine-tuning on 50k examples 
 3. Multiple correction rounds with progressively lower learning rates
 4. Final detoxification training with learning rate 3e-7 to remove undesirable patterns
 
@@ -100,30 +100,10 @@ outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.2)
 response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 ```
 
-**Hardware Requirements**
-
-- **Inference:** CPU only (no GPU required)
-- **RAM:** < 100 MB for inference
-- **Storage:** 60 MB (FP16), 30 MB (INT8 quantized)
-
 **Ethical Considerations**
 
 This model is intended for educational and development assistance purposes. Users should verify all generated code before deployment, particularly for security-sensitive applications. The model may occasionally produce incorrect or inefficient code and should not be relied upon as the sole source of truth for programming tasks.
 
-**Citation**
-
-If you use this model in your work, please cite:
-
-```
-@misc{stentor-python-30m-2026,
-  author = {Fine-tuning Experiment},
-  title = {Stentor Python 30M: A Compact Model for Python Code Generation},
-  year = {2026},
-  publisher = {Hugging Face},
-  url = {https://huggingface.co/username/stentor-python-30m}
-}
-```
-
 **Contact**
 
 For questions or feedback about this model, please open an issue on the Hugging Face repository.