Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- StentorLabs/Stentor-30M
+---
+**Model Description**
+
+Stentor Python 30M is a compact language model specifically fine-tuned for Python code generation and autocompletion tasks. Based on the Stentor-30M architecture, this model contains 30 million parameters and is designed to run efficiently on resource-constrained devices including mobile phones and embedded systems.
+
+**Model Details**
+
+- **Developed by:** Experimental fine-tuning project
+- **Model type:** Causal language model (LlamaForCausalLM)
+- **Language:** Python code, English instructions
+- **Parameters:** 30,419,712
+- **Context length:** 512 tokens
+- **Model size:** 60 MB (FP16), 30 MB (INT8)
+- **License:** Apache 2.0
+
+**Training Data**
+
+The model was fine-tuned on a curated dataset of 872 Python examples, including:
+
+- Basic algorithms (factorial, prime numbers, list operations)
+- Class implementations (Stack, BankAccount, Rectangle, Circle)
+- Recursive functions (quicksort, Fibonacci)
+- String manipulation (palindrome, anagram, vowel counting)
+- MBPP (Mostly Basic Python Problems) dataset tasks
+
+All examples follow a consistent format with "### Task:" instruction and "### Solution:" code block.
+
+**Training Process**
+
+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)
+3. Multiple correction rounds with progressively lower learning rates
+4. Final detoxification training with learning rate 3e-7 to remove undesirable patterns
+
+**Evaluation Results**
+
+The model was evaluated on several test categories:
+
+| Category | Pass Rate | Notes |
+|----------|-----------|-------|
+| Basic functions | 80% | Factorial, prime check, etc. |
+| Classes from training set | 100% | Stack, BankAccount, Rectangle |
+| New complex classes | 33% | Graph, Queue, inheritance |
+| Function signatures (MBPP) | 100% | Correctly generates def statements |
+
+**Capabilities**
+
+- Generates Python functions from natural language descriptions
+- Implements basic algorithms (factorial, prime check, palindrome)
+- Creates class definitions with methods (Stack, BankAccount, Rectangle)
+- Handles recursive functions (quicksort, Fibonacci)
+- Produces syntactically correct function signatures
+
+**Limitations**
+
+- May produce repeated or redundant code after the main solution
+- Struggles with complex data structures (graphs, trees, queues)
+- Does not reliably handle class inheritance patterns
+- Can generate incorrect list indexing operations
+- May continue generating text beyond the intended solution
+- Limited to 512 token context window
+- Not suitable for production use without output post-processing
+
+**Recommended Use Cases**
+
+- Code autocompletion in lightweight IDEs
+- Educational tool for Python beginners
+- Rapid prototyping of simple functions
+- Embedded systems with limited computational resources
+- Offline code assistance on mobile devices
+
+**Not Recommended For**
+
+- Complex algorithm implementation
+- Production code generation without human review
+- Tasks requiring deep contextual understanding
+- Generating large codebases
+- Security-critical applications
+
+**Usage Example**
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_path = "path/to/stentor-python-30m"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForCausalLM.from_pretrained(model_path)
+
+prompt = "### Task: Write a function that checks if a number is even\n\n### Solution:\n"
+inputs = tokenizer(prompt, return_tensors="pt")
+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.