**Model Card: Stentor Python 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.