A fine-tuned Mistral-based code generation model specialized for Python development, optimized for generating high-quality Python code completions and implementations.
A specialized 7B parameter code generation model based on Mistral architecture, fine-tuned specifically for Python development tasks. This model leverages the Mamba architecture and has been optimized using Unsloth and TRL (Transformer Reinforcement Learning) techniques.
This skill provides access to the Mamba-Codestral-7B model fine-tuned for Python coding assistance. It excels at:
When a user requests Python code generation or completion assistance:
1. **Understand the Request**: Analyze the user's Python development need - whether it's generating new code, completing existing code, refactoring, or implementing specific functionality.
2. **Load the Model**: Use the HuggingFace Transformers library to load the model:
- Model ID: `Agnuxo/Mamba-Codestral-7B-v0.1-python_coding_assistant_16bit`
- Pipeline: `text-generation`
- This is a 16-bit precision model optimized for efficiency
3. **Prepare the Prompt**: Structure the input prompt clearly:
- For code generation: Provide a clear description of what needs to be implemented
- For code completion: Include the partial code context
- For refactoring: Show the existing code with the improvement goal
4. **Generate Code**: Use the text-generation pipeline with appropriate parameters:
- Set reasonable max_length based on the task complexity
- Use temperature control for creativity vs. precision balance
- Consider top_k/top_p sampling for quality control
5. **Post-Process Output**:
- Extract the generated Python code
- Validate syntax correctness
- Format according to PEP 8 standards
- Add explanatory comments if needed
6. **Present Results**: Show the generated code with:
- Clear code blocks with Python syntax highlighting
- Brief explanation of the implementation approach
- Usage examples if applicable
- Any important caveats or assumptions
**User Request**: "Create a Python function to calculate fibonacci numbers with memoization"
**Agent Response**:
```python
from functools import lru_cache
@lru_cache(maxsize=None)
def fibonacci(n: int) -> int:
"""
Calculate the nth Fibonacci number using memoization.
Args:
n: The position in the Fibonacci sequence (0-indexed)
Returns:
The nth Fibonacci number
Raises:
ValueError: If n is negative
"""
if n < 0:
raise ValueError("n must be non-negative")
if n <= 1:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
print(fibonacci(10)) # Output: 55
```
To use this model, ensure the following dependencies are available:
```bash
pip install transformers torch accelerate safetensors
```
For optimal performance with quantized models:
```bash
pip install bitsandbytes optimum
```
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