README.md

---
library_name: transformers
tags:
- code
- coding
- software-development
- programming
- llm
- python
- qwen
- transformers
- peft
- lora
- finetuned
license: apache-2.0
---

# 🤖 MM Coder Agent v1

A professional AI coding assistant model fine-tuned from Qwen2.5-1.5B-Instruct for software development tasks.

## Model Overview

| Property | Value |
|----------|-------|
| **Base Model** | Qwen/Qwen2.5-1.5B-Instruct |
| **Architecture** | LoRA (PEFT Adapter) |
| **Parameters** | 1.5B (base) + 37MB (adapter) |
| **Task** | Code Generation / Software Development |
| **Framework** | Transformers, Safetensors |

## Model Description

MM Coder Agent v1 is a specialized coding assistant built on Qwen2.5-1.5B-Instruct. This model is optimized for:

- **Code Generation** - Generate clean, efficient code in multiple languages
- **Bug Detection** - Identify and fix common programming errors
- **Algorithm Implementation** - Implement sorting, searching, and data structures
- **Code Review** - Assist with code review and best practices

### Architecture Details

```json
{
  "peft_type": "LORA",
  "base_model_name_or_path": "Qwen/Qwen2.5-1.5B-Instruct",
  "r": 16,
  "lora_alpha": 32,
  "lora_dropout": 0.0,
  "task_type": "CAUSAL_LM",
  "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
}
```

## Live Demo

Try the model live at: [mm-coder-v1-space](https://huggingface.co/spaces/amkyawdev/mm-coder-v1-space)

## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig

# Load adapter config
peft_config = PeftConfig.from_pretrained("amkyawdev/mm-coder-agent-v1-combined")

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    peft_config.base_model_name_or_path,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
).eval()

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
    peft_config.base_model_name_or_path,
    trust_remote_code=True
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "amkyawdev/mm-coder-agent-v1-combined")

# Generate code
prompt = "Write a Python function to calculate fibonacci numbers"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Example Outputs

| Prompt | Output |
|--------|--------|
| `python hello world` | `print("Hello, World!")` |
| `reverse string python` | `s[::-1]` |
| `fibonacci function python` | Full fibonacci implementation |
| `bubble sort python` | Bubble sort algorithm |

## Training Data

- **Dataset**: mm-llm-coder-dataset (4M rows)
- **Additional**: mm-llm-coder-agent-dataset (4M rows)
- **Source**: Quality coding prompts and responses

## Use Cases

### Ideal For
- Code completion and generation
- Bug detection and fixing
- Algorithm implementation
- Learning programming concepts
- Quick prototyping

### Not Recommended For
- Production-critical systems without evaluation
- Security-sensitive applications without guardrails
- Tasks beyond software development

## Limitations

- 1.5B parameter model (smaller than GPT-4 class)
- May produce incorrect code - always verify outputs
- Limited context window
- Fine-tuned primarily for English

## License

Apache 2.0

## Citation

```bibtex
@model{amkyawdev/mm-coder-agent-v1-combined,
  title={MM Coder Agent v1},
  author={amkyawdev},
  year={2024},
  url={https://huggingface.co/amkyawdev/mm-coder-agent-v1-combined}
}
```

---

*Built with ❤️ using Transformers and PEFT*