---
pipeline_tag: text-generation
license: apache-2.0
tags:
- code-generation
- myanmar
- burmese
- qwen
- qwen2
- qwen2.5
- qwen2.5-coder
- transformers
- conversational
- text-generation
library_name: transformers
inference:
  parameters:
    max_new_tokens: 512
    temperature: 0.2
    top_p: 0.95
    repetition_penalty: 1.1
model-index:
- name: amk-coder-v2
  results:
  - task:
      type: text-generation
      name: CodeGeneration
    dataset:
      name: HumanEval
      type: openai/openai_humaneval
    metrics:
    - type: pass_at_1
      value: 50
      verified: false
    - type: pass_at_10
      value: 75
      verified: false
  - task:
      type: text-generation
      name: PythonCodeGeneration
    dataset:
      name: MBPP
      type: abdshhayan/MBPP
    metrics:
    - type: pass_at_1
      value: 55
      verified: false
---

# 🤖 amk-coder-v2 — Myanmar Coding Agent

Myanmar Coding Assistant — Fine-tuned from **Qwen2.5-Coder-1.5B** using **LoRA (PEFT)**

![Model](https://img.shields.io/badge/Model-Size-2B-blue)
![License](https://img.shields.io/badge/License-Apache--2.0-green)
![HuggingFace](https://img.shields.io/badge/-HuggingFace-orange)

---

## 📋 Table of Contents

- [Model Overview](#model-overview)
- [Training Details](#training-details)
- [Quick Start](#quick-start)
- [Usage Examples](#usage-examples)
- [API Deployment](#api-deployment)
- [Limitations](#limitations)
- [License](#license)

---

## Model Overview

**amk-coder-v2** is a Myanmar-localized coding assistant fine-tuned from **Qwen2.5-Coder-1.5B** using LoRA (PEFT) technique.

| Attribute | Value |
|---|---|
| **Base Model** | Qwen2.5-Coder-1.5B |
| **Parameters** | 2B (2,000M) |
| **Architecture** | Qwen2ForCausalLM |
| **Training Method** | LoRA (PEFT) fine-tuning |
| **Dataset** | [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset) (4M rows) |
| **Context Length** | 32,768 tokens |
| **Format** | Safetensors (BF16) |
| **License** | Apache-2.0 |
| **Languages** | Burmese + English |

### Features

| Feature | Description |
|---|---|
| 🇲🇲 **Myanmar Support** | Full support for Myanmar Unicode text |
| 💻 **Code Generation** | Python, JavaScript, C++, Java, and more |
| 🐛 **Debugging** | Bug detection and fixes |
| 📖 **Code Explanation** | Line-by-line explanations |

---

## Training Details

| Parameter | Value |
|---|---|
| **Framework** | Transformers + PEFT |
| **Training Method** | LoRA fine-tuning |
| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| **Optimizer** | paged_adamw_8bit |
| **Learning Rate** | 3e-5 |
| **Epochs** | 3 |
| **Batch Size** | 8 |
| **Max Length** | 2048 |
| **Precision** | FP16 mixed |
| **Hardware** | Kaggle Dual T4 GPU |
| **Training Time** | ~3-5 hrs |

### Chat Template (ChatML)

```
<|im_start|>system
You are an expert Myanmar AI coding agent with tool access.<|im_end|>
<|im_start|>user
{Instruction}
Tools available: {Tools}<|im_end|>
<|im_start|>assistant
Thought & Code:
```

---

## Quick Start

### Using Transformers (Python)

```python
# Method 1: Pipeline (Recommended for beginners)
from transformers import pipeline

pipe = pipeline("text-generation", model="amkyawdev/amk-coder-v2")
messages = [
    {"role": "user", "content": "Python function တစ်ခုရေးပါ။ list comprehension နဲ့ sorting လုပ်ပေးပါ။"}
]
result = pipe(messages, max_new_tokens=512, temperature=0.2)
print(result[0]['generated_text'])

# Method 2: Direct Model Loading
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("amkyawdev/amk-coder-v2")
model = AutoModelForCausalLM.from_pretrained(
    "amkyawdev/amk-coder-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function to reverse a string"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=512, temperature=0.2)
response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)
```

### Using vLLM (Production)

```bash
# Install vLLM
pip install vllm

# Start server
vllm serve "amkyawdev/amk-coder-v2" --tensor-parallel-size 1

# API call
curl -X POST "http://localhost:8000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "amkyawdev/amk-coder-v2",
    "messages": [
      {"role": "user", "content": "Hello, write Python code"}
    ],
    "max_tokens": 512,
    "temperature": 0.2
  }'
```

### Using SGLang

```bash
# Install SGLang
pip install sglang

# Start server
python -m sglang.launch_server --model-path "amkyawdev/amk-coder-v2" --port 30000

# API call
curl -X POST "http://localhost:30000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "amkyawdev/amk-coder-v2",
    "messages": [{"role": "user", "content": "Write a hello world in Python"}]
  }'
```

---

## Usage Examples

### 🇲🇲 Myanmar Prompts

```python
messages = [
    {"role": "user", "content": "Python function တစ်ခုရေးပါ။ ဂဏန်းတွေကို sorting လုပ်ပေးပါ။"}
]
# Output: def sort_numbers(numbers): return sorted(numbers)
```

### 🇬🇧 English Prompts

```python
messages = [
    {"role": "user", "content": "Explain this code:\nfor i in range(10):\n    print(i)"}
]
# Output: This is a for loop that prints numbers 0 to 9
```

### 🐛 Debugging

```python
messages = [
    {"role": "user", "content": "Fix this Python code:\nprint('Hello' + 5)"}
]
# Output: TypeError fix suggestion with corrected code
```

---

## API Deployment

### Backend Server

```bash
cd backend
pip install -r requirements.txt
export HF_TOKEN=hf_your_token
uvicorn app.main:app --host 0.0.0.0 --port 8000
```

### Endpoints

| Method | Endpoint | Description |
|---|---|---|
| GET | `/` | Health check |
| GET | `/health` | Service health status |
| POST | `/chat` | Streaming chat (SSE) |
| GET | `/demo` | Demo HTML interface |
| GET | `/models` | Model information |

### Request Format

```bash
# Streaming chat
curl -X POST "http://localhost:8000/chat" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {"role": "user", "content": "Write a Fibonacci function in Python"}
    ],
    "stream": true
  }'
```

### Docker Deployment

```bash
# Using Docker Model Runner
docker model run hf.co/amkyawdev/amk-coder-v2

# Using vLLM Docker
docker run --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 \
  --rm \
  vllm/vllm-openai:latest \
  --model amkyawdev/amk-coder-v2
```

---

## ⚠️ Limitations

1. **Context Length** - Maximum 32,768 tokens
2. **Code Quality** - May generate incorrect code; verify outputs
3. **Myanmar Unicode** - Best results with proper Zawgyi-to-Unicode conversion
4. **Domain Knowledge** - Limited to common programming languages
5. **Safety** - May produce harmful content; use responsible AI practices

---

## 📖 Resources

- [Qwen2.5-Coder Documentation](https://qwenlm.github.io/blog/Qwen2.5-Coder/)
- [Transformers Library](https://huggingface.co/docs/transformers)
- [HuggingFace Hub](https://huggingface.co/amkyawdev/amk-coder-v2)

---

## 🙏 Acknowledgments

- **Alibaba Cloud Qwen Team** - Base model Qwen2.5-Coder
- **HuggingFace** - Model hosting and infrastructure
- **Myanmar Developer Community** - Testing and feedback

---

## 📝 License

Apache License 2.0 - See LICENSE file for details.

---

## 📧 Contact

- **Author**: amkyawdev
- **HuggingFace**: [amkyawdev/amk-coder-v2](https://huggingface.co/amkyawdev/amk-coder-v2)
- **GitHub**: [github.com/amkyawdev](https://github.com/amkyawdev)

---

*Made with ❤️ for Myanmar Developers*