Instructions to use amkyawdev/amk-coder-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amkyawdev/amk-coder-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="amkyawdev/amk-coder-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amkyawdev/amk-coder-v2")
model = AutoModelForCausalLM.from_pretrained("amkyawdev/amk-coder-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amkyawdev/amk-coder-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amkyawdev/amk-coder-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkyawdev/amk-coder-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amkyawdev/amk-coder-v2

SGLang

How to use amkyawdev/amk-coder-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "amkyawdev/amk-coder-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkyawdev/amk-coder-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "amkyawdev/amk-coder-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amkyawdev/amk-coder-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amkyawdev/amk-coder-v2 with Docker Model Runner:
```
docker model run hf.co/amkyawdev/amk-coder-v2
```

amk-coder-v2 / README.md

amkyawdev

Enhanced model card with detailed documentation

ae813f9 verified 2 months ago

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	---
	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: Code Generation
	dataset:
	name: HumanEval
	type: openai/openai_humaneval
	metrics:
	- type: pass_at_1
	value: 50.0
	verified: false
	- type: pass_at_10
	value: 75.0
	verified: false
	- task:
	type: text-generation
	name: Python Code Generation
	dataset:
	name: MBPP
	type: abdshhayan/MBPP
	metrics:
	- type: pass_at_1
	value: 55.0
	verified: false
	---

	# 🤖 amk-coder-v2

	Myanmar Coding Assistant - Fine-tuned from Qwen2.5-Coder-1.5B

	[![Model](https://img.shields.io/badge/Model-Size-2B-blue)](https://huggingface.co/amkyawdev/amk-coder-v2)
	[![License](https://img.shields.io/badge/License-Apache--2.0-green)](LICENSE)
	[![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-orange)](https://huggingface.co/amkyawdev/amk-coder-v2)

	---

	## 📋 Table of Contents

	- [Features](#-features)
	- [Model Details](#-model-details)
	- [Quick Start](#-quick-start)
	- [Usage Examples](#-usage-examples)
	- [API Endpoints](#-api-endpoints)
	- [Deployment](#-deployment)
	- [Limitations](#-limitations)
	- [License](#-license)

	---

	## ✨ 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 \|
	\| 🔍 Web Search \| Integration for latest documentation \|
	\| 🌐 Streaming \| Real-time response streaming \|
	\| 🎨 Beautiful UI \| Modern chat interface \|

	---

	## 📊 Model Details

	\| Attribute \| Value \|
	\|-----------\|-------\|
	\| Base Model \| Qwen2.5-Coder-1.5B \|
	\| Parameters \| 2B (2,000M) \|
	\| Architecture \| Qwen2ForCausalLM \|
	\| Hidden Size \| 1536 \|
	\| Layers \| 28 \|
	\| Attention Heads \| 12 \|
	\| Vocab Size \| 151,936 \|
	\| Context Length \| 32,768 tokens \|
	\| Tensor Type \| BF16 \|
	\| Format \| Safetensors \|
	\| License \| Apache-2.0 \|

	### Training Details

	\| Parameter \| Value \|
	\|-----------\|-------\|
	\| Framework \| Transformers + PEFT \|
	\| Training Method \| LoRA fine-tuning \|
	\| Learning Rate \| 2e-4 \|
	\| Epochs \| 3 \|
	\| Batch Size \| 8 \|
	\| Max Length \| 2048 \|

	---

	## 🚀 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 နဲ့ list comprehension ရေးပါ"}
	]
	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
	# Code generation in Myanmar
	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 Endpoints

	### 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
	}'
	```

	---

	## 🐳 Deployment

	### Docker

	```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
	```

	### Google Colab

	[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/amkyawdev/amk-coder-v2)

	### Kaggle

	[![Kaggle](https://img.shields.io/badge/Kaggle-Notebook-orange)](https://www.kaggle.com)

	---

	## ⚠️ 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/docs/hub)

	---

	## 🙏 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](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