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")
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

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

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Update README.md with model details

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metadata

license: apache-2.0
pipeline_tag: text-generation
tags:
  - code
  - qwen
  - generated_from_trainer
  - myanmar-nlp
  - ai-agent
library_name: transformers
base_model: Qwen/Qwen2.5-Coder-1.5B
datasets:
  - amkyawdev/mm-llm-coder-agent-dataset
language:
  - my
  - en
metrics:
  - accuracy

Model Card for amk-coder-v2

Model Details

Model Description

Myanmar-localized coding agent model fine-tuned from Qwen/Qwen2.5-Coder-1.5B using LoRA (PEFT). Designed for code generation and coding assistance in Myanmar language context.

Developed by: amkyawdev
Model type: Language Model (LLM)
Language(s) (NLP): Myanmar (my), English (en)
License: Apache-2.0
Finetuned from model: Qwen/Qwen2.5-Coder-1.5B

Model Sources

Repository: amkyawdev/amk-coder-v2
Dataset: amkyawdev/mm-llm-coder-agent-dataset

Model Configuration

Parameter	Value
Base Model	Qwen/Qwen2.5-Coder-1.5B
Fine-tuning Method	LoRA (PEFT)
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Optimizer	paged_adamw_8bit
Precision	FP16 Mixed Precision
Learning Rate	3e-5
Training Infrastructure	Kaggle Cloud (Dual NVIDIA T4 GPUs)

Chat Template

This model uses the ChatML structure:

<|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:

Training Details

Training Data

Dataset: amkyawdev/mm-llm-coder-agent-dataset
Description: Myanmar localized coding agent dataset for instruction-tuned code generation

Training Hyperparameters

Parameter	Value
Precision	FP16 Mixed Precision
Optimizer	paged_adamw_8bit
Learning Rate	3e-5
Hardware	Kaggle Cloud (Dual NVIDIA T4 GPUs)

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "amkyawdev/amk-coder-v2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Chat prompt format
prompt = """<|im_start|>system
You are an expert Myanmar AI coding agent with tool access.<|im_end|>
<|im_start|>user
Write a Python function to add two numbers
Tools available: python<|im_end|>
<|im_start|>assistant
Thought & Code:
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Uses

Direct Use

This model can be used for code generation tasks with Myanmar language instructions. Suitable for building coding assistants that understand Burmese/Myanmar language prompts.

Out-of-Scope Use

Not intended for production deployment without fine-tuning
Not tested for safety-critical applications
May generate incorrect code; always verify outputs

Bias, Risks, and Limitations

Model may generate syntactically incorrect code
May not follow security best practices
Training data quality affects output quality
Myanmar language support may be limited compared to English

Environmental Impact

Hardware Type: NVIDIA T4 GPUs (Dual)
Cloud Provider: Kaggle
Training Time: ~3-5 hours

Citation

If you use this model, please cite:

@misc{amk-coder-v2,
  author = {amkyawdev},
  title = {amk-coder-v2: Myanmar Coding Agent Model},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/amkyawdev/amk-coder-v2}
}

More Information

Dataset: amkyawdev/mm-llm-coder-agent-dataset
Base Model: Qwen/Qwen2.5-Coder-1.5B