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
```

amkyawdev commited on 1 day ago

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@@ -17,197 +17,122 @@ language:
 metrics:
 - accuracy
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
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-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
 ### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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 ## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Compute Infrastructure
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 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](https://huggingface.co/amkyawdev/amk-coder-v2)
+- **Dataset:** [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/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:
+```xml
+<|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
+```python
+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](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset)
+- Base Model: [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B)