Instructions to use amkyawdev/myanmar-ai-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amkyawdev/myanmar-ai-v3 with Transformers:

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

pipe = pipeline("text-generation", model="amkyawdev/myanmar-ai-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amkyawdev/myanmar-ai-v3", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amkyawdev/myanmar-ai-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amkyawdev/myanmar-ai-v3"
# 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/myanmar-ai-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amkyawdev/myanmar-ai-v3

SGLang

How to use amkyawdev/myanmar-ai-v3 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/myanmar-ai-v3" \
    --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/myanmar-ai-v3",
		"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/myanmar-ai-v3" \
        --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/myanmar-ai-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amkyawdev/myanmar-ai-v3 with Docker Model Runner:
```
docker model run hf.co/amkyawdev/myanmar-ai-v3
```

🇲🇲 Myanmar AI V3

Myanmar AI V3 သည် Qwen2.5-Coder-1.5B base model ပေါ်တွင် LoRA adapter ဖြင့် fine-tune လုပ်ထားသော မြန်မာဘာသာစကားအတွက် Language Model ဖြစ်ပါသည်။

📋 Model Details

Field	Details
Model Name	amkyawdev/myanmar-ai-v3
Model Type	LoRA Adapter (Fine-tuned)
Base Model	Qwen/Qwen2.5-Coder-1.5B-Instruct
Language(s)	မြန်မာ (Burmese), English
License	MIT License
Developer	amkyawdev

🔧 Technical Specifications

Specification	Value
Architecture	Transformer-based Decoder (Causal LM)
Base Model Params	1.5 Billion
LoRA Rank (r)	16
LoRA Alpha	32
LoRA Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Task Type	Causal Language Modeling

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

MODEL_NAME = "amkyawdev/myanmar-ai-v3"
BASE_MODEL = "Qwen/Qwen2.5-Coder-1.5B-Instruct"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, MODEL_NAME)

# Generate
text = "မြန်မာနိုင်ငံအကြောင်း ပြောပါ"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Training Data

Dataset: amkyawdev/myanmar-ai-training
Data Format: SFT (Supervised Fine-Tuning) with system/user/assistant messages

⚠️ Limitations

မြန်မာဘာသာစကားဖြင့် conversation ပြုလုပ်နိုင်ပါသည်
English prompts များကိုလည်း နားလည်နိုင်ပါသည်
Programming/code generation tasks များအတွက် အထူးသင့်တော်ပါသည် (Base model: Qwen2.5-Coder)

📞 Contact

Developer: amkyawdev
Demo: Live Demo

Made with ❤️ for the Myanmar AI Community

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for amkyawdev/myanmar-ai-v3

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Adapter

(119)

this model

amkyawdev
/

myanmar-ai-v3