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

Libraries

How to use amkyawdev/myanmar-ghost with Transformers:

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amkyawdev/myanmar-ghost with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/amkyawdev/myanmar-ghost

SGLang

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

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

myanmar-ghost / README.md

amkyawdev

Fix YAML metadata - base_model, datasets, proper tags

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metadata

language:
  - my
  - en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
  - myanmar
  - burmese
  - llm
  - chat
  - instruction-following
  - conversational
  - autoregressive
base_model: MiniMaxAI/MiniMax-M2.7
datasets:
  - amkyawdev/myanmar-v3-clean
  - amkyawdev/burme-coder-max
  - amkyawdev/mm-llm-coder-agent-dataset
  - saillab/alpaca-myanmar_burmese-cleaned

🐉 Myanmar Ghost

Advanced Myanmar Language Model (LLM)

Fine-tuned on MiniMax-M2.7 with QLoRA for Myanmar language understanding.

💬 Features

🗣️ Myanmar Chat - Natural conversation in Burmese
📝 Instruction Following - Follow complex Myanmar instructions
💻 Code Generation - Write Myanmar code and documentation
🌐 Translation - Myanmar ↔ English
📖 Summarization - Summarize Myanmar text
❓ QA - Answer questions in Myanmar

📊 Training Data

Dataset	Samples
myanmar-v3-clean	877,706
burme-coder-max	1,000,000
mm-llm-coder-agent	4,000,020
alpaca-myanmar	41,601

Total: ~6M instruction samples

🚀 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model
model_name = "amkyawdev/myanmar-ghost"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    load_in_4bit=True,
    device_map="auto"
)

# Generate
prompt = """### Instruction:
မြန်မာစာမေးပွဲအကြောင်း ရှင်းပါ

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📋 Requirements

torch>=2.0.0
transformers>=4.40.0
bitsandbytes>=0.40.0
peft>=0.4.0
accelerate>=0.20.0

📜 License

Apache 2.0

👤 Author

Aung Myo Kyaw (amkyawdev)