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", device_map="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
```

amkyawdev commited on Jul 10

Commit

67349c2

verified ·

1 Parent(s): ba01b1a

Fix YAML metadata - base_model, datasets, proper tags

Browse files

Files changed (1) hide show

README.md +58 -26

README.md CHANGED Viewed

@@ -11,51 +11,83 @@ tags:
 - llm
 - chat
 - instruction-following
 ---
-# Myanmar Ghost
 **Advanced Myanmar Language Model (LLM)**
-## Overview
-Myanmar Ghost is a Large Language Model for Myanmar language instruction following and conversation.
-- Based on: MiniMax-M2.7 Architecture
-- Fine-tuned with: QLoRA on Myanmar datasets
-- Supports: Chat, Code, Translation, QA
-## Quick Start
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-model = AutoModelForCausalLM.from_pretrained("amkyawdev/myanmar-ghost", load_in_4bit=True)
-tokenizer = AutoTokenizer.from_pretrained("amkyawdev/myanmar-ghost")
-prompt = "### Instruction:
 မြန်မာစာမေးပွဲအကြောင်း ရှင်းပါ
 ### Response:
-"
-inputs = tokenizer(prompt, return_tensors="pt")
-outputs = model.generate(**inputs, max_new_tokens=256)
-print(tokenizer.decode(outputs[0]))
 ```
-## Training Data
-| Dataset | Samples |
-|---------|---------|
-| myanmar-v3-clean | 877K |
-| burme-coder-max | 1M |
-| mm-llm-coder-agent | 4M |
-| alpaca-myanmar | 91K |
-## License
 Apache 2.0
-## Author
-Aung Myo Kyaw (amkyawdev)

 - 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
+```python
+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)**