Instructions to use openthaigpt/openthaigpt-1.6-72b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openthaigpt/openthaigpt-1.6-72b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="openthaigpt/openthaigpt-1.6-72b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openthaigpt/openthaigpt-1.6-72b-instruct")
model = AutoModelForCausalLM.from_pretrained("openthaigpt/openthaigpt-1.6-72b-instruct", device_map="auto")
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 Settings

vLLM

How to use openthaigpt/openthaigpt-1.6-72b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openthaigpt/openthaigpt-1.6-72b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openthaigpt/openthaigpt-1.6-72b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openthaigpt/openthaigpt-1.6-72b-instruct

SGLang

How to use openthaigpt/openthaigpt-1.6-72b-instruct 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 "openthaigpt/openthaigpt-1.6-72b-instruct" \
    --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": "openthaigpt/openthaigpt-1.6-72b-instruct",
		"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 "openthaigpt/openthaigpt-1.6-72b-instruct" \
        --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": "openthaigpt/openthaigpt-1.6-72b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openthaigpt/openthaigpt-1.6-72b-instruct with Docker Model Runner:
```
docker model run hf.co/openthaigpt/openthaigpt-1.6-72b-instruct
```

kobkrit commited on Jul 27

Commit

9dc3e43

verified ·

1 Parent(s): fc05d08

Add Run-it-locally section: official Ollama tag + GGUF quants

Browse files

Files changed (1) hide show

README.md +10 -0

README.md CHANGED Viewed

@@ -50,6 +50,16 @@ model-index:
 | 💬 General Thai chat and coding | [OpenThaiGPT 1.6 72B](https://huggingface.co/openthaigpt/openthaigpt-1.6-72b-instruct) |
 | 💻 Limited GPU | [OpenThaiGPT 1.5 7B](https://huggingface.co/openthaigpt/openthaigpt1.5-7b-instruct) |
 ## Highlights
 - **Advanced Thai language model** with 72 billion parameters
 - **Strong reasoning capabilities** in both Thai and English

 | 💬 General Thai chat and coding | [OpenThaiGPT 1.6 72B](https://huggingface.co/openthaigpt/openthaigpt-1.6-72b-instruct) |
 | 💻 Limited GPU | [OpenThaiGPT 1.5 7B](https://huggingface.co/openthaigpt/openthaigpt1.5-7b-instruct) |
+## ⚡ Run it locally
+```bash
+ollama run openthai/openthai-1.6-72b
+```
+Official GGUF quants (Q4_K_M / Q5_K_M / Q8_0):
+[openthaigpt/openthaigpt-1.6-72b-instruct-GGUF](https://huggingface.co/openthaigpt/openthaigpt-1.6-72b-instruct-GGUF)
+— ~48 GB RAM/VRAM in 4-bit. Step-by-step guide: [openthai.aieat.or.th/ollama](https://openthai.aieat.or.th/ollama)
 ## Highlights
 - **Advanced Thai language model** with 72 billion parameters
 - **Strong reasoning capabilities** in both Thai and English