Instructions to use ThaiLLM/ThaiLLM-8B-MedApp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThaiLLM/ThaiLLM-8B-MedApp with Transformers:

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

pipe = pipeline("text-generation", model="ThaiLLM/ThaiLLM-8B-MedApp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ThaiLLM/ThaiLLM-8B-MedApp")
model = AutoModelForCausalLM.from_pretrained("ThaiLLM/ThaiLLM-8B-MedApp", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ThaiLLM/ThaiLLM-8B-MedApp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThaiLLM/ThaiLLM-8B-MedApp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThaiLLM/ThaiLLM-8B-MedApp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThaiLLM/ThaiLLM-8B-MedApp

SGLang

How to use ThaiLLM/ThaiLLM-8B-MedApp 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 "ThaiLLM/ThaiLLM-8B-MedApp" \
    --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": "ThaiLLM/ThaiLLM-8B-MedApp",
		"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 "ThaiLLM/ThaiLLM-8B-MedApp" \
        --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": "ThaiLLM/ThaiLLM-8B-MedApp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ThaiLLM/ThaiLLM-8B-MedApp with Docker Model Runner:
```
docker model run hf.co/ThaiLLM/ThaiLLM-8B-MedApp
```

PumeTu commited on May 12

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@@ -17,7 +17,7 @@ The model was trained using Prime-Intellect's [prime-rl](https://github.com/Prim
 | Model | Response (LLM Judge) | Citation (F1) | Accuracy | Trigger (F1) | Macro (F1) |
 |-------------------------------|:--------------------:|:-------------:|:--------:|:----------:|:--------:|
-| Typhoon 8B                    | 60.80                | 11.70         | 67.50    | 47.50      | 39.40    |
 | Qwen3-30B-A3B                | —                    | —             | 99.00    | 99.20      | 97.80    |
 | ThaiLLM-8B-IQ                | 69.30                | 70.00         | —        | —          | —        |
 | ThaiLLM-8B-ToolUse            | —                    | —             | 99.90    | 100.00     | 100.00   |

 | Model | Response (LLM Judge) | Citation (F1) | Accuracy | Trigger (F1) | Macro (F1) |
 |-------------------------------|:--------------------:|:-------------:|:--------:|:----------:|:--------:|
+| typhoon-s-thaillm-8b-instruct-research-preview                    | 60.80                | 11.70         | 67.50    | 47.50      | 39.40    |
 | Qwen3-30B-A3B                | —                    | —             | 99.00    | 99.20      | 97.80    |
 | ThaiLLM-8B-IQ                | 69.30                | 70.00         | —        | —          | —        |
 | ThaiLLM-8B-ToolUse            | —                    | —             | 99.90    | 100.00     | 100.00   |