Instructions to use amornpan/openthaigpt-MedChatModelv10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amornpan/openthaigpt-MedChatModelv10 with Transformers:

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

pipe = pipeline("text-generation", model="amornpan/openthaigpt-MedChatModelv10")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amornpan/openthaigpt-MedChatModelv10")
model = AutoModelForCausalLM.from_pretrained("amornpan/openthaigpt-MedChatModelv10")
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

vLLM

How to use amornpan/openthaigpt-MedChatModelv10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amornpan/openthaigpt-MedChatModelv10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amornpan/openthaigpt-MedChatModelv10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amornpan/openthaigpt-MedChatModelv10

SGLang

How to use amornpan/openthaigpt-MedChatModelv10 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 "amornpan/openthaigpt-MedChatModelv10" \
    --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": "amornpan/openthaigpt-MedChatModelv10",
		"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 "amornpan/openthaigpt-MedChatModelv10" \
        --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": "amornpan/openthaigpt-MedChatModelv10",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amornpan/openthaigpt-MedChatModelv10 with Docker Model Runner:
```
docker model run hf.co/amornpan/openthaigpt-MedChatModelv10
```

Model Card for Model ID

[ 850/1478 4:08:38 < 3:04:08, 0.06 it/s, Epoch 141/247]
Step	Training Loss	Validation Loss
50	1.889000	1.862223
100	1.871100	1.832502
150	1.822600	1.765569
200	1.741100	1.677201
250	1.633600	1.558259
300	1.503000	1.429101
350	1.407300	1.384786
400	1.373600	1.356308
450	1.347900	1.339552
500	1.333900	1.329472
550	1.324200	1.321458
600	1.315300	1.314869
650	1.306500	1.309380
700	1.300400	1.304810
750	1.294500	1.300931
800	1.288500	1.297661
850	1.283600	1.294858
Run history:

eval/loss	██▇▆▄▃▂▂▂▁▁▁▁▁▁▁▁
eval/runtime	██▇▆▅▅▆▇▄▆▅▄▅▂▃▂▁
eval/samples_per_second	▁▁▂▃▄▄▃▂▅▃▄▅▅▇▆▇█
eval/steps_per_second	▁▁▅▅▅▅▅▁▅▅▅▅▅████
train/epoch	▁▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▆▆▆▆▇▇▇▇█████
train/global_step	▁▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇█████
train/grad_norm	▇██▇▆▅▂▂▁▁▁▁▁▁▁▁▁
train/learning_rate	▁▂▃▄▅▅▆▇████▇▇▆▆▅
train/loss	██▇▆▅▄▂▂▂▂▁▁▁▁▁▁▁

Run summary:

eval/loss	1.29486
eval/runtime	19.1369
eval/samples_per_second	11.914
eval/steps_per_second	1.515
total_flos	2.208419516825174e+18
train/epoch	141.66667
train/global_step	850
train/grad_norm	0.05381
train/learning_rate	1e-05
train/loss	1.2836
train_loss	1.47271
train_runtime	14936.8511
train_samples_per_second	12.666
train_steps_per_second	0.099

Model Details

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Safetensors

Model size

8B params

Tensor type

F32

Paper for amornpan/openthaigpt-MedChatModelv10

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45