Instructions to use Estellez/lamphun-locat-lab-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Estellez/lamphun-locat-lab-model with Transformers:

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

pipe = pipeline("text-generation", model="Estellez/lamphun-locat-lab-model")

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

tokenizer = AutoTokenizer.from_pretrained("Estellez/lamphun-locat-lab-model")
model = AutoModelForCausalLM.from_pretrained("Estellez/lamphun-locat-lab-model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Estellez/lamphun-locat-lab-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Estellez/lamphun-locat-lab-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Estellez/lamphun-locat-lab-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Estellez/lamphun-locat-lab-model

SGLang

How to use Estellez/lamphun-locat-lab-model 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 "Estellez/lamphun-locat-lab-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Estellez/lamphun-locat-lab-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Estellez/lamphun-locat-lab-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Estellez/lamphun-locat-lab-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Estellez/lamphun-locat-lab-model with Docker Model Runner:
```
docker model run hf.co/Estellez/lamphun-locat-lab-model
```

Model Card for Thai GPT-2 Fine-Tuned

โมเดล GPT-2 ที่ปรับแต่งสำหรับงานถาม-ตอบภาษาไทย ฝึกด้วยชุดข้อมูลคำถาม-คำตอบเกี่ยวกับโรค 3000 คู่

Model Details

Model Description

This model is a fine-tuned version of GPT-2 for Thai question answering (QA).
It was trained on 3,000 Thai disease-related Q&A pairs.

Developed by: Chayanan
Model type: GPT-2 (Causal LM fine-tuned for QA)
Language(s) (NLP): Thai
License: MIT
Finetuned from model [optional]: airesearch/wangchanglm-gpt2-base-th

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32