Instructions to use Naphon/pythia-2.8b-thai-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Naphon/pythia-2.8b-thai-base-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Naphon/pythia-2.8b-thai-base-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Naphon/pythia-2.8b-thai-base-v1")
model = AutoModelForCausalLM.from_pretrained("Naphon/pythia-2.8b-thai-base-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Naphon/pythia-2.8b-thai-base-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Naphon/pythia-2.8b-thai-base-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Naphon/pythia-2.8b-thai-base-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Naphon/pythia-2.8b-thai-base-v1

SGLang

How to use Naphon/pythia-2.8b-thai-base-v1 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 "Naphon/pythia-2.8b-thai-base-v1" \
    --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": "Naphon/pythia-2.8b-thai-base-v1",
		"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 "Naphon/pythia-2.8b-thai-base-v1" \
        --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": "Naphon/pythia-2.8b-thai-base-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Naphon/pythia-2.8b-thai-base-v1 with Docker Model Runner:
```
docker model run hf.co/Naphon/pythia-2.8b-thai-base-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This model is currently in the training stage and may produce hallucinations

Example Use

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Naphon/pythia-2.8b-thai-base-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

text = "รองศาสตราจารย์ ดร.สุวิทย์ แซ่เตีย อธิการบดีมหาวิทยาลัยเทคโนโลยีพระจอมเกล้าธนบุรี"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=50, do_sample=True, temperature=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for Naphon/pythia-2.8b-thai-base-v1

Base model

EleutherAI/pythia-2.8b-deduped

Finetuned

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Finetunes

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Quantizations

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Naphon
/

pythia-2.8b-thai-base-v1

This model is currently in the training stage and may produce hallucinations

Example Use

Model tree for Naphon/pythia-2.8b-thai-base-v1

Dataset used to train Naphon/pythia-2.8b-thai-base-v1