Instructions to use pythainlp/KhanomTanLLM-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pythainlp/KhanomTanLLM-1B with Transformers:

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

pipe = pipeline("text-generation", model="pythainlp/KhanomTanLLM-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pythainlp/KhanomTanLLM-1B")
model = AutoModelForCausalLM.from_pretrained("pythainlp/KhanomTanLLM-1B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pythainlp/KhanomTanLLM-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pythainlp/KhanomTanLLM-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pythainlp/KhanomTanLLM-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pythainlp/KhanomTanLLM-1B

SGLang

How to use pythainlp/KhanomTanLLM-1B 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 "pythainlp/KhanomTanLLM-1B" \
    --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": "pythainlp/KhanomTanLLM-1B",
		"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 "pythainlp/KhanomTanLLM-1B" \
        --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": "pythainlp/KhanomTanLLM-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pythainlp/KhanomTanLLM-1B with Docker Model Runner:
```
docker model run hf.co/pythainlp/KhanomTanLLM-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

KhanomTan LLM (1B)

KhanomTan LLM is a bilingual language model trained in Thai and English from open source dataset by PyThaiNLP. We train the model from public dataset only. We public the dataset, source code, and model.

Repository: https://github.com/pythainlp/KhanomTanLLM

Codename: numfa-v2

Model Details

Model Description

The model was trained by easylm

Acknowledgements

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). We use TPU4-64 for training model about 4 days.

Thank you TPU Research Cloud and EasyLM project! We use EasyLM for pretraining model.

How to use

Example

# !pip install accelerate sentencepiece transformers bitsandbytes
import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="numfa/numfa_v2-1b", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating

outputs = pipe("test is", max_new_tokens=300, do_sample=True, temperature=0.9, top_k=50, top_p=0.95, no_repeat_ngram_size=2,typical_p=1.)
print(outputs[0]["generated_text"])

Downloads last month: 90

Safetensors

Model size

1B params

Tensor type

F16

Model tree for pythainlp/KhanomTanLLM-1B

Quantizations

3 models

pythainlp
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KhanomTanLLM-1B