Instructions to use jojo-ai-mst/thai-opt350m-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jojo-ai-mst/thai-opt350m-instruct with Transformers:

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

pipe = pipeline("text-generation", model="jojo-ai-mst/thai-opt350m-instruct")

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

tokenizer = AutoTokenizer.from_pretrained("jojo-ai-mst/thai-opt350m-instruct")
model = AutoModelForCausalLM.from_pretrained("jojo-ai-mst/thai-opt350m-instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jojo-ai-mst/thai-opt350m-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jojo-ai-mst/thai-opt350m-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jojo-ai-mst/thai-opt350m-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jojo-ai-mst/thai-opt350m-instruct

SGLang

How to use jojo-ai-mst/thai-opt350m-instruct 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 "jojo-ai-mst/thai-opt350m-instruct" \
    --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": "jojo-ai-mst/thai-opt350m-instruct",
		"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 "jojo-ai-mst/thai-opt350m-instruct" \
        --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": "jojo-ai-mst/thai-opt350m-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jojo-ai-mst/thai-opt350m-instruct with Docker Model Runner:
```
docker model run hf.co/jojo-ai-mst/thai-opt350m-instruct
```

jojo-ai-mst commited on Mar 21, 2024

Commit

fe262c9

verified ·

1 Parent(s): 04d745a

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -2

README.md CHANGED Viewed

@@ -32,8 +32,6 @@ widget:
 # thai-opt350m-instruct
  a generative language model for thai language based on opt350m
-![Thai Open Pretrained Transformer 350M - Generative](/thai-opt350m.png)
 **Thai-OPT350M-Instruct** is a fine-tuned pretrained transformer for **thai language** based on facebook/opt-350m.
 Dataset for thai-opt350m-instruct
@@ -63,6 +61,10 @@ User: อะไรคือวิธีที่ดีที่สุดใน
 # How to use
 ```python
 model = AutoModelForCausalLM.from_pretrained("jojo-ai-mst/thai-opt350m-instruct")
 tokenizer = AutoTokenizer.from_pretrained("jojo-ai-mst/thai-opt350m-instruct")

 # thai-opt350m-instruct
  a generative language model for thai language based on opt350m
 **Thai-OPT350M-Instruct** is a fine-tuned pretrained transformer for **thai language** based on facebook/opt-350m.
 Dataset for thai-opt350m-instruct
 # How to use
 ```python
+# pip install transformers
+from transformers import AutoModelForCausalLM,AutoTokenizer
 model = AutoModelForCausalLM.from_pretrained("jojo-ai-mst/thai-opt350m-instruct")
 tokenizer = AutoTokenizer.from_pretrained("jojo-ai-mst/thai-opt350m-instruct")