Instructions to use togethercomputer/GPT-JT-6B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/GPT-JT-6B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/GPT-JT-6B-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-JT-6B-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use togethercomputer/GPT-JT-6B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/GPT-JT-6B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/GPT-JT-6B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/GPT-JT-6B-v1

SGLang

How to use togethercomputer/GPT-JT-6B-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 "togethercomputer/GPT-JT-6B-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": "togethercomputer/GPT-JT-6B-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 "togethercomputer/GPT-JT-6B-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": "togethercomputer/GPT-JT-6B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/GPT-JT-6B-v1 with Docker Model Runner:
```
docker model run hf.co/togethercomputer/GPT-JT-6B-v1
```

juewang commited on Nov 25, 2022

Commit

ab73a1e

1 Parent(s): 918e74e

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@@ -80,7 +80,7 @@ widget:
 We present GPT-JT, a fork of GPT-6B, trained for 20,000 steps, that outperforms most 100B+ parameter models at classification, and improves most tasks relative to GPT-J-6B. GPT-JT was trained with a new decentralized algorithm on computers networked on slow 1Gbps links.
 GPT-JT is a bidirectional dense model, trained through UL2 objective with NI, P3, COT, the pile data.
-**Please check out our [Online Demo](https://huggingface.co/spaces/togethercomputer/TOMA-app)!.**
 # Quick Start
 ```python

 We present GPT-JT, a fork of GPT-6B, trained for 20,000 steps, that outperforms most 100B+ parameter models at classification, and improves most tasks relative to GPT-J-6B. GPT-JT was trained with a new decentralized algorithm on computers networked on slow 1Gbps links.
 GPT-JT is a bidirectional dense model, trained through UL2 objective with NI, P3, COT, the pile data.
+**Please check out our [Online Demo](https://huggingface.co/spaces/togethercomputer/GPT-JT)!**
 # Quick Start
 ```python