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
```

Effect of UL2 training objective

#20

by malteos - opened Jan 26, 2023

Discussion

malteos

Jan 26, 2023

Can you quantify how much of the performance gains are due to the UL2 training objective?

Or would you even share the checkpoint after UL2 but before task fine-tuning?

juewang

Together org Jan 27, 2023

Thank you for your question. The improvement in performance can primarily be attributed to the increase in available data, which allows the model to learn more knowledge effectively. Additionally, the UL2 training objective also contributes to the overall performance, although it should be noted that the improvement from this is relatively small (~1%). To summarize, adding more data is often one of the most effective ways to improve a specific task. However, once you hit the wall, UL2 can also be considered as a potential approach to further improve the performance.

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