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

Libraries

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

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

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

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

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-Moderation-6B")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-JT-Moderation-6B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/GPT-JT-Moderation-6B"
# 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-Moderation-6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

juewang commited on Mar 9, 2023

Commit

d53a63d

1 Parent(s): 699c889

Update README.md

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@@ -53,8 +53,7 @@ Output:
 - [allenai/prosocial-dialog](https://huggingface.co/datasets/allenai/prosocial-dialog).
 - A small subset of [OpenChat](https://huggingface.co/togethercomputer/OpenChaT)'s data to augment `casual` queries.
-The processed data can be found [here](https://drive.google.com/file/d/1ui4SuOYXyoq-5gVEC1NXwzJxs3hwaw0Y/view?usp=drivesdk).
 **Training Procedure**
@@ -63,6 +62,3 @@ The processed data can be found [here](https://drive.google.com/file/d/1ui4SuOYX
 - **Gradient Accumulations**: 1
 - **Batch:** 16 x 4 = 64
 - **Learning rate:** warmup to 1e-5 for 100 steps and then kept constant
-## Evaluation Results
-\[TODO\]

 - [allenai/prosocial-dialog](https://huggingface.co/datasets/allenai/prosocial-dialog).
 - A small subset of [OpenChat](https://huggingface.co/togethercomputer/OpenChaT)'s data to augment `casual` queries.
+- The processed data can be found [here](https://drive.google.com/file/d/1ui4SuOYXyoq-5gVEC1NXwzJxs3hwaw0Y/view?usp=drivesdk).
 **Training Procedure**
 - **Gradient Accumulations**: 1
 - **Batch:** 16 x 4 = 64
 - **Learning rate:** warmup to 1e-5 for 100 steps and then kept constant