Instructions to use togethercomputer/GPT-JT-6B-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/GPT-JT-6B-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="togethercomputer/GPT-JT-6B-v0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v0") model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-JT-6B-v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use togethercomputer/GPT-JT-6B-v0 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-v0" # 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-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/togethercomputer/GPT-JT-6B-v0
- SGLang
How to use togethercomputer/GPT-JT-6B-v0 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-v0" \ --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-v0", "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-v0" \ --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-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use togethercomputer/GPT-JT-6B-v0 with Docker Model Runner:
docker model run hf.co/togethercomputer/GPT-JT-6B-v0
File size: 562 Bytes
a5302c8 6201dbd a5302c8 6201dbd ce5e160 a5302c8 ce5e160 5b78297 41f623e 6201dbd 9b6c66f 43f10c8 9b6c66f a5302c8 99acfd8 a5302c8 99acfd8 41bd193 99acfd8 41bd193 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ---
language:
- en
datasets:
- natural_instructions
- the_pile
- cot
- Muennighoff/P3
tags:
- gpt
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.1
widget:
- text: "Is this review positive or negative? Review: Best cast iron skillet you will ever buy. Answer:"
example_title: "Sentiment analysis"
- text: "Where is Zurich? Ans:"
example_title: "Question Answering"
---
# Quick Start
```python
from transformers import pipeline
pipe = pipeline(model='togethercomputer/GPT-JT-6B-v0')
pipe("Where is Zurich? Ans:")
``` |