Instructions to use togethercomputer/RedPajama-INCITE-Instruct-3B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use togethercomputer/RedPajama-INCITE-Instruct-3B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="togethercomputer/RedPajama-INCITE-Instruct-3B-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1") model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Instruct-3B-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use togethercomputer/RedPajama-INCITE-Instruct-3B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "togethercomputer/RedPajama-INCITE-Instruct-3B-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/RedPajama-INCITE-Instruct-3B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1
- SGLang
How to use togethercomputer/RedPajama-INCITE-Instruct-3B-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/RedPajama-INCITE-Instruct-3B-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/RedPajama-INCITE-Instruct-3B-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/RedPajama-INCITE-Instruct-3B-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/RedPajama-INCITE-Instruct-3B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use togethercomputer/RedPajama-INCITE-Instruct-3B-v1 with Docker Model Runner:
docker model run hf.co/togethercomputer/RedPajama-INCITE-Instruct-3B-v1
Commit History
-
Update README.md
0c66778 -
Update README.md
6f1be80
-
Update tokenizer_config.json
ecbf8a0 -
Update README.md
43a7a94 -
Update README.md
6d69254 -
Update README.md
2afd232 -
Update README.md
993da6a
-
Update README.md
297be11 -
Update README.md
d2362a6 -
update readme.md
7e384d9 -
update readme
37807e2 -
update readme
cbdcae0 -
Update README.md
4b96b8d -
init
ea35838 -
initial commit
94d0b35