Instructions to use togethercomputer/RedPajama-INCITE-Chat-3B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/RedPajama-INCITE-Chat-3B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/RedPajama-INCITE-Chat-3B-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-Chat-3B-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/RedPajama-INCITE-Chat-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-Chat-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-Chat-3B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1

SGLang

How to use togethercomputer/RedPajama-INCITE-Chat-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-Chat-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-Chat-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-Chat-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-Chat-3B-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/RedPajama-INCITE-Chat-3B-v1 with Docker Model Runner:
```
docker model run hf.co/togethercomputer/RedPajama-INCITE-Chat-3B-v1
```

fine tune model?

by deewalia20 - opened May 13, 2023

Discussion

deewalia20

May 13, 2023

How to fine tune this model? Any sample code and training data format available?

deewalia20 changed discussion title from fine tune model to fine tune model? May 13, 2023

Tunkerspell

May 19, 2023

Yes, please! Colab notebook would be sweet! Also, just wanted confirmation the token size is 4096?

EugeneLu

May 20, 2023

Yes, please see our blog post.

deewalia20

May 23, 2023

Thanks @EugeneLu

deewalia20 changed discussion status to closed May 23, 2023

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