Instructions to use togethercomputer/RedPajama-INCITE-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/RedPajama-INCITE-7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/RedPajama-INCITE-7B-Chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Chat")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/RedPajama-INCITE-7B-Chat")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/RedPajama-INCITE-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/RedPajama-INCITE-7B-Chat"
# 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-7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/RedPajama-INCITE-7B-Chat

SGLang

How to use togethercomputer/RedPajama-INCITE-7B-Chat 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-7B-Chat" \
    --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-7B-Chat",
		"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-7B-Chat" \
        --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-7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/RedPajama-INCITE-7B-Chat with Docker Model Runner:
```
docker model run hf.co/togethercomputer/RedPajama-INCITE-7B-Chat
```

Help!: I Can't Convert RedPajama-INCITE-Chat-7B-v0.1 to ggml

by Joseph717171 - opened May 18, 2023

Discussion

Joseph717171

May 18, 2023

•

edited May 18, 2023

I have tried to convert RedPajama-INCITE-Chat-7B-v0.1 to ggml, using your packaged convert-path-to-ggml.py and convert.py, to no avail. Any help you can give would be greatly appreciated.

Joseph717171 changed discussion title from Help!: I Can't Convert RedPajama-INCITE-Chat-7B-v0.1 to sgml to Help!: I Can't Convert RedPajama-INCITE-Chat-7B-v0.1 to ggml May 18, 2023

zhangce

Together org May 18, 2023

@biyuan can you help here?

biyuan

May 18, 2023

Hi @Joseph717171 , would you like to try the following steps?

Checkout our code at https://github.com/togethercomputer/redpajama.cpp.git;
make redpajama-chat quantize-gptneox
Create a new script like the following under /examples/redpajama/scripts/ (just a slight change of the current install script):
Running this script by bash should be good to go. (I tested this on my machine.)

On the other hand, there exists some potential risk that this procedure can fail on a weak workstation: if the CPU RAM cannot hold the 7B model (about 14 GB), the script will exit. Currently, we do not have efficient support for running the converting in a restricted system budget.

Joseph717171

May 19, 2023

•

edited May 19, 2023

Thanks for your help guys! It worked like a charm! 🤩🚀

eshaanagarwal

Jun 12, 2023

Hi whats the inference speed you are getting for this ? is this any way supported with Langchain ?

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