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 Settings

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
```

Open Ended Generation

by nik8482 - opened Jun 14, 2023

Discussion

nik8482

Jun 14, 2023

Hi,

Is there anyway to stop the model from asking itself questions after generating an initial response? This is an example of responses I'm currently getting

Ideally I want the model to stop generating after the first response, I could reduce the max tokens length however I get responses which seem to get cut off midway

Thanks for the help!

szeta

Jun 14, 2023

•

edited Jun 14, 2023

Add "<human>:" as a stop word.

Example: (here I have ["<human>:"] inside the variable self.model_conf['stop_words'])

            self.stop_words_ids = [
                self.tokenizer(stop_word, return_tensors="pt")["input_ids"].squeeze()
                for stop_word in self.model_conf["stop_words"]
            ]
            self.stopping_criteria = StoppingCriteriaList(
                [StoppingCriteriaSub(stops=self.stop_words_ids)]
            )

and then add this as parameter to your generate() call:

stopping_criteria=self.stopping_criteria,

Sven00

Jun 16, 2023

@szeta can you please provide a step-by-step guide for less technical savvy folks? thank you

szeta

Jun 16, 2023

Sven i think we work on the same project. Let's catch up. :-)

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