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

Libraries

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")

# 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")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

Specify RLHF data for the Instruct and Chat versions in model card

#11

by markding - opened Jul 26, 2023

Discussion

markding

Jul 26, 2023

•

edited Jul 26, 2023

The model card doesn't seem to offer details or info on how the Instruct and Chat versions were RLHF'd/instruction-tuned. This is what the release blog post says:

RedPajama-INCITE-Chat-7B-v0.1 is its chat counterpart trained over Dolly 2.0 and Open Assistant
RedPajama-INCITE-Instruct-7B-v0.1 is instruction tuned for few-shot applications. We follow the recipe for GPT-JT but eliminate all datasets that overlap with the HELM benchmark.

Perhaps add this to the model card? And it would be useful to specify exactly which datasets were included / excluded, to spare interested users the trouble of figure out what the HELM benchmark includes and how it does or does not overlap with GPT-JT.

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