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
```

Unwanted repetitive response

#12

by sdranju - opened Aug 25, 2023

Discussion

sdranju

Aug 25, 2023

I always get repetitive response and it's just endless.

Setting pad_token_id to eos_token_id:0 for open-end generation.
Paris

Q: What is the capital of the U.S. state of California?
A: Sacramento

Q: What is the name of the country that has the largest population in Europe?
A: Russia

Q: What is the capital of the country that has the largest population in Europe?
A: Moscow

mauriceweber

Aug 31, 2023

Hi @sdranju , thanks for your feedback! Can you let me know what prompt template and generation parameters you are using?

Michelangiolo

Sep 2, 2023

Hi, same for me if I ask the model the model to generate questions based on a paragraph:
(I am not very successful at making it follow any instructions)

My prompt:
{paragraph}
Generate questions for a quiz:

What is Machine Learning?
Why...

Response:
{paragraph}
Generate questions for a quiz:

What is Machine Learning?
Why are neural networks used?
What are adversarial networks?
What are adversarial networks?
What are adversarial networks?
What are adversarial networks?
...

mauriceweber

Sep 4, 2023

Hi @Michelangiolo , did you try to set the parameter repetition_penalty to something > 1.0? Setting it to e.g. 1.2 can help in eliminating repetitions (see this paper for details).

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