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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

How to handle padding?

by GabrielePrato - opened Jun 9, 2023

Discussion

GabrielePrato

Jun 9, 2023

What is the correct way to handle padding with RedPajama? Currently I do the following:

train_tokenizer = AutoTokenizer.from_pretrained('togethercomputer/RedPajama-INCITE-7B-Base', use_fast=True)
eval_tokenizer = AutoTokenizer.from_pretrained('togethercomputer/RedPajama-INCITE-7B-Base', use_fast=True, padding_side='left')
train_tokenizer.add_special_tokens({'pad_token': '[PAD]'})
eval_tokenizer.add_special_tokens({'pad_token': '[PAD]'})

train_inputs = train_tokenizer(train_prompts, padding=True, return_tensors='pt')
train_output = model(**train_inputs) # model(input_ids=train_inputs.input_ids, attention_mask=train_inputs.attention_mask)

eval_inputs = eval_tokenizer(eval_prompts, padding=True, return_tensors='pt')
eval_output = model.generate(**eval_inputs, max_new_tokens=128) # model.generate(input_ids=eval_inputs.input_ids, attention_mask=eval_inputs.attention_mask)

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