Instructions to use ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16")
model = AutoModelForCausalLM.from_pretrained("ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16

SGLang

How to use ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16 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 "ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16" \
    --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": "ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16",
		"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 "ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16" \
        --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": "ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16 with Docker Model Runner:
```
docker model run hf.co/ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16
```

RedPajama-INCITE-7B-Base-sharded-bf16

This is the togethercomputer/RedPajama-INCITE-7B-Base model, but the model file(s) have been sharded to ~2GB each to ensure it can be loaded on low-RAM runtimes (like Colab).

Please refer to the original model card for all details/issues w.r.t. to this model. - inference examples are also available on the original model card linked above. - example colab notebook covering the basics

Downloads last month: 4

ethzanalytics
/

RedPajama-INCITE-7B-Base-sharded-bf16

RedPajama-INCITE-7B-Base-sharded-bf16

Please refer to the original model card for all details/issues w.r.t. to this model. - inference examples are also available on the original model card linked above. - example colab notebook covering the basics

Dataset used to train ethzanalytics/RedPajama-INCITE-7B-Base-sharded-bf16