Instructions to use zarakiquemparte/beluga-limarp-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zarakiquemparte/beluga-limarp-7b with Transformers:

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

pipe = pipeline("text-generation", model="zarakiquemparte/beluga-limarp-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zarakiquemparte/beluga-limarp-7b")
model = AutoModelForCausalLM.from_pretrained("zarakiquemparte/beluga-limarp-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zarakiquemparte/beluga-limarp-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/zarakiquemparte/beluga-limarp-7b

SGLang

How to use zarakiquemparte/beluga-limarp-7b 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 "zarakiquemparte/beluga-limarp-7b" \
    --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": "zarakiquemparte/beluga-limarp-7b",
		"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 "zarakiquemparte/beluga-limarp-7b" \
        --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": "zarakiquemparte/beluga-limarp-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zarakiquemparte/beluga-limarp-7b with Docker Model Runner:
```
docker model run hf.co/zarakiquemparte/beluga-limarp-7b
```

Model Card: Stable Beluga LimaRP 7b

This is a LLama 2 Model and uses Stable Beluga 7b as a base and merged with LimaRP LLama2 7B.

This merge of Lora with Model was done with this script

Bias, Risks, and Limitations

This model is not intended for supplying factual information or advice in any form

Training Details

This model is merged and can be reproduced using the tools mentioned above. Please refer to all provided links for extra model-specific details.

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