Instructions to use mlabonne/Beagle14-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlabonne/Beagle14-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlabonne/Beagle14-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlabonne/Beagle14-7B") model = AutoModelForCausalLM.from_pretrained("mlabonne/Beagle14-7B") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mlabonne/Beagle14-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlabonne/Beagle14-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlabonne/Beagle14-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlabonne/Beagle14-7B
- SGLang
How to use mlabonne/Beagle14-7B 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 "mlabonne/Beagle14-7B" \ --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": "mlabonne/Beagle14-7B", "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 "mlabonne/Beagle14-7B" \ --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": "mlabonne/Beagle14-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mlabonne/Beagle14-7B with Docker Model Runner:
docker model run hf.co/mlabonne/Beagle14-7B
GGUF Version of the best 7B LLM!
First of all: Thank you for this awesome model! It seems to perform really well. Small models are great, as one can run them locally. =D
@TheBloke Could you create a gguf Version of this model?
(By the way, I have access to a computer with two rtx 3090 - I am not quite sure how to create gguf versions, but if its doable, I could perhaps help.)
I could also use the old server of a company of a friend. It is equipped with 3 M40 GPUs. While beeing a bit old, they still got some Vram. Don't know if this is usefull.
Thanks @SimSim93 ! I'm currently evaluating a DPO version of this model, it should be even better.
If you want to make GGUF versions of a 7B model, you don't need any big hardware. I created this notebook to automate this process (T4 GPU): https://colab.research.google.com/drive/1P646NEg33BZy4BfLDNpTz0V0lwIU3CHu#scrollTo=fD24jJxq7t3k
I'm currently evaluating a DPO version of this model, it should be even better.
Not much Difference in Scores.