Instructions to use clibrain/lince-zero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clibrain/lince-zero with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="clibrain/lince-zero", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("clibrain/lince-zero", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("clibrain/lince-zero", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use clibrain/lince-zero with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "clibrain/lince-zero" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "clibrain/lince-zero", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/clibrain/lince-zero
- SGLang
How to use clibrain/lince-zero 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 "clibrain/lince-zero" \ --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": "clibrain/lince-zero", "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 "clibrain/lince-zero" \ --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": "clibrain/lince-zero", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use clibrain/lince-zero with Docker Model Runner:
docker model run hf.co/clibrain/lince-zero
Benchmark and evaluation
Hi, congrats on your work!
The model card says that this is a SOTA model, but is unclear on what benchmark this score is achieved.
Is this evaluation accessible?
Thank you! You're right, the model was SOTA when it was published. Now, as per MTBench evaluations, Clibrain's best one is LINCE Mistral.
Thanks for your answer! What I was actually asking about is what benchmark was used to evaluate the model, as this is unclear from the model card.
Was it MT-Bench? In that case, what was the question set to evaluate the model? Was a direct translation of the available ones or is it a custom one?
Also, from your response it implies that "SOTA" is used in the context of only models published by Clibrain, is that correct? Or is this in comparison with other spanish LLM's?