Instructions to use botbotrobotics/Cabra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use botbotrobotics/Cabra with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="botbotrobotics/Cabra")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("botbotrobotics/Cabra") model = AutoModelForCausalLM.from_pretrained("botbotrobotics/Cabra") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use botbotrobotics/Cabra with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "botbotrobotics/Cabra" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "botbotrobotics/Cabra", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/botbotrobotics/Cabra
- SGLang
How to use botbotrobotics/Cabra 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 "botbotrobotics/Cabra" \ --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": "botbotrobotics/Cabra", "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 "botbotrobotics/Cabra" \ --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": "botbotrobotics/Cabra", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use botbotrobotics/Cabra with Docker Model Runner:
docker model run hf.co/botbotrobotics/Cabra
Fixing some errors of the leaderboard evaluation results in the ModelCard yaml
Browse filesThe name of a few benchmarks are incorrect on the model metadata. This commit fixes some minor errors of the [last PR](1) on the ModelCard YAML metadata.
README.md
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@@ -67,6 +67,19 @@ model-index:
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- type: f1_macro
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value: 81.12
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name: f1-macro
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- type: pearson
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value: 29.38
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name: pearson
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name: Text Generation
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dataset:
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name: HateBR Binary
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type:
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split: test
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args:
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num_few_shot: 25
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- type: f1_macro
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value: 81.71
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name: f1-macro
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- type: f1_macro
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value: 64.84
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name: f1-macro
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- type: f1_macro
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value: 81.12
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name: f1-macro
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicolasdec/Cabra
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Assin2 STS
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type: eduagarcia/portuguese_benchmark
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split: test
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args:
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num_few_shot: 15
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metrics:
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- type: pearson
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value: 29.38
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name: pearson
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name: Text Generation
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dataset:
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name: HateBR Binary
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type: ruanchaves/hatebr
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split: test
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args:
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num_few_shot: 25
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- type: f1_macro
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value: 81.71
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name: f1-macro
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source:
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=nicolasdec/Cabra
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name: Open Portuguese LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: PT Hate Speech Binary
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type: hate_speech_portuguese
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: f1_macro
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value: 64.84
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name: f1-macro
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