Instructions to use botbotrobotics/CabraLlama3-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use botbotrobotics/CabraLlama3-70b with Transformers:

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

pipe = pipeline("text-generation", model="botbotrobotics/CabraLlama3-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("botbotrobotics/CabraLlama3-70b")
model = AutoModelForCausalLM.from_pretrained("botbotrobotics/CabraLlama3-70b", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use botbotrobotics/CabraLlama3-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "botbotrobotics/CabraLlama3-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "botbotrobotics/CabraLlama3-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/botbotrobotics/CabraLlama3-70b

SGLang

How to use botbotrobotics/CabraLlama3-70b 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/CabraLlama3-70b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "botbotrobotics/CabraLlama3-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/CabraLlama3-70b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "botbotrobotics/CabraLlama3-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use botbotrobotics/CabraLlama3-70b with Docker Model Runner:
```
docker model run hf.co/botbotrobotics/CabraLlama3-70b
```

CabraLlama3-70b / README.md

nicolasdec

Update README.md

f8b00df verified about 2 years ago

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metadata

language:
  - pt
license: llama3
library_name: transformers
tags:
  - portuguese
  - llama
  - cabra
  - llama-3
datasets:
  - botbot-ai/Cabra3k
model-index:
  - name: CabraLlama3-70b
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: ENEM Challenge (No Images)
          type: eduagarcia/enem_challenge
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 82.02
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BLUEX (No Images)
          type: eduagarcia-temp/BLUEX_without_images
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 70.1
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: OAB Exams
          type: eduagarcia/oab_exams
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 68.52
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Assin2 RTE
          type: assin2
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: f1_macro
            value: 93.21
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Assin2 STS
          type: eduagarcia/portuguese_benchmark
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: pearson
            value: 83.32
            name: pearson
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: FaQuAD NLI
          type: ruanchaves/faquad-nli
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: f1_macro
            value: 80.6
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HateBR Binary
          type: ruanchaves/hatebr
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 81.62
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: PT Hate Speech Binary
          type: hate_speech_portuguese
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 72.72
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: tweetSentBR
          type: eduagarcia/tweetsentbr_fewshot
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 73.85
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=botbot-ai/CabraLlama3-70b
          name: Open Portuguese LLM Leaderboard

Cabra Llama-3 70B

O Cabra Llama-3 70B é uma versão aprimorada do Meta Llama 3 70B Instruct, refinado com o uso do dataset Cabra 30k. Este modelo foi especialmente otimizado para compreender e responder em português (pt-br).

Conheça os nossos outros modelos e datasets, e o Cabra Llama 3 8b.

Detalhes do modelo base

Modelo: Meta-Llama-3-70B-Instruct

A Meta desenvolveu e lançou a família de modelos Llama 3, uma coleção de modelos de texto generativos pré-treinados e ajustados por instruções nos tamanhos de 8B e 70B. Os modelos Llama 3 ajustados por instruções são otimizados para casos de uso em diálogos e superam muitos dos modelos de chat de código aberto disponíveis em benchmarks comuns da indústria. Além disso, ao desenvolver esses modelos, tomamos grande cuidado para otimizar a utilidade e a segurança.

Arquitetura do Modelo: Llama 3 é um modelo de linguagem auto-regressivo que usa uma arquitetura de transformador otimizada. As versões ajustadas utilizam o aprimoramento supervisionado (SFT) e aprendizado por reforço com feedback humano (RLHF) para se alinhar às preferências humanas quanto à utilidade e segurança.

Dataset: Cabra 30k

Dataset interno para fine-tuning. Vamos lançar em breve.

Quantização / GGUF

Colocamos diversas versões (GGUF) quantanizadas no branch "quantanization".

Open Portuguese LLM Leaderboard Evaluation Results

Detailed results can be found here and on the 🚀 Open Portuguese LLM Leaderboard

Metric	Value
Average	78.44
ENEM Challenge (No Images)	82.02
BLUEX (No Images)	70.10
OAB Exams	68.52
Assin2 RTE	93.21
Assin2 STS	83.32
FaQuAD NLI	80.60
HateBR Binary	81.62
PT Hate Speech Binary	72.72
tweetSentBR	73.85