Instructions to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/CabraMistral-v3-7b-32k-GGUF",
	filename="CabraMistral-v3-7b-32k.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/CabraMistral-v3-7b-32k-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/CabraMistral-v3-7b-32k-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M

Ollama
How to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/CabraMistral-v3-7b-32k-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/CabraMistral-v3-7b-32k-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/CabraMistral-v3-7b-32k-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/CabraMistral-v3-7b-32k-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/CabraMistral-v3-7b-32k-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CabraMistral-v3-7b-32k-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/CabraMistral-v3-7b-32k-GGUF

This is quantized version of botbot-ai/CabraMistral-v3-7b-32k created using llama.cpp

Model Description

Esse modelo é um finetune do Mistral 7b Instruct 0.3 com o dataset BotBot Cabra 10k. Esse modelo é optimizado para português.

Conheça os nossos outros modelos: Cabra.

Detalhes do Modelo

Modelo: Mistral 7b Instruct 0.3

Mistral-7B-v0.3 é um modelo de transformador, com as seguintes escolhas arquitetônicas:

Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer

dataset: Cabra 10k

Dataset interno para finetuning. Vamos lançar em breve.

Exemplo

<s> [INST] who is Elon Musk? [/INST]Elon Musk é um empreendedor, inventor e capitalista americano. Ele é o fundador, CEO e CTO da SpaceX, CEO da Neuralink e fundador do The Boring Company. Musk também é o proprietário do Twitter.</s>

Paramentros de trainamento

- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 3

Framework

Transformers 4.39.0.dev0
Pytorch 2.1.2+cu118
Datasets 2.14.6
Tokenizers 0.15.2

Evals

Open Portuguese LLM Leaderboard Evaluation Results

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

Metric	Value
Average	60.66
ENEM Challenge (No Images)	58.64
BLUEX (No Images)	45.62
OAB Exams	41.46
Assin2 RTE	86.14
Assin2 STS	68.06
FaQuAD NLI	47.46
HateBR Binary	70.46
PT Hate Speech Binary	62.39
tweetSentBR	65.71

Downloads last month: 14

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/CabraMistral-v3-7b-32k-GGUF

Base model

botbotrobotics/CabraMistral-v3-7b-32k

Quantized

(2)

this model

Evaluation results

accuracy on ENEM Challenge (No Images)
Open Portuguese LLM Leaderboard

58.640
accuracy on BLUEX (No Images)
Open Portuguese LLM Leaderboard

45.620
accuracy on OAB Exams
Open Portuguese LLM Leaderboard

41.460
f1-macro on Assin2 RTE
test set Open Portuguese LLM Leaderboard

86.140
pearson on Assin2 STS
test set Open Portuguese LLM Leaderboard

68.060
f1-macro on FaQuAD NLI
test set Open Portuguese LLM Leaderboard

47.460
f1-macro on HateBR Binary
test set Open Portuguese LLM Leaderboard

70.460
f1-macro on PT Hate Speech Binary
test set Open Portuguese LLM Leaderboard

62.390