Instructions to use lucianosb/boto-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lucianosb/boto-9B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lucianosb/boto-9B-GGUF", dtype="auto")

llama-cpp-python

How to use lucianosb/boto-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lucianosb/boto-9B-GGUF",
	filename="boto9b.BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lucianosb/boto-9B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lucianosb/boto-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lucianosb/boto-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lucianosb/boto-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf lucianosb/boto-9B-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 lucianosb/boto-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf lucianosb/boto-9B-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 lucianosb/boto-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lucianosb/boto-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/lucianosb/boto-9B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use lucianosb/boto-9B-GGUF with Ollama:
```
ollama run hf.co/lucianosb/boto-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use lucianosb/boto-9B-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 lucianosb/boto-9B-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 lucianosb/boto-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lucianosb/boto-9B-GGUF to start chatting

Docker Model Runner
How to use lucianosb/boto-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/lucianosb/boto-9B-GGUF:Q4_K_M
```

Lemonade

How to use lucianosb/boto-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lucianosb/boto-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.boto-9B-GGUF-Q4_K_M

List all available models

lemonade list

Boto 9B - GGUF

Criador do Modelo: Luciano Santa Brígida
Modelo Original: Boto-9B

Boto-9B é um modelo de linguagem de 9 bilhões de parâmetros, otimizado a partir do Gemma2-9B.

Confira os presets para usar com LM Studio.

Arquivos Incluídos

Nome	Método Quant	Bits	Tamanho	Desc
boto9b.Q2_K.gguf	q2_K	2	3.81 GB	Quantização em 2-bit. Significativa perda de qualidade. Não-recomendado.
boto9b.Q3_K_M.gguf	q3_K_M	3	4.76 GB	Quantização em 3-bit.
boto9b.Q3_K_S.gguf	q3_K_S	3	4.34 GB	Quantização em 3-bit.
boto9b.Q4_0.gguf	q4_0	4	5.44 GB	Quantização em 4-bit. Prefira usar o Q3_K_M
boto9b.Q4_K_S.gguf	q4_K_S	4	5.48 GB	Quantização em 4-bit.
boto9b.Q3_K_L.gguf	q3_K_L	3	5.13 GB	Quantização em 3-bit com menor perda de qualidade.
boto9b.Q4_K_M.gguf	q4_K_M	4	5.76 GB	Quantização em 4-bit.
boto9b.Q4_1.gguf	q4_1	4	5.96 GB	Quantização em 4-bit. Acurácia maior que q4_0 mas não tão boa quanto q5_0. Inferência mais rápida que os modelos q5.
boto9b.Q5_0.gguf	q5_0	5	6.48 GB	Quantização em 5-bit. Melhor acurácia, maior uso de recursos, inferência mais lenta.
boto9b.Q5_1.gguf	q5_1	5	7 GB	Quantização em 5-bit. Ainda Melhor acurácia, maior uso de recursos, inferência mais lenta.
boto9b.Q5_K_M.gguf	q5_K_M	5	6.65 GB	Quantização em 5-bit. Melhor performance. Recomendado.
boto9b.Q5_K_S.gguf	q5_K_S	5	6.48 GB	Quantização em 5-bit.
boto9b.Q6_K.gguf	q6_K	6	7.59 GB	Quantização em 6-bit.
boto9b.Q8_0.gguf	q8_0	8	9.83 GB	Quantização em 8-bit. Quase indistinguível do float16. Usa muitos recursos e é mais lento.

Observação: os valores de RAM acima não pressupõem descarregamento de GPU. Se as camadas forem descarregadas para a GPU, isso reduzirá o uso de RAM e usará VRAM.

Template

### Instrução:
{prompt}

### Resposta:

Uploaded model

Developed by: lucianosb
License: apache-2.0
Finetuned from model : unsloth/gemma-2-9b-bnb-4bit

This gemma2 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 747

GGUF

Model size

9B params

Architecture

gemma2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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