Instructions to use NeveAI/Neve-Cascade-X3-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeveAI/Neve-Cascade-X3-1B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="NeveAI/Neve-Cascade-X3-1B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NeveAI/Neve-Cascade-X3-1B-GGUF", dtype="auto")

llama-cpp-python

How to use NeveAI/Neve-Cascade-X3-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NeveAI/Neve-Cascade-X3-1B-GGUF",
	filename="Neve-Cascade-X3-1B-Q3_K_XL.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use NeveAI/Neve-Cascade-X3-1B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL
# Run inference directly in the terminal:
llama-cli -hf NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL
# Run inference directly in the terminal:
llama-cli -hf NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL

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 NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL
# Run inference directly in the terminal:
./llama-cli -hf NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL

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 NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL

Use Docker

docker model run hf.co/NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL

LM Studio
Jan

vLLM

How to use NeveAI/Neve-Cascade-X3-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeveAI/Neve-Cascade-X3-1B-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": "NeveAI/Neve-Cascade-X3-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL

SGLang

How to use NeveAI/Neve-Cascade-X3-1B-GGUF 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 "NeveAI/Neve-Cascade-X3-1B-GGUF" \
    --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": "NeveAI/Neve-Cascade-X3-1B-GGUF",
		"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 "NeveAI/Neve-Cascade-X3-1B-GGUF" \
        --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": "NeveAI/Neve-Cascade-X3-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use NeveAI/Neve-Cascade-X3-1B-GGUF with Ollama:
```
ollama run hf.co/NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL
```

Unsloth Studio new

How to use NeveAI/Neve-Cascade-X3-1B-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 NeveAI/Neve-Cascade-X3-1B-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 NeveAI/Neve-Cascade-X3-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NeveAI/Neve-Cascade-X3-1B-GGUF to start chatting

Docker Model Runner
How to use NeveAI/Neve-Cascade-X3-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL
```

Lemonade

How to use NeveAI/Neve-Cascade-X3-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NeveAI/Neve-Cascade-X3-1B-GGUF:Q3_K_XL

Run and chat with the model

lemonade run user.Neve-Cascade-X3-1B-GGUF-Q3_K_XL

List all available models

lemonade list

Neve-Cascade-X3-1B-GGUF

## Introdução

O Neve Cascade X3 é um modelo de linguagem de última geração focado em baixo consumo para hardware limitado. Esta versão em formato GGUF foi otimizada pela NeveAI para oferecer o equilíbrio ideal entre precisão lógica e eficiência computacional.

Destaques do Modelo

Este modelo foi desenvolvido para uso geral e execução de tarefas multimodais, focando em:

Long Context: Suporte a contexto extenso de até 128K tokens, permitindo processamento eficiente de documentos longos e fluxos complexos.
Arquitetura Leve e Escalável: Projetado para oferecer alto desempenho mesmo em ambientes com recursos limitados, incluindo GPUs de consumidor e infraestrutura local.
Cobertura Multilíngue Global: Treinado em mais de 140 idiomas, garantindo forte capacidade de generalização internacional.

Benchmark de Performance

O Neve Cascade X3 apresenta desempenho sólido em benchmarks de reasoning, STEM, multimodalidade e multilingual:

Categoria	Benchmark	Neve Cascade X3	Gemma 3 PT 27B	Gemma 3 PT 12B
Reasoning	BIG-Bench Hard	28.4	77.7	72.6
Knowledge	TriviaQA	39.8	85.5	78.2
STEM	GSM8K	38.4	82.6	71.0
Coding	HumanEval	36.0	48.8	45.7
Multilingual	Global-MMLU-Lite	24.9	75.7	69.4
Multimodal	MMMU	39.2	56.1	50.3

Detalhes da Arquitetura

Arquitetura: Transformer multimodal otimizado para texto e visão.
Contexto: Até 128K tokens de contexto.
Entrada Multimodal: Suporte nativo para texto e imagens em resolução até 896×896.
Treinamento: Infraestrutura baseada em TPU + JAX + ML Pathways.

Como utilizar (GGUF)

Este modelo é compatível com llama.cpp, Ollama, LM Studio e outras ferramentas que suportam o formato GGUF. Foco direcionado ao uso do modelo na plataforma autoral da organização NeveAI

Licença

Este repositório e os pesos do modelo estão licenciados sob a Licença Apache 2.0.

Contato

Se tiver qualquer dúvida, por favor, levante um issue ou entre em contato conosco em NeveIA.

Downloads last month: 93

GGUF

Model size

1.0B params

Architecture

gemma3

Hardware compatibility

3-bit

Model tree for NeveAI/Neve-Cascade-X3-1B-GGUF

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Quantized

(194)

this model

Collection including NeveAI/Neve-Cascade-X3-1B-GGUF

Neve Cascade

Collection

Modelos de baixo consumo para hardware limitado e eficiência extrema. • 2 items • Updated 6 days ago