Instructions to use NeveAI/Neve-Strata-S2-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NeveAI/Neve-Strata-S2-4B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="NeveAI/Neve-Strata-S2-4B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("NeveAI/Neve-Strata-S2-4B-GGUF", dtype="auto")

llama-cpp-python

How to use NeveAI/Neve-Strata-S2-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="NeveAI/Neve-Strata-S2-4B-GGUF",
	filename="Neve-Strata-S2-4B-Q8_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-Strata-S2-4B-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-Strata-S2-4B-GGUF:Q8_K_XL
# Run inference directly in the terminal:
llama-cli -hf NeveAI/Neve-Strata-S2-4B-GGUF:Q8_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-Strata-S2-4B-GGUF:Q8_K_XL
# Run inference directly in the terminal:
llama-cli -hf NeveAI/Neve-Strata-S2-4B-GGUF:Q8_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-Strata-S2-4B-GGUF:Q8_K_XL
# Run inference directly in the terminal:
./llama-cli -hf NeveAI/Neve-Strata-S2-4B-GGUF:Q8_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-Strata-S2-4B-GGUF:Q8_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL

Use Docker

docker model run hf.co/NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL

LM Studio
Jan

vLLM

How to use NeveAI/Neve-Strata-S2-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NeveAI/Neve-Strata-S2-4B-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-Strata-S2-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL

SGLang

How to use NeveAI/Neve-Strata-S2-4B-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-Strata-S2-4B-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-Strata-S2-4B-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-Strata-S2-4B-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-Strata-S2-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use NeveAI/Neve-Strata-S2-4B-GGUF with Ollama:
```
ollama run hf.co/NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL
```

Unsloth Studio new

How to use NeveAI/Neve-Strata-S2-4B-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-Strata-S2-4B-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-Strata-S2-4B-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-Strata-S2-4B-GGUF to start chatting

Pi new

How to use NeveAI/Neve-Strata-S2-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use NeveAI/Neve-Strata-S2-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL

Run Hermes

hermes

Docker Model Runner
How to use NeveAI/Neve-Strata-S2-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL
```

Lemonade

How to use NeveAI/Neve-Strata-S2-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NeveAI/Neve-Strata-S2-4B-GGUF:Q8_K_XL

Run and chat with the model

lemonade run user.Neve-Strata-S2-4B-GGUF-Q8_K_XL

List all available models

lemonade list

Neve-Strata-S2-4B-GGUF

Introdução

O Neve Strata S2 é um modelo de linguagem de última geração focado em programação e raciocínio para execução em escala. 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 diversas, focando em:

Unified Multimodal Understanding: Treinamento com fusão antecipada de tokens multimodais, garantindo forte desempenho em tarefas de texto e compreensão visual.
Arquitetura Híbrida Eficiente: Combinação de Gated Delta Networks com Mixture-of-Experts, proporcionando alta performance com baixa latência.
Raciocínio e Generalização: Otimizado com técnicas avançadas de reinforcement learning para lidar com tarefas complexas e cenários do mundo real.
Cobertura Multilíngue Global: Suporte expandido para múltiplos idiomas, garantindo aplicação ampla em diferentes contextos culturais e linguísticos.

Benchmark de Performance

O Neve Strata S2 apresenta desempenho sólido em benchmarks de conhecimento, raciocínio e tarefas gerais:

Categoria	Benchmark	Neve Strata S2	Qwen3.5-4B
Knowledge	MMLU-Pro	82.5	79.1
Knowledge	MMLU-Redux	91.1	88.8
Reasoning	GPQA Diamond	81.7	76.2
Instruction	IFEval	91.5	89.8
Long Context	LongBench v2	55.2	50.0
Agent / Tool Use	TAU2-Bench	79.1	79.9

Detalhes da Arquitetura

Arquitetura: Gated DeltaNet + Mixture of Experts (MoE).
Parâmetros: ~4B parâmetros.
Janela de Contexto: 262.144 tokens nativos (extensível até ~1M).
Camadas: 32 camadas com estrutura híbrida intercalando DeltaNet e Attention.
Multimodalidade: Suporte a texto e visão com encoder integrado.

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: 67

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

8-bit

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