Instructions to use NeveAI/Neve-Prism-3-12B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NeveAI/Neve-Prism-3-12B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="NeveAI/Neve-Prism-3-12B-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NeveAI/Neve-Prism-3-12B-GGUF", dtype="auto") - llama-cpp-python
How to use NeveAI/Neve-Prism-3-12B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="NeveAI/Neve-Prism-3-12B-GGUF", filename="Neve-Prism-3-12B-Q5_K_XL.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use NeveAI/Neve-Prism-3-12B-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-Prism-3-12B-GGUF:Q5_K_XL # Run inference directly in the terminal: llama-cli -hf NeveAI/Neve-Prism-3-12B-GGUF:Q5_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-Prism-3-12B-GGUF:Q5_K_XL # Run inference directly in the terminal: llama-cli -hf NeveAI/Neve-Prism-3-12B-GGUF:Q5_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-Prism-3-12B-GGUF:Q5_K_XL # Run inference directly in the terminal: ./llama-cli -hf NeveAI/Neve-Prism-3-12B-GGUF:Q5_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-Prism-3-12B-GGUF:Q5_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf NeveAI/Neve-Prism-3-12B-GGUF:Q5_K_XL
Use Docker
docker model run hf.co/NeveAI/Neve-Prism-3-12B-GGUF:Q5_K_XL
- LM Studio
- Jan
- vLLM
How to use NeveAI/Neve-Prism-3-12B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NeveAI/Neve-Prism-3-12B-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-Prism-3-12B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/NeveAI/Neve-Prism-3-12B-GGUF:Q5_K_XL
- SGLang
How to use NeveAI/Neve-Prism-3-12B-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-Prism-3-12B-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-Prism-3-12B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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-Prism-3-12B-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-Prism-3-12B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use NeveAI/Neve-Prism-3-12B-GGUF with Ollama:
ollama run hf.co/NeveAI/Neve-Prism-3-12B-GGUF:Q5_K_XL
- Unsloth Studio new
How to use NeveAI/Neve-Prism-3-12B-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-Prism-3-12B-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-Prism-3-12B-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-Prism-3-12B-GGUF to start chatting
- Docker Model Runner
How to use NeveAI/Neve-Prism-3-12B-GGUF with Docker Model Runner:
docker model run hf.co/NeveAI/Neve-Prism-3-12B-GGUF:Q5_K_XL
- Lemonade
How to use NeveAI/Neve-Prism-3-12B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NeveAI/Neve-Prism-3-12B-GGUF:Q5_K_XL
Run and chat with the model
lemonade run user.Neve-Prism-3-12B-GGUF-Q5_K_XL
List all available models
lemonade list
| library_name: transformers | |
| license: apache-2.0 | |
| pipeline_tag: image-text-to-text | |
| base_model: google/gemma-3-12b-it | |
| tags: | |
| - NeveAI | |
| - Neve | |
| - Prism | |
| <div align="center"> | |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/68a3ba234a7dfca33d72eee2/BG71IO9zlNcw4eTRYKZzO.png" width="50%"> | |
| </div> | |
| <h1 align="center">Neve-Prism-3-12B-GGUF</h1> | |
| <div align="center"> | |
| <a href="https://github.com/NeveIA"> | |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/68a3ba234a7dfca33d72eee2/KQa7-ajynUAhTS-kxNtYT.png" width="20%" alt="NeveAI GitHub"> | |
| </a> | |
| </div> | |
| ## Introdução | |
| O **Neve Prism 3** é um modelo de linguagem de última geração focado em **visão para análise de imagens em alta precisão**. 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: | |
| * **Treinamento de Alta Escala (JAX + Pathways):** Construído com infraestrutura moderna que permite treinamento eficiente e altamente escalável em múltiplos dispositivos. | |
| * **Raciocínio e Factualidade:** Forte desempenho em tarefas de compreensão, lógica e conhecimento geral. | |
| * **Multimodalidade:** Capaz de lidar com tarefas envolvendo texto e visão, incluindo compreensão de documentos, imagens e dados estruturados. | |
| * **Generalização Multitarefa:** Projetado como modelo foundation, com capacidade de adaptação a múltiplos domínios e tipos de tarefa. | |
| ## Benchmark de Performance | |
| O Neve Prism 3 apresenta desempenho consistente em benchmarks de raciocínio, STEM e multimodalidade: | |
| | Categoria | Benchmark | Neve Prism 3 | Gemma 3 4B | | |
| | :--- | :--- | :---: | :---: | | |
| | **Reasoning** | HellaSwag | **84.2** | 77.2 | | |
| | **Knowledge** | TriviaQA | **78.2** | 65.8 | | |
| | **Reasoning** | BIG-Bench Hard | **72.6** | 50.9 | | |
| | **Math** | GSM8K | **82.6** | 71.0 | | |
| | **STEM** | MMLU | **74.5** | 59.6 | | |
| | **Coding** | HumanEval | **48.8** | 45.7 | | |
| | **Multilingual** | Global-MMLU-Lite | **69.4** | 57.0 | | |
| | **Multimodal** | MMMU | **50.3** | 39.2 | | |
| --- | |
| ## Detalhes da Arquitetura | |
| - **Arquitetura:** Transformer multimodal otimizado para foundation tasks. | |
| - **Treinamento:** JAX + ML Pathways (orquestração distribuída em larga escala). | |
| - **Parâmetros:** Classe média-alta (escalável entre diferentes tamanhos). | |
| - **Foco:** Raciocínio, conhecimento factual e tarefas multimodais. | |
| - **Capacidades:** Suporte a texto, visão e tarefas complexas de múltiplos domínios. | |
| ## 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](https://github.com/Etamus/NeveAI) | |
| ## Licença | |
| Este repositório e os pesos do modelo estão licenciados sob a [Licença Apache 2.0](LICENSE). | |
| ## Contato | |
| Se tiver qualquer dúvida, por favor, levante um issue ou entre em contato conosco em [NeveIA](https://github.com/NeveIA). |