Instructions to use kvfs/NFA-Panel-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kvfs/NFA-Panel-AI 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 kvfs/NFA-Panel-AI:F16 # Run inference directly in the terminal: llama cli -hf kvfs/NFA-Panel-AI:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kvfs/NFA-Panel-AI:F16 # Run inference directly in the terminal: llama cli -hf kvfs/NFA-Panel-AI:F16
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 kvfs/NFA-Panel-AI:F16 # Run inference directly in the terminal: ./llama-cli -hf kvfs/NFA-Panel-AI:F16
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 kvfs/NFA-Panel-AI:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kvfs/NFA-Panel-AI:F16
Use Docker
docker model run hf.co/kvfs/NFA-Panel-AI:F16
- LM Studio
- Jan
- Ollama
How to use kvfs/NFA-Panel-AI with Ollama:
ollama run hf.co/kvfs/NFA-Panel-AI:F16
- Unsloth Desktop
- Docker Model Runner
How to use kvfs/NFA-Panel-AI with Docker Model Runner:
docker model run hf.co/kvfs/NFA-Panel-AI:F16
- Lemonade
How to use kvfs/NFA-Panel-AI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kvfs/NFA-Panel-AI:F16
Run and chat with the model
lemonade run user.NFA-Panel-AI-F16
List all available models
lemonade list
- Atomic Chat
File size: 1,133 Bytes
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tags:
- gguf
- llama.cpp
- unsloth
- vision-language-model
---
# NFA-Panel-AI : GGUF
This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
**Example usage**:
- For text only LLMs: `llama-cli -hf kvfs/NFA-Panel-AI --jinja`
- For multimodal models: `llama-mtmd-cli -hf kvfs/NFA-Panel-AI --jinja`
## Available Model files:
- `Qwen2.5-VL-7B-Instruct.Q4_K_M.gguf`
- `Qwen2.5-VL-7B-Instruct.F16-mmproj.gguf`
## ⚠️ Ollama Note for Vision Models
**Important:** Ollama currently does not support separate mmproj files for vision models.
To create an Ollama model from this vision model:
1. Place the `Modelfile` in the same directory as the finetuned bf16 merged model
3. Run: `ollama create model_name -f ./Modelfile`
(Replace `model_name` with your desired name)
This will create a unified bf16 model that Ollama can use.
This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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