Instructions to use maxnocker/model 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 maxnocker/model 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 maxnocker/model:Q4_K_M # Run inference directly in the terminal: llama cli -hf maxnocker/model:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf maxnocker/model:Q4_K_M # Run inference directly in the terminal: llama cli -hf maxnocker/model: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 maxnocker/model:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf maxnocker/model: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 maxnocker/model:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf maxnocker/model:Q4_K_M
Use Docker
docker model run hf.co/maxnocker/model:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use maxnocker/model with Ollama:
ollama run hf.co/maxnocker/model:Q4_K_M
- Unsloth Studio
How to use maxnocker/model 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 maxnocker/model 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 maxnocker/model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for maxnocker/model to start chatting
- Docker Model Runner
How to use maxnocker/model with Docker Model Runner:
docker model run hf.co/maxnocker/model:Q4_K_M
- Lemonade
How to use maxnocker/model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull maxnocker/model:Q4_K_M
Run and chat with the model
lemonade run user.model-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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tags:
- gguf
- llama.cpp
- unsloth
- vision-language-model
---
# model - 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** repo_id/model_name **-p** "why is the sky blue?"
- For multimodal models: **llama-mtmd-cli** **-m** model_name.gguf **--mmproj** mmproj_file.gguf
## Available Model files:
- `Qwen2.5-VL-7B-Instruct.Q5_K_M.gguf`
- `Qwen2.5-VL-7B-Instruct.Q8_0.gguf`
- `Qwen2.5-VL-7B-Instruct.Q4_K_M.gguf`
- `Qwen2.5-VL-7B-Instruct.BF16-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.
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