Instructions to use dranger003/miquliz-120b-v2.0-iMat.GGUF 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 dranger003/miquliz-120b-v2.0-iMat.GGUF 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 dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS # Run inference directly in the terminal: llama cli -hf dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS # Run inference directly in the terminal: llama cli -hf dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS # Run inference directly in the terminal: ./build/bin/llama cli -hf dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS
Use Docker
docker model run hf.co/dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS
- Llama new
- LM Studio
- Jan
- Ollama
How to use dranger003/miquliz-120b-v2.0-iMat.GGUF with Ollama:
ollama run hf.co/dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS
- Unsloth Desktop
- Docker Model Runner
How to use dranger003/miquliz-120b-v2.0-iMat.GGUF with Docker Model Runner:
docker model run hf.co/dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS
- Lemonade
How to use dranger003/miquliz-120b-v2.0-iMat.GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dranger003/miquliz-120b-v2.0-iMat.GGUF:IQ2_XS
Run and chat with the model
lemonade run user.miquliz-120b-v2.0-iMat.GGUF-IQ2_XS
List all available models
lemonade list
- Atomic Chat
GGUF importance matrix (imatrix) quants for https://huggingface.co/wolfram/miquliz-120b-v2.0
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.
Using IQ2_XXS it seems to fit 100/141 layers using 2K context on a 24GB card.
| Layers | Context | Template |
|---|---|---|
140 |
32768 |
[INST] {prompt} [/INST] |
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