Instructions to use wolfram/miquliz-120b-v2.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wolfram/miquliz-120b-v2.0-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wolfram/miquliz-120b-v2.0-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use wolfram/miquliz-120b-v2.0-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 wolfram/miquliz-120b-v2.0-GGUF:IQ1_S # Run inference directly in the terminal: llama cli -hf wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf wolfram/miquliz-120b-v2.0-GGUF:IQ1_S # Run inference directly in the terminal: llama cli -hf wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
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 wolfram/miquliz-120b-v2.0-GGUF:IQ1_S # Run inference directly in the terminal: ./llama-cli -hf wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
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 wolfram/miquliz-120b-v2.0-GGUF:IQ1_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
Use Docker
docker model run hf.co/wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
- LM Studio
- Jan
- Ollama
How to use wolfram/miquliz-120b-v2.0-GGUF with Ollama:
ollama run hf.co/wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
- Unsloth Studio
How to use wolfram/miquliz-120b-v2.0-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 wolfram/miquliz-120b-v2.0-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 wolfram/miquliz-120b-v2.0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for wolfram/miquliz-120b-v2.0-GGUF to start chatting
- Docker Model Runner
How to use wolfram/miquliz-120b-v2.0-GGUF with Docker Model Runner:
docker model run hf.co/wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
- Lemonade
How to use wolfram/miquliz-120b-v2.0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull wolfram/miquliz-120b-v2.0-GGUF:IQ1_S
Run and chat with the model
lemonade run user.miquliz-120b-v2.0-GGUF-IQ1_S
List all available models
lemonade list
- Atomic Chat
Upload folder using huggingface_hub
Upload folder using huggingface_hub
Multi commit ID: 09990a224fc355c32f95e419639e09ab23f004f8d801a59765a508f254732f8f
Scheduled commits:
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- Upload 1 file(s) totalling 35.4G (a4b56e8c294faecd13d5f45e857854285620e1eb62d38d0753b156463d717d42)
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- Upload 1 file(s) totalling 49.0G (a8a56c617bb530fa5db44cf2f686897eeef8b3071b4b367b35faf18ae7ccc390)
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- Upload 1 file(s) totalling 44.2G (ac1eece2ee213c1d4f9d79376edfe1ec6dec7a8ba2f3ab95f21cd4c4da8367f7)
- Upload 1 file(s) totalling 50.0G (3708c9c408edaf63c48d3ab300272edc8bfb1e18b5a5664d0dc3db910c7a807e)
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- Upload 1 file(s) totalling 50.0G (9f60591bcd282e64c05d9f7ed779d1a55dbea21ab12fe1304a24f725645a5d21)
- Upload 1 file(s) totalling 35.0G (cdab53220bd6eb5cfbfd4b61d1724c4ea1d52f25f115315347a3da3403e09e57)
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