Instructions to use LatitudeGames/Equinox-31B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LatitudeGames/Equinox-31B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LatitudeGames/Equinox-31B-GGUF", filename="Equinox-31B-Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LatitudeGames/Equinox-31B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LatitudeGames/Equinox-31B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LatitudeGames/Equinox-31B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LatitudeGames/Equinox-31B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LatitudeGames/Equinox-31B-GGUF: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 LatitudeGames/Equinox-31B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LatitudeGames/Equinox-31B-GGUF: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 LatitudeGames/Equinox-31B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LatitudeGames/Equinox-31B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LatitudeGames/Equinox-31B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use LatitudeGames/Equinox-31B-GGUF with Ollama:
ollama run hf.co/LatitudeGames/Equinox-31B-GGUF:Q4_K_M
- Unsloth Studio
How to use LatitudeGames/Equinox-31B-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 LatitudeGames/Equinox-31B-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 LatitudeGames/Equinox-31B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LatitudeGames/Equinox-31B-GGUF to start chatting
- Pi
How to use LatitudeGames/Equinox-31B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf LatitudeGames/Equinox-31B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "LatitudeGames/Equinox-31B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use LatitudeGames/Equinox-31B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf LatitudeGames/Equinox-31B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default LatitudeGames/Equinox-31B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use LatitudeGames/Equinox-31B-GGUF with Docker Model Runner:
docker model run hf.co/LatitudeGames/Equinox-31B-GGUF:Q4_K_M
- Lemonade
How to use LatitudeGames/Equinox-31B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LatitudeGames/Equinox-31B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Equinox-31B-GGUF-Q4_K_M
List all available models
lemonade list
iq3_xs spits out gibberish
Tested with the latest version of llama cpp, no reasoning template
Would you mind checking whether https://huggingface.co/bartowski/LatitudeGames_Equinox-31B-GGUF/tree/main 's quants suffer from the same issue?
I know Google models are weirdly fussy about what tensors get quanted below q8 but I couldn't tell you exactly which offhand
Edit: I think it's *.attn_q.weight? And you don't wanna go below q6_k ?
Idk a lot of the quant cookers keep their analysis to themselves
Not the person you asked, but I noticed an immediate and clear difference in writing quality, word choice, and scene tracking switching from the Latitude Q5 to Bartowski Q5. (But I did also go from Q5-M to Q5-L... but I'm not sure that would account for the improvement.) Maybe his imatrix thingy is super special?
EDIT: I'm using LMStudio, beta with beta runtime. Serving to SillyTavern. Settings as listed in model card.
Both do work, though. No gibberish.
Would you mind checking whether https://huggingface.co/bartowski/LatitudeGames_Equinox-31B-GGUF/tree/main 's quants suffer from the same issue?
I checked it out. The same problem
Checked latest version of llamacpp?
Checked you're using a sampler either suggested by latitude or by Google? (Temp 1 too p 0.95 top k 64 min p 0.0 for the latter)
Checked the sga 256 hash? LLM models esp gguf can be loaded and inferenced even if they've gotten corrupted in download.
I can confirm that the iq quants are completely broken, Bartowski's too. QKM works just fine.
I went ahead and deleted all the IQ quants for now.
IIRC all quants with IQ3_S tensors (so basically every IQ3) have issues with llama.cpp compiled with CUDA 13.2 and won't work correctly. Recompiling llama.cpp with an unaffected CUDA version should fix it. But apparently it affects LMStudio?
Can confirm. SHA 256 sum matches.
https://huggingface.co/mradermacher/Equinox-31B-i1-GGUF/blob/main/Equinox-31B.i1-IQ3_M.gguf
Update: llama.cpp, Windows x64 (CUDA 12) - CUDA 12.4 DLLs