Instructions to use BeaverAI/Anubis-Mini-8B-v1f-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use BeaverAI/Anubis-Mini-8B-v1f-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="BeaverAI/Anubis-Mini-8B-v1f-GGUF", filename="Anubis-Mini-8B-v1f-Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use BeaverAI/Anubis-Mini-8B-v1f-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf BeaverAI/Anubis-Mini-8B-v1f-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 BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf BeaverAI/Anubis-Mini-8B-v1f-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 BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf BeaverAI/Anubis-Mini-8B-v1f-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 BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M
Use Docker
docker model run hf.co/BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use BeaverAI/Anubis-Mini-8B-v1f-GGUF with Ollama:
ollama run hf.co/BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M
- Unsloth Studio new
How to use BeaverAI/Anubis-Mini-8B-v1f-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 BeaverAI/Anubis-Mini-8B-v1f-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 BeaverAI/Anubis-Mini-8B-v1f-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BeaverAI/Anubis-Mini-8B-v1f-GGUF to start chatting
- Docker Model Runner
How to use BeaverAI/Anubis-Mini-8B-v1f-GGUF with Docker Model Runner:
docker model run hf.co/BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M
- Lemonade
How to use BeaverAI/Anubis-Mini-8B-v1f-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull BeaverAI/Anubis-Mini-8B-v1f-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Anubis-Mini-8B-v1f-GGUF-Q4_K_M
List all available models
lemonade list
Serious question: What's going on here (in general)? π³
Hello guys, thank you for this new model. I guess it is based on Llama 3.3 8B? That's very nice and appreciated! β€
I have a question though. Are you guys like the last the Mohicans of E/RP finetuning? π₯
Seriously, this is concerning, because up to this day, I haven't seen anyone else do any finetune of Ministral 14B except you. And that's a very good model, I thought E/RP finetuners would absolutely love its ability to follow instructions!
Also, so far I haven't seen anyone do any finetune of Llama 3.3 8B except you.
Now, I know that Llama 3.3 8B was released coughleakedcough just yesterday, but if it follows the same path as Ministral 14B, then there won't be too many finetunes for it either.
What's going on? Is E/RP finetuning dead? π₯
Yes, finetuning is dying, nowadays many models are increasingly benchmaxxed and focused on assistant/agent tasks. It's sad.
Part of it is general attention fatigue.
Speficially here we find diminishing returns of novelty to biasing a small model towards different styles of prose when no new capability is being introduced.
There's also some kind of delicate flower of emergent reasoning power that the best small frontier models manage to express, and which gets 'fuzzed' by post-training processing (merges, distils, reaps). My tests for logical inference reveal this quite clearly.
Just my $.02