Instructions to use gelukuMLG/L3-Theta-Cat-2x8B-Bf16-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 gelukuMLG/L3-Theta-Cat-2x8B-Bf16-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 gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf gelukuMLG/L3-Theta-Cat-2x8B-Bf16-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 gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf gelukuMLG/L3-Theta-Cat-2x8B-Bf16-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 gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M
Use Docker
docker model run hf.co/gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF with Ollama:
ollama run hf.co/gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M
- Unsloth Studio
How to use gelukuMLG/L3-Theta-Cat-2x8B-Bf16-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 gelukuMLG/L3-Theta-Cat-2x8B-Bf16-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 gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF to start chatting
- Docker Model Runner
How to use gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF with Docker Model Runner:
docker model run hf.co/gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M
- Lemonade
How to use gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull gelukuMLG/L3-Theta-Cat-2x8B-Bf16-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.L3-Theta-Cat-2x8B-Bf16-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
This is an experimental 2x8B moe with random gates, using the following 2 models
Hermes-2-Theta-l3-8B by Nous Research https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B
llama-3-cat-8B-instruct-V1 by TheSkullery https://huggingface.co/TheSkullery/llama-3-cat-8b-instruct-v1
Important
Make sure to add </s> a stop sequence as it uses llama-3-cat-8B-instruct-V1 as the base model.
Update:
Due to request i decided to add the rest of the quants. Enjoy
Mergekit recipe of the model if too lazy to check the files:
base_model: TheSkullery/llama-3-cat-8b-instruct-v1
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: TheSkullery/llama-3-cat-8b-instruct-v1
positive_prompts:
- " "
- source_model: NousResearch/Hermes-2-Theta-Llama-3-8B
positive_prompts:
- " "
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