Instructions to use Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix 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 Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix 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 Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix: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 Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix: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 Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio
How to use Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix 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 Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix 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 Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix to start chatting
- Docker Model Runner
How to use Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.Poppy_Porpoise-v0.7-L3-8B-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
- Atomic Chat
New and improved version here:
Prefer the new version 0.72 here!
My upload speeds have been cooked and unstable lately.
Realistically I'd need to move to get a better provider.
If you want and you are able to...
You can support my various endeavors here (Ko-fi).
I apologize for disrupting your experience.
"It keeps getting better!"
"One of the top recent performers in the Chaiverse Leaderboard!"
GGUF-IQ-Imatrix quants for ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B.
Updated! These quants have been redone with the fixes from llama.cpp/pull/6920 in mind.
Use KoboldCpp version 1.64 or higher.
Compatible SillyTavern presets here (recommended/simple)) or here (Virt's).
Use the latest version of KoboldCpp. Use the provided presets.
This is all still highly experimental, let the authors know how it performs for you, feedback is more important than ever now.
For 8GB VRAM GPUs, I recommend the Q4_K_M-imat quant for up to 12288 context sizes.
Original model information:
Update: Vision/multimodal capabilities again!
If you want to use vision functionality:
- You must use the latest versions of Koboldcpp.
To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo. https://huggingface.co/ChaoticNeutrals/Llava_1.5_Llama3_mmproj
- You can load the mmproj by using the corresponding section in the interface:
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