Instructions to use Lewdiculous/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M
- Ollama
How to use Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio
How to use Lewdiculous/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-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/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix to start chatting
- Docker Model Runner
How to use Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.Erosumika-7B-v3-0.2-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
- Atomic Chat
This repo contains GGUF-IQ-Imatrix quantized model files for Erosumika-7B-v3-0.2.
Recommended starting SillyTavern presets here.
Quants:
"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
"Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
What does "Imatrix" mean?
It stands for Importance Matrix, a technique used to improve the quality of quantized models. The Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse. [1] [2]
For imatrix data generation, kalomaze's groups_merged.txt with added roleplay chats was used, you can find it here. This was just to add a bit more diversity to the data.
Steps:
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
Using the latest llama.cpp at the time.
Original model information:
Erosumika-7B-v3-0.2
~Mistral 0.2 Edition~
Model Details
The Mistral 0.2 version of Erosumika-7B-v3, a DARE TIES merge between Nitral's Kunocchini-7b, Endevor's InfinityRP-v1-7B and my FlatErosAlpha, a flattened(in order to keep the vocab size 32000) version of tavtav's eros-7B-ALPHA. Alpaca and ChatML work best. Slightly smarter and better prompt comprehension than Mistral 0.1 Erosumika-7B-v3. 32k context should work.
Limitations and biases
The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope. It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
merge_method: task_arithmetic
base_model: alpindale/Mistral-7B-v0.2-hf
models:
- model: localfultonextractor/Erosumika-7B-v3
parameters:
weight: 1.0
dtype: float16
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