Instructions to use Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lewdiculous/Eris-Daturamix-7b-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/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Eris-Daturamix-7b-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/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/Eris-Daturamix-7b-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/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/Eris-Daturamix-7b-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/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio
How to use Lewdiculous/Eris-Daturamix-7b-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/Eris-Daturamix-7b-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/Eris-Daturamix-7b-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/Eris-Daturamix-7b-GGUF-IQ-Imatrix to start chatting
- Docker Model Runner
How to use Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/Eris-Daturamix-7b-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.Eris-Daturamix-7b-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
- Atomic Chat
This repository hosts GGUF-Imatrix quantizations for Test157t/Eris-Daturamix-7b.
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
quantization_options = [
"Q4_K_M", "IQ4_XS", "Q5_K_M", "Q6_K",
"Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
]
Relevant information:
For imatrix data generation, kalomaze's groups_merged.txt with added roleplay chats was used, you can find it here.
There's already a v2 of this model here.
Original model information:
The following models were included in the merge:
Configuration
slices:
- sources:
- model: Test157t/Eris-Floramix-7b
layer_range: [0, 32]
- model: ResplendentAI/Datura_7B
layer_range: [0, 32]
merge_method: slerp
base_model: Test157t/Eris-Floramix-7b
parameters:
t:
- filter: self_attn
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
- Downloads last month
- 126
3-bit
4-bit
5-bit
6-bit
8-bit
16-bit
