Instructions to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF", filename="Gemma-2-Ataraxy-v4d-9B.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-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 QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Gemma-2-Ataraxy-v4d-9B-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 QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Gemma-2-Ataraxy-v4d-9B-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 QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Gemma-2-Ataraxy-v4d-9B-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 QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-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 QuantFactory/Gemma-2-Ataraxy-v4d-9B-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 QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Gemma-2-Ataraxy-v4d-9B-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/Gemma-2-Ataraxy-v4d-9B-GGUF
This is quantized version of lemon07r/Gemma-2-Ataraxy-v4d-9B created using llama.cpp
Original Model Card
Gemma-2-Ataraxy-v4d-9B
For all intents and purposes, we can consider this the best "all rounder" of the Ataraxy. While primarily made with creative writing in mind, this one has done well in testing, and was made based on a lot of what I've discovered through trial, experimentation, testing and feedback from others.
Is this the best Ataraxy model? Not sure. I made a lot of variations, and quite honestly most of them aren't great, or at least as good as the very first version. The v2 series could do well in writing tests, but was a little too over the top and sloppy. The v3 series was a return to roots, and is a lot closer to v1, and can be considered v1 but slightly better or different and where we start to see some improvements in some areas. v4 is where we see further improvements, especially in overall or general use, even though my primary goal was writing ability. People seem to really like the very first version of Ataraxy, even if it doesn't do as well in various benchmarks. I hope this one comes close to beating it's predecessor, but if it doesn't I will keep trying.
All the Ataraxy series are primarily made for writing ability, but after some threshold, it started to get hard to tell, and even test for writing performance because they were all pretty good. Hopefully with some feedback we can continue to seek improvements.
Quants
Provided by @mradermacher
GGUF Static: https://huggingface.co/mradermacher/Gemma-2-Ataraxy-v4d-9B-GGUF
GGUF IMatrix: https://huggingface.co/mradermacher/Gemma-2-Ataraxy-v4d-9B-i1-GGUF
Leaderboards
Open LLM Leaderboard 2 (12B and under)
Merge Details
This is a merge of pre-trained language models created using mergekit.
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: lemon07r/Gemma-2-Ataraxy-v4c-9B
dtype: bfloat16
merge_method: slerp
parameters:
t: 0.25
slices:
- sources:
- layer_range: [0, 42]
model: lemon07r/Gemma-2-Ataraxy-v4c-9B
- layer_range: [0, 42]
model: sam-paech/Darkest-muse-v1
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