Instructions to use QuantFactory/Gemma-2-Ataraxy-Doppel-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-Doppel-9B-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/Gemma-2-Ataraxy-Doppel-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-Doppel-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Gemma-2-Ataraxy-Doppel-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-Doppel-9B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Gemma-2-Ataraxy-Doppel-9B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
QuantFactory/Gemma-2-Ataraxy-Doppel-9B-GGUF
This is quantized version of lemon07r/Gemma-2-Ataraxy-Doppel-9B created using llama.cpp
Original Model Card
Gemma-2-Ataraxy-Doppel-9B
One last test model.. that you should ignore again.
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the della merge method using unsloth/gemma-2-9b-it as a base.
Models Merged
The following models were included in the merge:
- princeton-nlp/gemma-2-9b-it-SimPO
- wzhouad/gemma-2-9b-it-WPO-HB
- nbeerbower/Gemma2-Gutenberg-Doppel-9B
Configuration
The following YAML configuration was used to produce this model:
base_model: unsloth/gemma-2-9b-it
dtype: bfloat16
merge_method: della
parameters:
epsilon: 0.1
int8_mask: 1.0
lambda: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 42]
model: unsloth/gemma-2-9b-it
- layer_range: [0, 42]
model: wzhouad/gemma-2-9b-it-WPO-HB
parameters:
density: 0.55
weight: 0.6
- layer_range: [0, 42]
model: princeton-nlp/gemma-2-9b-it-SimPO
parameters:
density: 0.35
weight: 0.6
- layer_range: [0, 42]
model: nbeerbower/Gemma2-Gutenberg-Doppel-9B
parameters:
density: 0.25
weight: 0.4
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