Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper โข 2311.03099 โข Published โข 38
How to use QuantFactory/Gemma2-9B-TitanFusion-GGUF with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Gemma2-9B-TitanFusion-GGUF", device_map="auto")How to use QuantFactory/Gemma2-9B-TitanFusion-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M
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/Gemma2-9B-TitanFusion-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M
docker model run hf.co/QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M
How to use QuantFactory/Gemma2-9B-TitanFusion-GGUF with Ollama:
ollama run hf.co/QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M
How to use QuantFactory/Gemma2-9B-TitanFusion-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M
How to use QuantFactory/Gemma2-9B-TitanFusion-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Gemma2-9B-TitanFusion-GGUF:Q4_K_M
lemonade run user.Gemma2-9B-TitanFusion-GGUF-Q4_K_M
lemonade list
This is quantized version of bunnycore/Gemma2-9B-TitanFusion created using llama.cpp
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using nbeerbower/gemma2-gutenberg-9B as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: princeton-nlp/gemma-2-9b-it-SimPO
parameters:
weight: 0.5
density: 0.5
- model: nbeerbower/gemma2-gutenberg-9B
parameters:
weight: 0.5
density: 0.5
- model: ehristoforu/Gemma2-9B-it-psy10k-mental_health
parameters:
weight: 0.5
density: 0.5
- model: ehristoforu/Gemma2-9b-it-train6
parameters:
weight: 0.5
density: 0.5
merge_method: dare_ties
base_model: nbeerbower/gemma2-gutenberg-9B
dtype: bfloat16
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