Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper โข 2203.05482 โข Published โข 10
How to use jeiku/Furry_Request_3B_GGUF with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jeiku/Furry_Request_3B_GGUF", device_map="auto")How to use jeiku/Furry_Request_3B_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 jeiku/Furry_Request_3B_GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf jeiku/Furry_Request_3B_GGUF:Q2_K
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jeiku/Furry_Request_3B_GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf jeiku/Furry_Request_3B_GGUF:Q2_K
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 jeiku/Furry_Request_3B_GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama cli -hf jeiku/Furry_Request_3B_GGUF:Q2_K
docker model run hf.co/jeiku/Furry_Request_3B_GGUF:Q2_K
How to use jeiku/Furry_Request_3B_GGUF with Ollama:
ollama run hf.co/jeiku/Furry_Request_3B_GGUF:Q2_K
How to use jeiku/Furry_Request_3B_GGUF with Docker Model Runner:
docker model run hf.co/jeiku/Furry_Request_3B_GGUF:Q2_K
How to use jeiku/Furry_Request_3B_GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jeiku/Furry_Request_3B_GGUF:Q2_K
lemonade run user.Furry_Request_3B_GGUF-Q2_K
lemonade list
This is a merge of pre-trained language models created using mergekit.
This model was merged using the linear merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
merge_method: linear
models:
- model: jeiku/Rosa_v1_3B+jeiku/Furry_Request_StableLM
parameters:
weight: 1
dtype: float16
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