Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper • 2203.05482 • Published • 8
How to use ApocalypseParty/G4-31B-SFT-v2-1-ConfigC with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="ApocalypseParty/G4-31B-SFT-v2-1-ConfigC") # Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText
processor = AutoProcessor.from_pretrained("ApocalypseParty/G4-31B-SFT-v2-1-ConfigC")
model = AutoModelForImageTextToText.from_pretrained("ApocalypseParty/G4-31B-SFT-v2-1-ConfigC")How to use ApocalypseParty/G4-31B-SFT-v2-1-ConfigC with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApocalypseParty/G4-31B-SFT-v2-1-ConfigC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ApocalypseParty/G4-31B-SFT-v2-1-ConfigC",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ApocalypseParty/G4-31B-SFT-v2-1-ConfigC
How to use ApocalypseParty/G4-31B-SFT-v2-1-ConfigC with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ApocalypseParty/G4-31B-SFT-v2-1-ConfigC" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ApocalypseParty/G4-31B-SFT-v2-1-ConfigC",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "ApocalypseParty/G4-31B-SFT-v2-1-ConfigC" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ApocalypseParty/G4-31B-SFT-v2-1-ConfigC",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ApocalypseParty/G4-31B-SFT-v2-1-ConfigC with Docker Model Runner:
docker model run hf.co/ApocalypseParty/G4-31B-SFT-v2-1-ConfigC
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:
models:
- model: google/gemma-4-31B-it
parameters:
weight: 0.55
- model: ApocalypseParty/G4-31B-SFT-v2-1
parameters:
weight: 0.45
merge_method: linear
dtype: bfloat16