Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 8
How to use ApocalypseParty/G4-31B-configED with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="ApocalypseParty/G4-31B-configED") # Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("ApocalypseParty/G4-31B-configED")
model = AutoModelForMultimodalLM.from_pretrained("ApocalypseParty/G4-31B-configED")How to use ApocalypseParty/G4-31B-configED with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApocalypseParty/G4-31B-configED"
# 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-configED",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ApocalypseParty/G4-31B-configED
How to use ApocalypseParty/G4-31B-configED with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ApocalypseParty/G4-31B-configED" \
--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-configED",
"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-configED" \
--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-configED",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ApocalypseParty/G4-31B-configED with Docker Model Runner:
docker model run hf.co/ApocalypseParty/G4-31B-configED
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-configED" \
--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-configED",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'This is a merge of pre-trained language models created using mergekit.
This model was merged using the Task Arithmetic merge method using google/gemma-4-31B-it as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
merge_method: task_arithmetic
base_model: google/gemma-4-31B-it
models:
- model: ApocalypseParty/G4-31B-SFT-v5-2
parameters:
weight: 1.0
- model: ApocalypseParty/G4-31B-DFT-Test-2-chkpt120
parameters:
weight: 0.35
dtype: bfloat16
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "ApocalypseParty/G4-31B-configED" \ --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-configED", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'