Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 9
How to use rickp1011/llama2-codellama-taskarithmatic with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="rickp1011/llama2-codellama-taskarithmatic") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rickp1011/llama2-codellama-taskarithmatic")
model = AutoModelForCausalLM.from_pretrained("rickp1011/llama2-codellama-taskarithmatic", device_map="auto")How to use rickp1011/llama2-codellama-taskarithmatic with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rickp1011/llama2-codellama-taskarithmatic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "rickp1011/llama2-codellama-taskarithmatic",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/rickp1011/llama2-codellama-taskarithmatic
How to use rickp1011/llama2-codellama-taskarithmatic with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "rickp1011/llama2-codellama-taskarithmatic" \
--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": "rickp1011/llama2-codellama-taskarithmatic",
"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 "rickp1011/llama2-codellama-taskarithmatic" \
--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": "rickp1011/llama2-codellama-taskarithmatic",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use rickp1011/llama2-codellama-taskarithmatic with Docker Model Runner:
docker model run hf.co/rickp1011/llama2-codellama-taskarithmatic
This is a merge of pre-trained language models created using mergekit.
This model was merged using the task arithmetic merge method using meta-llama/Llama-2-7b-hf as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: meta-llama/Llama-2-7b-hf
- model: meta-llama/CodeLlama-7b-Python-hf
parameters:
density: 0.55
weight: 0.5
- model: meta-llama/CodeLlama-7b-Instruct-hf
parameters:
density: 0.65
weight: 0.5
base_model: meta-llama/Llama-2-7b-hf
merge_method: task_arithmetic
dtype: float16