Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 8
How to use appvoid/dot-v2.4 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="appvoid/dot-v2.4") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("appvoid/dot-v2.4")
model = AutoModelForCausalLM.from_pretrained("appvoid/dot-v2.4")How to use appvoid/dot-v2.4 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/dot-v2.4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "appvoid/dot-v2.4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/appvoid/dot-v2.4
How to use appvoid/dot-v2.4 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "appvoid/dot-v2.4" \
--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": "appvoid/dot-v2.4",
"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 "appvoid/dot-v2.4" \
--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": "appvoid/dot-v2.4",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use appvoid/dot-v2.4 with Docker Model Runner:
docker model run hf.co/appvoid/dot-v2.4
This is a merge of pre-trained language models created using mergekit.
This model was merged using the task arithmetic merge method using appvoid/palmer-003 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: vihangd/DopeyTinyLlama-1.1B-v1
parameters:
density: 0.25
weight: 0.33
- model: raidhon/coven_tiny_1.1b_32k_orpo_alpha
parameters:
density: 0.40
weight: 0.50
- model: l3utterfly/tinyllama-1.1b-layla-v4
parameters:
density: 0.28
weight: 0.20
- model: ShieldX/manovyadh-1.1B-v1-chat
parameters:
density: 0.33
weight: 0.30
- model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
parameters:
density: 0.30
weight: 0.45
- model: AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling
parameters:
density: 0.32
weight: 0.26
- model: microsoft/rho-math-1b-interpreter-v0.1
parameters:
density: 0.28
weight: 0.35
merge_method: task_arithmetic
base_model: appvoid/palmer-003
parameters:
normalize: false
int8_mask: true
dtype: float16
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "appvoid/dot-v2.4"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/dot-v2.4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'