Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 8
How to use appvoid/dot-v1.9 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="appvoid/dot-v1.9") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("appvoid/dot-v1.9")
model = AutoModelForCausalLM.from_pretrained("appvoid/dot-v1.9")How to use appvoid/dot-v1.9 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/dot-v1.9"
# 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-v1.9",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/appvoid/dot-v1.9
How to use appvoid/dot-v1.9 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "appvoid/dot-v1.9" \
--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-v1.9",
"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-v1.9" \
--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-v1.9",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use appvoid/dot-v1.9 with Docker Model Runner:
docker model run hf.co/appvoid/dot-v1.9
This is a merge of pre-trained language models created using mergekit.
This model was merged using the task arithmetic merge method using TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T 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: 0.20
weight: 0.30
- model: raidhon/coven_tiny_1.1b_32k_orpo_alpha
parameters:
density: 0.45
weight: 0.26
- model: l3utterfly/tinyllama-1.1b-layla-v4
parameters:
density: 0.25
weight: 0.125
- model: ShieldX/manovyadh-1.1B-v1-chat
parameters:
density: 0.18
weight: 0.125
- model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
parameters:
density: 0.25
weight: 0.25
- model: sreeramajay/TinyLlama-1.1B-orca-v1.0
parameters:
density: 0.15
weight: 0.37
- model: AIGym/TinyLlama-1.1B-2.5T-chat-and-function-calling
parameters:
density: 0.25
weight: 0.26
- model: appvoid/palmer-003
parameters:
density: 0.75
weight: 0.50
merge_method: task_arithmetic
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
parameters:
normalize: true
int8_mask: true
dtype: float16