Resolving Interference When Merging Models
Paper • 2306.01708 • Published • 19
How to use appvoid/arco-exp-07 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="appvoid/arco-exp-07") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("appvoid/arco-exp-07")
model = AutoModelForCausalLM.from_pretrained("appvoid/arco-exp-07")How to use appvoid/arco-exp-07 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/arco-exp-07"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "appvoid/arco-exp-07",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/appvoid/arco-exp-07
How to use appvoid/arco-exp-07 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "appvoid/arco-exp-07" \
--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/arco-exp-07",
"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/arco-exp-07" \
--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/arco-exp-07",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use appvoid/arco-exp-07 with Docker Model Runner:
docker model run hf.co/appvoid/arco-exp-07
This is a merge of pre-trained language models created using mergekit.
This model was merged using the TIES merge method using appvoid/arco-reflection as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: appvoid/danube-reasoner
parameters:
density: 0.6
weight: 0.4
- model: appvoid/danube-reason-4ep
parameters:
density: 0.6
weight: 0.4
- model: appvoid/arco-chat-v0.1
parameters:
density: 0.6
weight: 0.3
- model: appvoid/cubby-chat
parameters:
density: 0.6
weight: 0.4
merge_method: ties
base_model: appvoid/arco-reflection
parameters:
normalize: false
int8_mask: true
dtype: float16