Resolving Interference When Merging Models
Paper • 2306.01708 • Published • 19
How to use monology/mixtral-ties with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="monology/mixtral-ties") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("monology/mixtral-ties")
model = AutoModelForCausalLM.from_pretrained("monology/mixtral-ties")How to use monology/mixtral-ties with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "monology/mixtral-ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "monology/mixtral-ties",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/monology/mixtral-ties
How to use monology/mixtral-ties with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "monology/mixtral-ties" \
--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": "monology/mixtral-ties",
"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 "monology/mixtral-ties" \
--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": "monology/mixtral-ties",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use monology/mixtral-ties with Docker Model Runner:
docker model run hf.co/monology/mixtral-ties
For experimental purposes. Probably not very good.
This is a merge of pre-trained language models created using mergekit.
This model was merged using the TIES merge method using mistralai/Mistral-7B-v0.1 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: monology/mixtral-slerp0
parameters:
density: 0.5
weight: 0.1
- model: monology/mixtral-slerp1
parameters:
density: 0.5
weight: 0.1
- model: monology/mixtral-slerp2
parameters:
density: 0.5
weight: 0.1
- model: monology/mixtral-slerp3
parameters:
density: 0.5
weight: 0.1
- model: monology/mixtral-slerp4
parameters:
density: 0.5
weight: 0.1
- model: monology/mixtral-slerp5
parameters:
density: 0.5
weight: 0.1
- model: monology/mixtral-slerp6
parameters:
density: 0.5
weight: 0.1
- model: monology/mixtral-slerp7
parameters:
density: 0.5
weight: 0.1
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: true
dtype: float16