Resolving Interference When Merging Models
Paper • 2306.01708 • Published • 19
How to use ClaudioItaly/Memo-2024 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ClaudioItaly/Memo-2024") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Memo-2024")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Memo-2024")How to use ClaudioItaly/Memo-2024 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/Memo-2024"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ClaudioItaly/Memo-2024",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ClaudioItaly/Memo-2024
How to use ClaudioItaly/Memo-2024 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ClaudioItaly/Memo-2024" \
--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": "ClaudioItaly/Memo-2024",
"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 "ClaudioItaly/Memo-2024" \
--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": "ClaudioItaly/Memo-2024",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ClaudioItaly/Memo-2024 with Docker Model Runner:
docker model run hf.co/ClaudioItaly/Memo-2024
This is a merge of pre-trained language models created using mergekit.
This model was merged using the TIES merge method using SillyTilly/mistralai_Mistral-Nemo-Base-2407 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: SillyTilly/mistralai_Mistral-Nemo-Base-2407
dtype: bfloat16
merge_method: ties
parameters:
normalize: true
int8_mask: true
lambda: 0.8
slices:
- sources:
- model: SillyTilly/mistralai_Mistral-Nemo-Base-2407
layer_range: [0, -1]
parameters:
weight: 1.0
density: 0.6
- model: SillyTilly/mistralai_Mistral-Nemo-Instruct-2407
layer_range: [0, -1]
parameters:
weight: 0.7
density: 0.6
tie_alpha: 0.3
tie_norm: L2
merge_verbose: true