Resolving Interference When Merging Models
Paper • 2306.01708 • Published • 19
How to use NikitosKey/Merge-Math-Coder-7B-v1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="NikitosKey/Merge-Math-Coder-7B-v1")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("NikitosKey/Merge-Math-Coder-7B-v1")
model = AutoModelForCausalLM.from_pretrained("NikitosKey/Merge-Math-Coder-7B-v1", device_map="auto")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use NikitosKey/Merge-Math-Coder-7B-v1 with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: llama cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: llama cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
docker model run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
How to use NikitosKey/Merge-Math-Coder-7B-v1 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NikitosKey/Merge-Math-Coder-7B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "NikitosKey/Merge-Math-Coder-7B-v1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
How to use NikitosKey/Merge-Math-Coder-7B-v1 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "NikitosKey/Merge-Math-Coder-7B-v1" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "NikitosKey/Merge-Math-Coder-7B-v1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "NikitosKey/Merge-Math-Coder-7B-v1" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "NikitosKey/Merge-Math-Coder-7B-v1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use NikitosKey/Merge-Math-Coder-7B-v1 with Ollama:
ollama run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
How to use NikitosKey/Merge-Math-Coder-7B-v1 with Docker Model Runner:
docker model run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
How to use NikitosKey/Merge-Math-Coder-7B-v1 with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull NikitosKey/Merge-Math-Coder-7B-v1:Q8_0
lemonade run user.Merge-Math-Coder-7B-v1-Q8_0
lemonade list
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-Instruct-v0.2 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-Instruct-v0.2
# Базовая модель, не меняем
parameters:
density: 1.0
weight: 1.0
- model: WizardLM/WizardMath-7B-V1.1
# Эксперт подмешивается
parameters:
density: 0.5 # DARE: берем только 50% самых важных изменений
weight: 0.5 # Вес влияния
merge_method: ties
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
normalize: true
int8_mask: true
dtype: float16
ollama run hf.co/NikitosKey/Merge-Math-Coder-7B-v1:Q8_0