Resolving Interference When Merging Models
Paper • 2306.01708 • Published • 19
How to use hrjang/hrjang2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="hrjang/hrjang2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("hrjang/hrjang2")
model = AutoModelForCausalLM.from_pretrained("hrjang/hrjang2")How to use hrjang/hrjang2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hrjang/hrjang2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "hrjang/hrjang2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/hrjang/hrjang2
How to use hrjang/hrjang2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "hrjang/hrjang2" \
--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": "hrjang/hrjang2",
"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 "hrjang/hrjang2" \
--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": "hrjang/hrjang2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use hrjang/hrjang2 with Docker Model Runner:
docker model run hf.co/hrjang/hrjang2
This is a merge of pre-trained language models created using mergekit.
This model was merged using the TIES merge method using meta-llama/Meta-Llama-3-8B as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
density: 0.5
weight: 0.5
- model: MLP-KTLim/llama-3-Korean-Bllossom-8B
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: meta-llama/Meta-Llama-3-8B
parameters:
normalize: false
int8_mask: true
dtype: float16