Instructions to use rhplus0831/maid-yuzu-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rhplus0831/maid-yuzu-v3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rhplus0831/maid-yuzu-v3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rhplus0831/maid-yuzu-v3")
model = AutoModelForCausalLM.from_pretrained("rhplus0831/maid-yuzu-v3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rhplus0831/maid-yuzu-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rhplus0831/maid-yuzu-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rhplus0831/maid-yuzu-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rhplus0831/maid-yuzu-v3

SGLang

How to use rhplus0831/maid-yuzu-v3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rhplus0831/maid-yuzu-v3" \
    --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": "rhplus0831/maid-yuzu-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

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 "rhplus0831/maid-yuzu-v3" \
        --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": "rhplus0831/maid-yuzu-v3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rhplus0831/maid-yuzu-v3 with Docker Model Runner:
```
docker model run hf.co/rhplus0831/maid-yuzu-v3
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

maid-yuzu-v3

This is a merge of pre-trained language models created using mergekit.

This model was created because I wanted to know how the density and weight values of the dare_ties method affect the base model.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using smelborp/MixtralOrochi8x7B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model:
  model:
    path: smelborp/MixtralOrochi8x7B
dtype: bfloat16
merge_method: dare_ties
slices:
- sources:
  - layer_range: [0, 32]
    model:
      model:
        path: ycros/BagelMIsteryTour-v2-8x7B
    parameters:
      density: 0.6
      weight: 0.25
  - layer_range: [0, 32]
    model:
      model:
        path: NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO
    parameters:
      density: 0.4
      weight: 0.1
  - layer_range: [0, 32]
    model:
      model:
        path: smelborp/MixtralOrochi8x7B