Instructions to use Azazelle/Maylin-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azazelle/Maylin-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Azazelle/Maylin-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Azazelle/Maylin-7b") model = AutoModelForCausalLM.from_pretrained("Azazelle/Maylin-7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Azazelle/Maylin-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Azazelle/Maylin-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Azazelle/Maylin-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Azazelle/Maylin-7b
- SGLang
How to use Azazelle/Maylin-7b 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 "Azazelle/Maylin-7b" \ --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": "Azazelle/Maylin-7b", "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 "Azazelle/Maylin-7b" \ --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": "Azazelle/Maylin-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Azazelle/Maylin-7b with Docker Model Runner:
docker model run hf.co/Azazelle/Maylin-7b
Model Card for Maylin-7b
DARE merge intended to help the Argetsu model be more coherent and less horny.
.yaml file for mergekit
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: SanjiWatsuki/Sonya-7B #200
parameters:
weight: 0.45
density: 0.75
- model: Azazelle/Argetsu #175
parameters:
weight: 0.39
density: 0.70
- model: Azazelle/Tippy-Toppy-7b #100
parameters:
weight: 0.22
density: 0.52
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
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