Instructions to use Azazelle/Argetsu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azazelle/Argetsu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Azazelle/Argetsu")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Azazelle/Argetsu") model = AutoModelForCausalLM.from_pretrained("Azazelle/Argetsu") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Azazelle/Argetsu with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Azazelle/Argetsu" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Azazelle/Argetsu", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Azazelle/Argetsu
- SGLang
How to use Azazelle/Argetsu 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/Argetsu" \ --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/Argetsu", "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/Argetsu" \ --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/Argetsu", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Azazelle/Argetsu with Docker Model Runner:
docker model run hf.co/Azazelle/Argetsu
Model Card for Argetsu
Slerp merge of lots of models.
.yaml file for mergekit
slices:
- sources:
- model: Azazelle/SlimMelodicMaid
layer_range: [0, 32]
- model: Azazelle/Dumb-Maidlet
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
parameters:
t:
- filter: self_attn
value: [0.2, 0.6, 0.44, 0.76, 1]
- filter: mlp
value: [0.8, 0.4, 0.56, 0.24, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
- Downloads last month
- 235
docker model run hf.co/Azazelle/Argetsu