Instructions to use Azazelle/Yuna-7b-Merge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azazelle/Yuna-7b-Merge with Transformers:

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

pipe = pipeline("text-generation", model="Azazelle/Yuna-7b-Merge")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azazelle/Yuna-7b-Merge")
model = AutoModelForCausalLM.from_pretrained("Azazelle/Yuna-7b-Merge")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Azazelle/Yuna-7b-Merge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azazelle/Yuna-7b-Merge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azazelle/Yuna-7b-Merge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Azazelle/Yuna-7b-Merge

SGLang

How to use Azazelle/Yuna-7b-Merge 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/Yuna-7b-Merge" \
    --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/Yuna-7b-Merge",
		"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/Yuna-7b-Merge" \
        --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/Yuna-7b-Merge",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Azazelle/Yuna-7b-Merge with Docker Model Runner:
```
docker model run hf.co/Azazelle/Yuna-7b-Merge
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Argetsu

Experimental DARE (no idea if this is decent).

.yaml file for mergekit

models:
  - model: Dans-DiscountModels/Dans-07YahooAnswers-7b
    # no parameters necessary for base model
  - model: Azazelle/Maylin-7b #200
    parameters:
      weight: 0.45
      density: 0.75
  - model: Azazelle/smol_bruin-7b #175
    parameters:
      weight: 0.39
      density: 0.70
  - model: SanjiWatsuki/Kunoichi-7B #100
    parameters:
      weight: 0.22
      density: 0.52
merge_method: dare_ties
base_model: Dans-DiscountModels/Dans-07YahooAnswers-7b
parameters:
  int8_mask: true
dtype: bfloat16

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Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Azazelle/Yuna-7b-Merge

Quantizations

1 model