Instructions to use LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 with Transformers:

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

pipe = pipeline("text-generation", model="LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130")
model = AutoModelForCausalLM.from_pretrained("LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130

SGLang

How to use LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 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 "LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130 with Docker Model Runner:
```
docker model run hf.co/LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Dazzling-Star-Aurora-32b-v0.0

If somewhere amid that aimlessly drifting sky,There was a planet where our wishes could flow free... would we try to make it there? I wonder what we'd wish for if we did...~

Listen to the song on youtube: https://www.youtube.com/watch?v=e1EExQiRhC0

Story behind it: Bored at midnight, decided to create a merge I guess, resulting in this model, I like it, so try it out?

Models:

EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2
ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3
Qwen/Qwen2.5-32B

Instruct Format: ChatML

Thank you to AuriAetherwiing for providing compute and helping merge the models.

Merge Details

Merge Method

This model was merged using the TIES merge method using Qwen_Qwen2.5-32B as a base.

Models Merged

The following models were included in the merge:

ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3
EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2
    parameters:
      weight: 0.3
      density: 0.7
  - model: ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3
    parameters:
      weight: 0.4
      density: 0.8
base_model: Qwen/Qwen2.5-32B
parameters:
  epsilon: 0.05
  lambda: 1
  int8_mask: true
  normalize: true
merge_method: ties
dtype: bfloat16

Downloads last month: 8

Safetensors

Model size

33B params

Tensor type

BF16

Model tree for LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130

Quantizations

3 models

Collection including LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130

Stellar Model Series

Collection

A Series of merges created by me in my spare time. • 6 items • Updated Dec 19, 2024 • 2

Paper for LyraNovaHeart/Dazzling-Star-Aurora-32b-v0.0-Experimental-1130

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19