Instructions to use ResplendentAI/Aurora_l3_8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ResplendentAI/Aurora_l3_8B with Transformers:

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

pipe = pipeline("text-generation", model="ResplendentAI/Aurora_l3_8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ResplendentAI/Aurora_l3_8B")
model = AutoModelForCausalLM.from_pretrained("ResplendentAI/Aurora_l3_8B")
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 ResplendentAI/Aurora_l3_8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ResplendentAI/Aurora_l3_8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ResplendentAI/Aurora_l3_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ResplendentAI/Aurora_l3_8B

SGLang

How to use ResplendentAI/Aurora_l3_8B 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 "ResplendentAI/Aurora_l3_8B" \
    --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": "ResplendentAI/Aurora_l3_8B",
		"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 "ResplendentAI/Aurora_l3_8B" \
        --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": "ResplendentAI/Aurora_l3_8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ResplendentAI/Aurora_l3_8B with Docker Model Runner:
```
docker model run hf.co/ResplendentAI/Aurora_l3_8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Aurora

A more poetic offering with a focus on perfecting the quote/asterisk RP format. I have strengthened the creative writing training.

Make sure your example messages and introduction are formatted cirrectly. You must respond in quotes if you want the bot to follow. Thoroughly tested and did not see a single issue. The model can still do plaintext/aserisks if you choose.

Downloads last month: 5

Safetensors

Model size

8B params

Tensor type

F16

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Quantizations

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