Instructions to use hardlyworking/Aura_24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hardlyworking/Aura_24B with Transformers:

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

pipe = pipeline("text-generation", model="hardlyworking/Aura_24B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hardlyworking/Aura_24B")
model = AutoModelForCausalLM.from_pretrained("hardlyworking/Aura_24B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hardlyworking/Aura_24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hardlyworking/Aura_24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hardlyworking/Aura_24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hardlyworking/Aura_24B

SGLang

How to use hardlyworking/Aura_24B 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 "hardlyworking/Aura_24B" \
    --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": "hardlyworking/Aura_24B",
		"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 "hardlyworking/Aura_24B" \
        --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": "hardlyworking/Aura_24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hardlyworking/Aura_24B with Docker Model Runner:
```
docker model run hf.co/hardlyworking/Aura_24B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Aura_24B

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

lora
GreenerPastures/Useful_Idiot_24B

Configuration

The following YAML configuration was used to produce this model:

base_model: GreenerPastures/Useful_Idiot_24B
merge_method: slerp

models:
  - model: GreenerPastures/Useful_Idiot_24B
  - model: lora

parameters:
  t: 0.5        # interpolation factor (0 = first model, 1 = second)
  normalize: true

output:
  save_safetensors: true
  dtype: bfloat16

Downloads last month: 6

Safetensors

Model size

24B params

Tensor type

BF16

Model tree for hardlyworking/Aura_24B

Base model

GreenerPastures/Useful_Idiot_24B

Finetuned

(1)

this model

Quantizations

2 models