Instructions to use DoppelReflEx/MiniusLight-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DoppelReflEx/MiniusLight-24B with Transformers:

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

pipe = pipeline("text-generation", model="DoppelReflEx/MiniusLight-24B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DoppelReflEx/MiniusLight-24B")
model = AutoModelForCausalLM.from_pretrained("DoppelReflEx/MiniusLight-24B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DoppelReflEx/MiniusLight-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DoppelReflEx/MiniusLight-24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DoppelReflEx/MiniusLight-24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DoppelReflEx/MiniusLight-24B

SGLang

How to use DoppelReflEx/MiniusLight-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 "DoppelReflEx/MiniusLight-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": "DoppelReflEx/MiniusLight-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 "DoppelReflEx/MiniusLight-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": "DoppelReflEx/MiniusLight-24B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DoppelReflEx/MiniusLight-24B with Docker Model Runner:
```
docker model run hf.co/DoppelReflEx/MiniusLight-24B
```

DoppelReflEx commited on Apr 27, 2025

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@@ -108,7 +108,7 @@ license: cc-by-nc-4.0
     <div class="playwrite-ca-guides-regular pb tac">
     <h1 class="soft-blue-custom">MiniusLight-24B</h1>
     <h2 class="soft-blue-custom"><a href="https://huggingface.co/DoppelReflEx/MN-12B-Mimicore-Nocturne" class="accent bold">12B</a> - 24B-v1 - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v1.01" class="accent bold">24B-v1.01</a></h2>
-    <img src="https://img3.gelbooru.com/images/ec/af/ecafd7749bf946917190f98f609a9f47.jpg" alt="cover image" class="main-banner-image"/>
     <a href="https://twitter.com/void_ling/status/1855164935141200230" class="alink soft-blue-custom">Origin Content (Click Here)</a>
     </div>
     <div class="info">

     <div class="playwrite-ca-guides-regular pb tac">
     <h1 class="soft-blue-custom">MiniusLight-24B</h1>
     <h2 class="soft-blue-custom"><a href="https://huggingface.co/DoppelReflEx/MN-12B-Mimicore-Nocturne" class="accent bold">12B</a> - 24B-v1 - <a href="https://huggingface.co/DoppelReflEx/MiniusLight-24B-v1.01" class="accent bold">24B-v1.01</a></h2>
+    <img src="https://cdn.donmai.us/original/8c/1f/__shorekeeper_wuthering_waves_drawn_by_void_0__8c1f85311759aea5d15ebe12bcfcab32.jpg" alt="cover image" class="main-banner-image"/>
     <a href="https://twitter.com/void_ling/status/1855164935141200230" class="alink soft-blue-custom">Origin Content (Click Here)</a>
     </div>
     <div class="info">