Instructions to use Vortex5/Radiant-Shadow-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vortex5/Radiant-Shadow-12B with Transformers:

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

pipe = pipeline("text-generation", model="Vortex5/Radiant-Shadow-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vortex5/Radiant-Shadow-12B")
model = AutoModelForCausalLM.from_pretrained("Vortex5/Radiant-Shadow-12B")
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

vLLM

How to use Vortex5/Radiant-Shadow-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vortex5/Radiant-Shadow-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vortex5/Radiant-Shadow-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vortex5/Radiant-Shadow-12B

SGLang

How to use Vortex5/Radiant-Shadow-12B 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 "Vortex5/Radiant-Shadow-12B" \
    --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": "Vortex5/Radiant-Shadow-12B",
		"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 "Vortex5/Radiant-Shadow-12B" \
        --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": "Vortex5/Radiant-Shadow-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vortex5/Radiant-Shadow-12B with Docker Model Runner:
```
docker model run hf.co/Vortex5/Radiant-Shadow-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Radiant-Shadow-12B

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

📒Notes: I had some issues with chatml instruction template, try Mistral V7 works well.

Merge Details

Merge Method

This model was merged using the Passthrough merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
- sources:
  - model: Vortex5/Lunar-Nexus-12B
    layer_range: [0, 17]

- sources:
  - model: Retreatcost/KansenSakura-Radiance-RP-12b
    layer_range: [17, 31]

- sources:
  - model: Vortex5/Shadow-Crystal-12B
    layer_range: [31, 40]
merge_method: passthrough
dtype: bfloat16
tokenizer:
  source: union

Downloads last month: 2

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for Vortex5/Radiant-Shadow-12B

Retreatcost/KansenSakura-Radiance-RP-12b

Vortex5/Lunar-Nexus-12B

Vortex5/Shadow-Crystal-12B

Merge model

this model

Quantizations

5 models