Instructions to use Ayaka157/RetNet-0.5B-v21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ayaka157/RetNet-0.5B-v21 with Transformers:

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

pipe = pipeline("text-generation", model="Ayaka157/RetNet-0.5B-v21", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Ayaka157/RetNet-0.5B-v21", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ayaka157/RetNet-0.5B-v21 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ayaka157/RetNet-0.5B-v21"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ayaka157/RetNet-0.5B-v21",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ayaka157/RetNet-0.5B-v21

SGLang

How to use Ayaka157/RetNet-0.5B-v21 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 "Ayaka157/RetNet-0.5B-v21" \
    --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": "Ayaka157/RetNet-0.5B-v21",
		"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 "Ayaka157/RetNet-0.5B-v21" \
        --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": "Ayaka157/RetNet-0.5B-v21",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Ayaka157/RetNet-0.5B-v21 with Docker Model Runner:
```
docker model run hf.co/Ayaka157/RetNet-0.5B-v21
```

RetNet-0.5B-v21 / config.json

Ayaka157

Upload folder using huggingface_hub

a9e39bc verified 22 days ago

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{
  "architectures": [
    "RetNetForCausalLM"
  ],
  "checkpoint_layers": -1,
  "chunk_size": 128,
  "d_model": 1408,
  "dropout": 0.1,
  "dtype": "float32",
  "embed_scale": 27.712812921102035,
  "ffn_dim": 2816,
  "growth_old_dim": 768,
  "mode": "chunkwise",
  "model_type": "retnet_hf",
  "norm_eps": 1e-05,
  "num_heads": 22,
  "num_layers": 20,
  "prompt_loss_weight": 0.1,
  "tie_weights": true,
  "transformers_version": "4.57.6",
  "use_gla": true,
  "use_growth_ln": true,
  "use_swiglu": false,
  "value_dim": 2816,
  "vocab_size": 20000,
  "auto_map": {
    "AutoConfig": "modeling_retnet.RetNetHFConfig",
    "AutoModelForCausalLM": "modeling_retnet.RetNetForCausalLM"
  }
}

	{
	"architectures": [
	"RetNetForCausalLM"
	],
	"checkpoint_layers": -1,
	"chunk_size": 128,
	"d_model": 1408,
	"dropout": 0.1,
	"dtype": "float32",
	"embed_scale": 27.712812921102035,
	"ffn_dim": 2816,
	"growth_old_dim": 768,
	"mode": "chunkwise",
	"model_type": "retnet_hf",
	"norm_eps": 1e-05,
	"num_heads": 22,
	"num_layers": 20,
	"prompt_loss_weight": 0.1,
	"tie_weights": true,
	"transformers_version": "4.57.6",
	"use_gla": true,
	"use_growth_ln": true,
	"use_swiglu": false,
	"value_dim": 2816,
	"vocab_size": 20000,
	"auto_map": {
	"AutoConfig": "modeling_retnet.RetNetHFConfig",
	"AutoModelForCausalLM": "modeling_retnet.RetNetForCausalLM"
	}
	}