Instructions to use ELBennett/Weevil-Recurrent-16M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ELBennett/Weevil-Recurrent-16M-Base with Transformers:

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

pipe = pipeline("text-generation", model="ELBennett/Weevil-Recurrent-16M-Base", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ELBennett/Weevil-Recurrent-16M-Base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ELBennett/Weevil-Recurrent-16M-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ELBennett/Weevil-Recurrent-16M-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ELBennett/Weevil-Recurrent-16M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ELBennett/Weevil-Recurrent-16M-Base

SGLang

How to use ELBennett/Weevil-Recurrent-16M-Base 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 "ELBennett/Weevil-Recurrent-16M-Base" \
    --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": "ELBennett/Weevil-Recurrent-16M-Base",
		"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 "ELBennett/Weevil-Recurrent-16M-Base" \
        --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": "ELBennett/Weevil-Recurrent-16M-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ELBennett/Weevil-Recurrent-16M-Base with Docker Model Runner:
```
docker model run hf.co/ELBennett/Weevil-Recurrent-16M-Base
```

Weevil-Recurrent-16M-Base / config.json

ELBennett

Release the post-decay 16M recurrent base model

f1b1d76 verified 5 days ago

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932 Bytes

	{
	"architectures": [
	"WeevilRecurrentForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_weevil_recurrent.WeevilRecurrentConfig",
	"AutoModel": "modeling_weevil_recurrent.WeevilRecurrentModel",
	"AutoModelForCausalLM": "modeling_weevil_recurrent.WeevilRecurrentForCausalLM"
	},
	"bos_token_id": 50281,
	"chunk_size": 32,
	"coda_layers": 2,
	"compute_dtype": "bfloat16",
	"conv_kernel": 4,
	"core_passes": 16,
	"dtype": "float32",
	"embedding_dim": 128,
	"eos_token_id": 50279,
	"head_dim": 16,
	"hidden_size": 256,
	"intermediate_size": 1024,
	"max_position_embeddings": 8192,
	"model_type": "weevil_recurrent",
	"norm_eps": 1e-06,
	"num_heads": 16,
	"pad_token_id": 50283,
	"pause_token_id": 50285,
	"prelude_layers": 2,
	"recurrent_layers": 4,
	"tie_word_embeddings": false,
	"transformers_version": "5.19.0",
	"unk_token_id": 50280,
	"use_cache": true,
	"vocab_size": 50368
	}