Instructions to use mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper with Transformers:

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

pipe = pipeline("text-generation", model="mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper

SGLang

How to use mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper 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 "mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper" \
    --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": "mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper",
		"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 "mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper" \
        --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": "mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper with Docker Model Runner:
```
docker model run hf.co/mshapiro123/recurrent-qwen2.5-0.5b-natural-keeper
```

mshapiro123 commited on Aug 16

Commit

7cc6097

verified ·

1 Parent(s): 2f9f359

Add paper links and architecture figure

Browse files

Files changed (2) hide show

README.md +5 -1
figure1_architecture_comparison.svg +204 -0

README.md CHANGED Viewed

@@ -11,7 +11,11 @@ tags:
 # recurrent-qwen2.5-0.5b-natural-keeper
-The frozen natural keeper of the full-block recurrent retrofit: the verbally trained step-2,000 checkpoint from *Retrofitting Recurrent Depth into a Pretrained Language Model* (arXiv:2608.11233), and the substrate designated for the registered companion study on guided stochastic width.
 ## What this checkpoint is

 # recurrent-qwen2.5-0.5b-natural-keeper
+The frozen natural keeper of the full-block recurrent retrofit: the verbally trained step-2,000 checkpoint from *Retrofitting Recurrent Depth into a Pretrained Language Model* ([arXiv:2608.11233](https://arxiv.org/abs/2608.11233); [PDF](https://arxiv.org/pdf/2608.11233)), and the substrate designated for the registered companion study on guided stochastic width.
+## Architecture
+![Architecture comparison from the paper](./figure1_architecture_comparison.svg)
 ## What this checkpoint is

figure1_architecture_comparison.svg ADDED Viewed