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

Libraries

How to use mshapiro123/recurrent-qwen2.5-0.5b-full-block 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-full-block", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mshapiro123/recurrent-qwen2.5-0.5b-full-block", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/mshapiro123/recurrent-qwen2.5-0.5b-full-block

SGLang

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

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

recurrent-qwen2.5-0.5b-full-block

File size: 660 Bytes

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  "expected_correct_by_depth": {
    "1": 128,
    "2": 127,
    "3": 127,
    "4": 128
  },
  "source_data": "outputs/stage5/stage5_synthetic_depth_frozen_eval_v3_depth22_n24/data/test_chain_mcq.jsonl",
  "source_receipt": "outputs/stage5/stage5_n24_support12_rung_20260707_140139/eval/frozen_depth22_step_6000/active_summary.json",
  "identity_check": false,
  "verification_data": "hf_release/verification_assets/recurrent-qwen2.5-0.5b-full-block.jsonl",
  "verification_data_sha256": "76de261d06dc95b94574b5fe3bb06045add47faf03011cd2b2b2f2bdd5cfefb4",
  "row_count": 512,
  "rows_by_depth": {
    "1": 128,
    "2": 128,
    "3": 128,
    "4": 128
  }
}