Text Generation
Transformers
Safetensors
qwen3
reasoning
extrapolation
rl
qwen
text-generation-inference
Instructions to use HerrHruby/RCT-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HerrHruby/RCT-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HerrHruby/RCT-4B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HerrHruby/RCT-4B") model = AutoModelForCausalLM.from_pretrained("HerrHruby/RCT-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HerrHruby/RCT-4B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HerrHruby/RCT-4B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HerrHruby/RCT-4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HerrHruby/RCT-4B
- SGLang
How to use HerrHruby/RCT-4B 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 "HerrHruby/RCT-4B" \ --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": "HerrHruby/RCT-4B", "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 "HerrHruby/RCT-4B" \ --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": "HerrHruby/RCT-4B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HerrHruby/RCT-4B with Docker Model Runner:
docker model run hf.co/HerrHruby/RCT-4B
Add model card for Reasoning Cache (RCT-4B)
#1
by nielsr HF Staff - opened
This PR adds a comprehensive model card for the RCT-4B model, which implements the Reasoning Cache (RC) algorithm.
Key improvements include:
- Added
pipeline_tag: text-generationfor better discoverability. - Added
library_name: transformersto enable the automatedtransformerscode snippet, as the model architecture is compatible. - Included relevant
tagssuch asreasoning,extrapolation,rl, andqwen. - Provided a clear model description based on the paper abstract.
- Linked to the official research paper: Reasoning Cache: Continual Improvement Over Long Horizons via Short-Horizon RL.
- Added a link to the official GitHub repository: IanYHWu/rc.
- Included a usage section explaining the need for RC-decoding logic, referencing vLLM for inference and verl for training, and clarifying
transformerscompatibility for loading. - Added a BibTeX citation.
This enhanced model card provides essential information for users and improves the model's integration and discoverability on the Hugging Face Hub.
HerrHruby changed pull request status to merged