Text Generation
Transformers
PyTorch
Safetensors
English
rubirlm
causal-lm
base-model
1b
Mixture of Experts
Instructions to use DevHunterAI/RubiRLM-1B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevHunterAI/RubiRLM-1B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DevHunterAI/RubiRLM-1B-Base")# Load model directly from transformers import RubiRLM model = RubiRLM.from_pretrained("DevHunterAI/RubiRLM-1B-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DevHunterAI/RubiRLM-1B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DevHunterAI/RubiRLM-1B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DevHunterAI/RubiRLM-1B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DevHunterAI/RubiRLM-1B-Base
- SGLang
How to use DevHunterAI/RubiRLM-1B-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 "DevHunterAI/RubiRLM-1B-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": "DevHunterAI/RubiRLM-1B-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 "DevHunterAI/RubiRLM-1B-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": "DevHunterAI/RubiRLM-1B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DevHunterAI/RubiRLM-1B-Base with Docker Model Runner:
docker model run hf.co/DevHunterAI/RubiRLM-1B-Base
| language: | |
| - en | |
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - rubirlm | |
| - causal-lm | |
| - base-model | |
| - text-generation | |
| - 1b | |
| - moe | |
| datasets: | |
| - HuggingFaceFW/fineweb | |
| - HuggingFaceH4/ultrachat_200k | |
| pipeline_tag: text-generation | |
| # RubiRLM-1B-Base | |
| **RubiRLM-1B-Base** is a **1B-parameter base language model** released by **DevHunterAI**. | |
| **Model size: 1B parameters** | |
| **Training datasets:** FineWeb, UltraChat-200k | |
| **Model type:** Base / pretrained language model | |
| **Important:** This release is a **base model**. It can be used for prompt-based generation and experimental chat-style interaction, but it is **not an instruction-tuned chat assistant**. | |
| ## Architecture | |
|  | |
| **RubiRLM 1B** uses a recursive language modeling architecture with recurrent state flow, Mixture-of-Experts routing, and conditional block execution. | |
| ## Key Features | |
| - **1B parameters** | |
| - **Recursive Language Model (RLM)** architecture | |
| - **10 recursive blocks** | |
| - **d_model = 1024** | |
| - **16 attention heads** | |
| - **max sequence length = 2048** | |
| - **6 recursive reasoning steps** | |
| - **Mixture-of-Experts: 32 experts, top-1 routing** | |
| - **Layer skip router for conditional execution** | |
| - **Packed execution support** | |
| - **Tied token embedding and LM head** | |
| ## Training Data | |
| This model was trained using a mixture of: | |
| - **FineWeb** | |
| - **UltraChat-200k** | |
| ## Intended Usage | |
| This model is intended for: | |
| - base language modeling research | |
| - continued pretraining | |
| - experimental prompt-based generation | |
| - architecture experimentation around recursive and MoE-based language models | |
| ## Not Intended As | |
| This release should **not** be treated as: | |
| - a fully aligned assistant | |
| - a safety-tuned production chatbot | |
| - an instruction-following model with guaranteed conversational quality | |
| ## Loading | |
| Because this repository includes custom model code, loading may require `trust_remote_code=True` depending on your workflow. | |
| ## Files | |
| - `pytorch_model.bin`: exported RubiRLM weights | |
| - `training_checkpoint.pt`: original training checkpoint | |
| - `config.json`: Hugging Face-facing config | |
| - `rubirlm_config.json`: full RubiRLM architecture config | |
| - `RubiRLM.py`: model implementation | |
| - `xqs_moe.py`, `xqs_stack.py`, `x_quantum_sparse_ops.py`, `rubi_train_stack.py`: supporting code | |
| ## Notes | |
| The exported weights were produced from the final training checkpoint and packaged for Hugging Face publication. |