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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
| { | |
| "vocab_size": 50257, | |
| "max_seq_len": 2048, | |
| "d_model": 1024, | |
| "n_layers": 10, | |
| "n_heads": 16, | |
| "ff_mult": 4, | |
| "dropout": 0.1, | |
| "recurse_steps": 6, | |
| "critique_threshold": 0.2, | |
| "tie_embeddings": true, | |
| "use_moe": true, | |
| "moe_num_experts": 32, | |
| "moe_top_k": 1, | |
| "moe_expert_hidden": 1280, | |
| "moe_router_jitter": 0.01, | |
| "moe_aux_loss_weight": 0.01, | |
| "use_layer_skip": true, | |
| "layer_skip_threshold": 0.8, | |
| "layer_skip_target": 0.03, | |
| "layer_skip_aux_weight": 0.01, | |
| "use_ternary_weights": true, | |
| "use_flash_attention": true, | |
| "use_fused_ops": true, | |
| "packed_execution": true, | |
| "use_torch_compile": false, | |
| "moe_backend": "auto", | |
| "moe_ep_size": 1 | |
| } |