Text Generation
Transformers
PyTorch
English
veronica
polymorphic-mlp
mixture-of-branches
entropy-regularized-routing
decoder-only
causal-lm
rope
expandable-architecture
research
Instructions to use MhaWay/Veronica with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MhaWay/Veronica with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MhaWay/Veronica")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MhaWay/Veronica", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MhaWay/Veronica with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MhaWay/Veronica" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MhaWay/Veronica", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MhaWay/Veronica
- SGLang
How to use MhaWay/Veronica 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 "MhaWay/Veronica" \ --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": "MhaWay/Veronica", "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 "MhaWay/Veronica" \ --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": "MhaWay/Veronica", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MhaWay/Veronica with Docker Model Runner:
docker model run hf.co/MhaWay/Veronica
| { | |
| "_comment": "Ultra-deep reasoning config: 24L/12H/768d, mlp_mult=4. Maximum reasoning depth for RAG + multi-step inference. ~11-12GB VRAM.", | |
| "architectures": [ | |
| "VeronicaForCausalLM" | |
| ], | |
| "bos_token_id": 50257, | |
| "dropout": 0.0, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 50256, | |
| "gradient_checkpointing": true, | |
| "hidden_size": 768, | |
| "layer_norm_epsilon": 1e-05, | |
| "max_position_embeddings": 2048, | |
| "mlp_mult": 4, | |
| "model_type": "veronica", | |
| "n_embd": 768, | |
| "n_head": 12, | |
| "n_layer": 24, | |
| "num_attention_heads": 12, | |
| "num_funcs": 3, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 50258, | |
| "rope_theta": 10000.0, | |
| "router_aux_weight": 0.015999763124389305, | |
| "router_dim": 128, | |
| "router_tau": 1.400028804954452, | |
| "transformers_version": "4.57.0.dev0", | |
| "use_cache": false, | |
| "use_channel_attention": false, | |
| "use_flash_attn": true, | |
| "vocab_size": 50271 | |
| } | |