Text Generation
Transformers
Safetensors
English
rixis1
neuranet
neuranet-zero
conversational
grouped-query-attention
long-context
custom_code
Instructions to use rubenroy/NeuraNET-Zero-18B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubenroy/NeuraNET-Zero-18B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rubenroy/NeuraNET-Zero-18B-Preview", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("rubenroy/NeuraNET-Zero-18B-Preview", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rubenroy/NeuraNET-Zero-18B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rubenroy/NeuraNET-Zero-18B-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rubenroy/NeuraNET-Zero-18B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rubenroy/NeuraNET-Zero-18B-Preview
- SGLang
How to use rubenroy/NeuraNET-Zero-18B-Preview 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 "rubenroy/NeuraNET-Zero-18B-Preview" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rubenroy/NeuraNET-Zero-18B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "rubenroy/NeuraNET-Zero-18B-Preview" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rubenroy/NeuraNET-Zero-18B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rubenroy/NeuraNET-Zero-18B-Preview with Docker Model Runner:
docker model run hf.co/rubenroy/NeuraNET-Zero-18B-Preview
| language: | |
| - en | |
| license: cc-by-nc-nd-4.0 | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - neuranet | |
| - neuranet-zero | |
| - conversational | |
| - text-generation | |
| - grouped-query-attention | |
| - long-context | |
| # >>> NeuraNET Zero 18B Preview <<< | |
| An 18-billion-parameter conversational language model powered by the RiXIS 1 architecture. | |
| <br> | |
| Copyright (c) 2026 Ruben Roy. All rights reserved. | |
| ## Overview | |
| NeuraNET Zero is a large language model designed for conversational interaction and general text generation. | |
| This repo contains the public NeuraNET Zero 18B Preview checkpoint, which includes: | |
| - the RiXIS 1 configuration and modelling implementation; | |
| - BF16 model weights; | |
| - small X\NeuraNET tokenizer and chat template; | |
| - eager attention and PyTorch SDPA support; | |
| - standard generation and KV-cache support | |
| NeuraNET Zero is released as a preview for non-commercial research and evaluation. | |
| ## Model Specifications | |
| | Property | Value | | |
| |---|---:| | |
| | Model | NeuraNET Zero 18B Preview | | |
| | Architecture | RiXIS 1 | | |
| | Parameters | 17,711,116,288 | | |
| | Transformer layers | 80 | | |
| | Vocabulary size | 32,001 | | |
| | Maximum context length | 32,768 tokens | | |
| | Normalisation | RMSNorm | | |
| | Primary language | English | | |
| | Licence | CC BY-NC-ND 4.0 | | |
| The model uses grouped-query attention with 32 query heads and 8 key-value heads. | |
| ## Usage | |
| ### Installation | |
| ```bash | |
| pip install -U "transformers==5.14.1" accelerate safetensors torch | |
| ``` | |
| ### Example | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "rubenroy/NeuraNET-Zero-18B-Preview" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| trust_remote_code=True, | |
| dtype="auto", | |
| device_map="cuda" | |
| ).eval() | |
| messages = [ | |
| { | |
| "role": "user", | |
| "content": "Hello there! Can you introduce yourself?" | |
| } | |
| ] | |
| inputs = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=True, | |
| add_generation_prompt=True, | |
| return_tensors="pt", | |
| return_dict=True | |
| ).to(model.device) | |
| with torch.inference_mode(): | |
| generated_ids = model.generate( | |
| **inputs, | |
| max_new_tokens=1024, | |
| do_sample=False, | |
| use_cache=True, | |
| eos_token_id=tokenizer.eos_token_id, | |
| pad_token_id=tokenizer.pad_token_id | |
| ) | |
| new_tokens = generated_ids[ | |
| :, | |
| inputs["input_ids"].shape[1]: | |
| ] | |
| response = tokenizer.batch_decode( | |
| new_tokens, | |
| skip_special_tokens=True | |
| )[0] | |
| print(response.strip()) | |
| ``` | |
| ## Sampling | |
| The above example uses greedy decoding for stable output: | |
| ```python | |
| do_sample=False | |
| ``` | |
| For more varied generation, sampling can be enabled: | |
| ```python | |
| generated_ids = model.generate( | |
| **inputs, | |
| max_new_tokens=256, | |
| do_sample=True, | |
| temperature=0.8, | |
| top_p=0.9, | |
| repetition_penalty=1.05, | |
| use_cache=True, | |
| eos_token_id=tokenizer.eos_token_id, | |
| pad_token_id=tokenizer.pad_token_id | |
| ) | |
| ``` | |
| ## RiXIS | |
| RiXIS 1 \[PREVIEW\] is contained in this repository via: | |
| ``` | |
| configuration_rixis1.py | |
| modeling_rixis1.py | |
| ``` | |
| For this reason: | |
| ```python | |
| trust_remote_code=True | |
| ``` | |
| Please review repository code before enabling remote execution. | |
| **DISCLAIMER:** Authorised public RiXIS 1 model weights release ("NeuraNET Zero"). Source files are a reference implementation for loading and inference. proprietary development infrastructure and implementation details are omitted. | |
| ## Evaluation | |
| This release does not currently publish a benchmark suite or verified evaluation results. | |
| Community evaluation is welcome within the terms of the licence. Evaluation reports must state: | |
| - the exact model revision; | |
| - the prompt or chat format; | |
| - decoding parameters; | |
| - hardware and software versions; | |
| - any preprocessing or postprocessing; | |
| - whether the model was modified | |
| ## Licence | |
| Licensed under the Creative Commons Attribution-NonCommercial- | |
| NoDerivatives 4.0 International License (CC BY-NC-ND 4.0); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) | |
| Unless required by applicable law or agreed to in writing, this work | |
| is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS | |
| OF ANY KIND, either express or implied. See the License for the | |
| specific language governing permissions and limitations under the | |
| License. |