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
File size: 4,410 Bytes
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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. |