Introducing Horus LLM Series
Introducing the Horus 1.0 Models
A state-of-the-art family of Large Language Models, engineered for deep reasoning, multilingual precision, and efficient on-device intelligence.
Models & Architecture

Horus 1.0 4B
Text-to-Text
4B ParameterText Generation
Horus 1.0 4B is optimized for high-performance extraction and generation. Built on a transformer-based architecture, it balances computational efficiency with exceptional knowledge density.
100%
Open Source Weights
Global Community Impact
Total Horus Models Downloads
0
Trusted by researchers and developers worldwide across Hugging Face and enterprise mirrors.
How to Use
1
Install the framework in Python
pip install neuralnode2
Implementation Guide
Basic setup and loading:
import neuralnode as nn
# Choose your model version (replace with your preferred version)
MODEL_ID = "tokenaii/horus/Horus-1.0-4B-Q4_K_M.gguf" # 4-bit for low VRAM
# Download and load
model = nn.HorusModel(MODEL_ID).load()
# Use immediately
response = model.chat([{"role": "user", "content": "Hello!"}])
print(response["content"])One-liner: chat in a single chain:
import neuralnode as nn
# One-liner: create model, load it, and chat in a single chain
response = nn.HorusModel("tokenaii/horus/Horus-1.0-4B-Q4_K_M.gguf").load().chat(
[{"role": "user", "content": "What is AI?"}]
)
# Print the response content
print(response["content"])Performance Metrics
| Benchmark Metric | Horus 1.0 (4B) | Llama-3.1 (8B) | Avg Peers |
|---|---|---|---|
| Reasoning (MMLU) | 85.0% | 69.0% | 71.0% |
| Coding (SWE-bench) | 55.0% | 30.0% | 40.0% |
| Terminal (Logic) | 84.0% | 55.0% | 50.0% |
| HellaSwag (Knowledge) | 85.0% | 80.0% | 75.0% |
| Browse (General) | 88.0% | 45.0% | 40.0% |
Hardware Capability
Horus 1.0 4B
Minimum: 16GB RAM / 8GB VRAM
Target: Int4 / GGUF
Quantized Versions (GGUF)
| Format | File Size | Min RAM | Min VRAM | Quality | Best For |
|---|---|---|---|---|---|
| F16 | 9.03 GB | 12 GB | 10 GB | Maximum quality | High-end GPUs (RTX 3090, A100) |
| Q8_0 | 4.8 GB | 6 GB | 5 GB | Near-lossless | RTX 3060 12GB, RTX 4060 |
| Q6_K | 3.71 GB | 5 GB | 4 GB | Excellent | RTX 3060, RTX 4060 Laptop |
| Q5_K_M | 3.23 GB | 4 GB | 3.5 GB | Very Good | GTX 1650, RTX 3050 |
| Q4_K_M | 2.78 GB | 3.5 GB | 3 GB | Good | Entry-level GPUs, CPU-only |
Standardized Verification



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