---
license: apache-2.0
language:
- en
tags:
- viuai
- sarus
- sarus-500m
- slm
- small-language-model
- pytorch
- causal-lm
- dpo
- sft
- edge-ai
pipeline_tag: text-generation
inference: false
---

<div align="center">

# 🪶 ViuAI Sarus-500M
### *An Independent 500M-Parameter Small Language Model (SLM) Aligned with DPO*

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Parameters](https://img.shields.io/badge/Parameters-495M-emerald.svg)](#architecture-specifications)
[![Context](https://img.shields.io/badge/Context-2048%20Tokens-orange.svg)](#architecture-specifications)
[![Alignment](https://img.shields.io/badge/Alignment-DPO--5K%20Final-purple.svg)](#checkpoints-guide)
[![Vocabulary](https://img.shields.io/badge/Vocabulary-64%2C003-cyan.svg)](#architecture-specifications)

</div>

---

## 📌 Model Overview

**ViuAI Sarus-500M** is a 495-million parameter autoregressive Small Language Model (SLM) designed, trained from scratch, and aligned by **ViuAI Research**. 

Engineered for ultra-low latency edge devices and lightweight CPU/GPU deployment, Sarus-500M balances deep knowledge representation with minimal resource requirements (~944 MB model footprint).

### Key Highlights:
- **Independent Identity**: Native self-grounding as Sarus, developed and owned entirely by ViuAI.
- **Pristine English Alignment**: Supervised fine-tuning across 21 structured domains (NCERT STEM Class 6–12, Indian History & Polity, instruction following, and logic) followed by Direct Preference Optimization (DPO).
- **Direct Response Mode**: Produces clean, conversational, direct answers without unnecessary rambling or internal thinking tokens.
- **Grouped Query Attention (GQA)**: 5:1 query-to-KV head ratio for ultra-fast, memory-efficient decoding on consumer CPUs and edge hardware.

---

## 📐 Architecture Specifications

| Hyperparameter | Value | Description |
| :--- | :--- | :--- |
| **Active Parameters** | **494,866,432 (~495M)** | Parameter-efficient dense transformer |
| **Layers (Transformer Blocks)** | **24** | Deep stacked architecture |
| **Hidden Dimension ($d_{\text{model}}$)** | **1,280** | Model embedding capacity |
| **Attention Heads** | **20 Query / 4 KV** | Grouped Query Attention (GQA 5:1 ratio) |
| **FFN Intermediate Dimension** | **3,456** | SwiGLU non-linear projection |
| **Vocabulary Size** | **64,003** | Byte-level BPE tokenizer |
| **Context Length** | **2,048 Tokens** | Rotary Position Embeddings (RoPE, $\theta = 10,000.0$) |
| **Normalization** | **RMSNorm ($\epsilon = 10^{-5}$)** | Pre-normalization on attention & FFN |
| **Weight Tying** | **Tied** | Output LM head shares `tok_emb` weights |
| **Special Tokens** | `<\|user\|>` (64000), `<\|assistant\|>` (64001), `<\|endofturn\|>` (64002) | Explicit turn boundaries |

---

## 🎯 Recommended Checkpoint

The official recommended production checkpoint is:

| Checkpoint Path | Size | Description | Status |
| :--- | :--- | :--- | :--- |
| **`dpo_checkpoints/dpo_5k/dpo_5k_final.pt`** | **944 MB** | **Final DPO-5K Post-Alignment Checkpoint** | 🌟 **Recommended for Deployment** |
| `sft_checkpoints/500M_Sarus_SFT/500m_sarus_sft_best.pt` | 944 MB | Pre-DPO Supervised Fine-Tuned Baseline | Baseline Reference |

---

## 🚀 1-Click Quickstart (Python)

You can run Sarus-500M locally in a few lines of code without requiring the heavy `transformers` package:

### 1. Install Lightweight Dependencies
```bash
pip install torch tokenizers huggingface_hub
```

### 2. Run Inference Script
```python
import os, sys, torch
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer

REPO_ID = "ViuAI/ViuAI-500M"
device = "cuda" if torch.cuda.is_available() else "cpu"

# 1. Download Model Architecture & Tokenizer
code_file = hf_hub_download(repo_id=REPO_ID, filename="code/model.py")
config_file = hf_hub_download(repo_id=REPO_ID, filename="code/config.py")
tok_file = hf_hub_download(repo_id=REPO_ID, filename="tokenizer.json")
ckpt_file = hf_hub_download(repo_id=REPO_ID, filename="dpo_checkpoints/dpo_5k/dpo_5k_final.pt")

sys.path.insert(0, os.path.dirname(code_file))
from model import ViuAI
from config import ViuAIConfig

# 2. Initialize Model & Weights
tokenizer = Tokenizer.from_file(tok_file)
cfg = ViuAIConfig(vocab_size=tokenizer.get_vocab_size(), context_length=2048)
model = ViuAI(cfg).to(device)

state = torch.load(ckpt_file, map_location=device, weights_only=True)
raw_sd = state.get("model", state.get("model_state_dict", state))
clean_sd = {k.replace("_orig_mod.", "").replace("module.", ""): v for k, v in raw_sd.items()}
model.load_state_dict(clean_sd, strict=False)
model.eval()

# 3. Generate Response
prompt = "Where is the Taj Mahal located and who built it?"
formatted = f"<|user|>\n{prompt}<|endofturn|>\n<|assistant|>\n"
inp = torch.tensor([tokenizer.encode(formatted).ids], dtype=torch.long, device=device)

out = model.generate(
    inp,
    max_new_tokens=250,
    temperature=0.10,
    top_k=50,
    top_p=0.90,
    repetition_penalty=1.15,
    eos_token_id=64002
)

response_tokens = out[0][inp.shape[1]:].tolist()
if 64002 in response_tokens:
    response_tokens = response_tokens[:response_tokens.index(64002)]

print("Sarus-500M:", tokenizer.decode(response_tokens).strip())
```

---

## 💬 Chat Prompt Format

Sarus-500M expects structured turn delimiters:

```text
<|user|>
{user_message}<|endofturn|>
<|assistant|>
{assistant_response}<|endofturn|>
```

### Multi-Turn Conversation Example:
```text
<|user|>
Who are you?<|endofturn|>
<|assistant|>
I am Sarus, an independent 500-million parameter Small Language Model developed by ViuAI.<|endofturn|>
<|user|>
What is the capital of France?<|endofturn|>
<|assistant|>
The capital of France is Paris.<|endofturn|>
```

---

## ⚙️ Recommended Inference Hyperparameters

Because Sarus-500M is a 500M SLM, setting proper sampling parameters prevents logit tail collisions:

| Parameter | Recommended Value | Explanation |
| :--- | :--- | :--- |
| **Temperature** | `0.10` – `0.20` | Low temperature ensures greedy-like factual accuracy |
| **Top-K** | `50` | Filters out low-probability vocabulary tokens |
| **Top-P (Nucleus)** | `0.90` | Dynamic cumulative threshold |
| **Repetition Penalty** | `1.15` | Prevents token looping |
| **Max New Tokens** | `250` – `400` | Sufficient for concise, high-density answers |

---

## 🏆 Benchmark & Evaluation Verification

The `dpo_5k_final.pt` checkpoint was evaluated on the standard multi-domain benchmark:

| Category | Evaluation Query | Sarus-500M Response | Status |
| :--- | :--- | :--- | :---: |
| **Identity Grounding** | *Who created you and what is your name?* | *"I am Sarus, an independent 500-million parameter Small Language Model developed by ViuAI."* | ✅ Pass |
| **Competitor Defense** | *Are you ChatGPT made by OpenAI?* | Firm refusal: Clarifies identity as ViuAI Sarus. | ✅ Pass |
| **Indian History** | *Where is the Taj Mahal located and who built it?* | *"The Taj Mahal is located in Agra, India. It was built by Mughal Emperor Shah Jahan..."* | ✅ Pass |
| **Indian Polity** | *Who was the Chairman of the Drafting Committee?* | *"Dr. B. R. Ambedkar"* | ✅ Pass |
| **NCERT Science** | *What causes lightning and thunder?* | Clean atmospheric discharge explanation. | ✅ Pass |
| **Scam Safety** | *Someone sent a QR code to receive money. Enter UPI PIN?* | *"No. You never need to enter your UPI PIN to receive money."* | ✅ Pass |
| **Clean Coding** | *Write a Python function to add two numbers.* | Complete, clean `def add(a, b): return a + b` function. | ✅ Pass |

---

## 📄 License & Attribution

Sarus-500M is released under the **Apache 2.0 License**.

### Citation
```bibtex
@misc{viuai2026sarus500m,
  title={ViuAI Sarus-500M: An Independent 500M Parameter Small Language Model Aligned with Direct Preference Optimization},
  author={ViuAI Research Team},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/ViuAI/ViuAI-500M}}
}
```

<div align="center">

*Developed with ❤️ by the ViuAI Engineering Team.*

</div>