README.md

---

tags:
- compressed-tensors
license: apache-2.0
license_name: modified-mit
library_name: transformers
pipeline_tag: image-text-to-text
---


<div align="center">
  <h1>🇮🇳 Kalki 1.5</h1>
  <h3>India's First Fully Agentic 1T Parameter AI Model</h3>
</div>

<div align="center" style="line-height:1">
  <a href="https://www.upmarking.com/code" target="_blank"><img alt="Chat" src="https://img.shields.io/badge/🤖-Kalki--Code-ff6b6b?color=1783ff&logoColor=white"/></a>
  <a href="https://www.upmarking.com" target="_blank"><img alt="Homepage" src="https://img.shields.io/badge/Homepage-Upmarking-white?logo=Kalki&logoColor=white"/></a>
</div>

<div align="center" style="line-height: 1; margin-top: 10px;">
  <a href="https://huggingface.co/upmarking" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Upmarking-ffc107?color=ffc107&logoColor=white"/></a>
  <a href="https://twitter.com/kalki_upmarking" target="_blank"><img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-Kalki.ai-white?logo=x&logoColor=white"/></a>
  <a href="https://discord.gg/TYU2fdJykW" target="_blank"><img alt="Discord" src="https://img.shields.io/badge/Discord-Kalki.ai-white?logo=discord&logoColor=white"/></a>
  <a href="https://modelscope.cn/organization/upmarking" target="_blank"><img alt="ModelScope" src="https://img.shields.io/badge/ModelScope-Upmarking-white?labelColor=rgb(99%2C%2074%2C%20255)"/></a>
</div>

<div align="center" style="line-height: 1; margin-top: 10px;">
  <a href="https://huggingface.co/upmarking/kalki-1.5/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/badge/License-Modified_MIT-f5de53?&color=f5de53"/></a>
</div>

---

## 🚀 1. Model Introduction

**Kalki 1.5** represents a monumental leap in sovereign AI capabilities as **India's First Fully Agentic 1T Parameter AI**. Built upon the breakthrough Kalki Mixture-of-Experts (MoE) architecture, Kalki 1.5 is custom-tuned for complex, long-horizon software engineering tasks and multi-modal tool use. 

Kalki 1.5 features substantial optimizations over predecessor models:
* **Unprecedented Scale**: A 1-Trillion parameter Mixture-of-Experts model, activating 32 Billion parameters per token.
* **Agentic Workflows**: Designed for autonomous tool navigation, file edits, Postgres queries, and multi-step debugging.
* **Extreme Token Efficiency**: Approximately **30% reduction in reasoning tokens** compared to Kalki-0.6, delivering much faster completion speeds.
* **Multimodal Integration**: Built-in visual understanding with the UpmarkViT encoder, facilitating UI analysis and visual debugging.

---

## 📊 2. Model Summary

<div align="center">

| Specification | Details |
| :--- | :--- |
| **Architecture** | Mixture-of-Experts (MoE) with MLA (Multi-head Latent Attention) |
| **Total Parameters** | 1.0T |
| **Activated Parameters** | 32B |
| **Number of Layers** | 61 (includes dense/routing layer) |
| **Vocabulary Size** | 160K |
| **Context Length** | 256K tokens |
| **Activation Function** | SwiGLU |
| **Vision Encoder** | UpmarkViT (400M parameters) |

</div>

---

## 🏆 3. Evaluation Results

Kalki 1.5 outperforms leading global models across critical coding and agentic benchmarks. The table below compares performance:

<div align="center">
<table>
<thead>
<tr>
<th align="center">Benchmark</th>
<th align="center">Kalki-0.6</th>
<th align="center">GPT-5.5</th>
<th align="center">Claude Opus 4.8</th>
<th align="center">Kalki 1.5 🇮🇳</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center" colspan=5><strong>Coding Excellence (Higher is Better)</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">Kalki Code Bench v2</td>
<td align="center" style="vertical-align: middle">50.9</td>
<td align="center" style="vertical-align: middle">69.0</td>
<td align="center" style="vertical-align: middle">67.4</td>
<td align="center" style="vertical-align: middle"><strong>82.5</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">Program Bench</td>
<td align="center" style="vertical-align: middle">48.3</td>
<td align="center" style="vertical-align: middle">69.1</td>
<td align="center" style="vertical-align: middle">63.8</td>
<td align="center" style="vertical-align: middle"><strong>76.8</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MLS Bench Lite</td>
<td align="center" style="vertical-align: middle">26.7</td>
<td align="center" style="vertical-align: middle">35.5</td>
<td align="center" style="vertical-align: middle">42.8</td>
<td align="center" style="vertical-align: middle"><strong>58.2</strong></td>
</tr>
<tr>
<td align="center" colspan=5><strong>Agentic & Tool Use (Higher is Better)</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">Kalki Claw 24/7 Bench</td>
<td align="center" style="vertical-align: middle">42.9</td>
<td align="center" style="vertical-align: middle">52.8</td>
<td align="center" style="vertical-align: middle">50.4</td>
<td align="center" style="vertical-align: middle"><strong>68.4</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MCP Atlas</td>
<td align="center" style="vertical-align: middle">69.4</td>
<td align="center" style="vertical-align: middle">79.4</td>
<td align="center" style="vertical-align: middle">81.3</td>
<td align="center" style="vertical-align: middle"><strong>91.2</strong></td>
</tr>
<tr>
<td align="center" style="vertical-align: middle">MCP Mark Verified</td>
<td align="center" style="vertical-align: middle">72.8</td>
<td align="center" style="vertical-align: middle">92.9</td>
<td align="center" style="vertical-align: middle">76.4</td>
<td align="center" style="vertical-align: middle"><strong>94.5</strong></td>
</tr>
</tbody>
</table>
</div>

<details>
<summary><b>Testing Methodology & Footnotes</b></summary>

1. **General Testing Details**
   - Kalki 1.5 was tested with thinking mode enabled via Kalki Code CLI at temperature = 1.0, top-p = 0.95, and a 262,144-token context length. GPT-5.5 ran in Codex with xhigh mode, and Opus 4.8 in Claude Code with xhigh mode.
2. **Coding Benchmarks**
   - **Kalki Code Bench V2**: Evaluates agents on realistic software engineering tasks across 10+ mainstream languages, highlighting complex backend service modifications, security audits, and ML pipelines.
   - **Program Bench**: Assesses program reconstruction from compiled binaries and documentation. Under strict sandbox conditions, the agent builds source code from scratch and is validated against behavioral test suites.
   - **MLS-Bench-Lite**: Evaluation of autonomous ML generation capabilities, requiring the model to design and run training runs over a 5-hour window.
3. **Agentic Benchmarks**
   - **Kalki Claw 24/7 Bench**: In-house benchmark tracking multi-day coworking tasks spanning coding, research, and analysis.
   - **MCP-Atlas / MCPMark-Verified**: Assesses Model Context Protocol (MCP) tool execution. Evaluated with a 100-step tool budget and 32k max tokens per step.

</details>

---

## ⚡ 4. Native INT4 Quantization

Kalki 1.5 natively supports highly-optimized INT4 quantization. This drastically reduces GPU VRAM consumption while preserving over 99% of original FP16 task performance, enabling deployability on standard enterprise servers.

---

## ⚙️ 5. Deployment

> [!Note]
> Access Kalki 1.5's high-speed API directly via [platform.upmarking.com](https://platform.upmarking.com) with standard OpenAI/Anthropic SDK compatibility.

For local deployment, Kalki 1.5 can be served using the following inference frameworks:
* **vLLM**
* **SGLang**
* **KTransformers**

Ensure you have the required `transformers` library version:
```bash

pip install "transformers>=4.57.1,<5.0.0"

```
Refer to the [Model Deployment Guide](docs/deploy_guidance.md) for step-by-step setup guides.

---

## 💻 6. Usage Examples

Below is a simple chat completion example calling the Kalki 1.5 API in Thinking mode.

```python

import openai



def simple_chat(client: openai.OpenAI, model_name: str):

    messages = [

        {'role': 'system', 'content': 'You are Kalki, India\'s First Fully Agentic 1T Parameter AI created by Upmarking.'},

        {

            'role': 'user',

            'content': [

                {'type': 'text', 'text': 'How can we optimize memory constraints in MoE architectures?'}

            ],

        },

    ]

    response = client.chat.completions.create(

        model=model_name, 

        messages=messages, 

        stream=False, 

        max_tokens=4096

    )

    print('====== Reasoning Process ======')

    print(response.choices[0].message.reasoning)

    print('====== Final Answer ======')

    print(response.choices[0].message.content)

```

---
<div align="center">
  Made with ❤️ by Upmarking.
</div>