| ---
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| tags:
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| - compressed-tensors
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| license: apache-2.0
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| license_name: modified-mit
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| library_name: transformers
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| pipeline_tag: image-text-to-text
|
| ---
|
|
|
| <div align="center">
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| <h1>🇮🇳 Kalki 1.5</h1>
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| <h3>India's First Fully Agentic 1T Parameter AI Model</h3>
|
| </div>
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|
|
| <div align="center" style="line-height:1">
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| <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>
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| <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>
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| </div>
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|
|
| <div align="center" style="line-height: 1; margin-top: 10px;">
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| <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>
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| <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;">
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| <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>
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| </div>
|
|
|
| ---
|
|
|
| ## 🚀 1. Model Introduction
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|
|
| **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.
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|
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| Kalki 1.5 features substantial optimizations over predecessor models:
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| * **Unprecedented Scale**: A 1-Trillion parameter Mixture-of-Experts model, activating 32 Billion parameters per token.
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| * **Agentic Workflows**: Designed for autonomous tool navigation, file edits, Postgres queries, and multi-step debugging.
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| * **Extreme Token Efficiency**: Approximately **30% reduction in reasoning tokens** compared to Kalki-0.6, delivering much faster completion speeds.
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| * **Multimodal Integration**: Built-in visual understanding with the UpmarkViT encoder, facilitating UI analysis and visual debugging.
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|
|
| ---
|
|
|
| ## 📊 2. Model Summary
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|
|
| <div align="center">
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|
|
| | Specification | Details |
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| | :--- | :--- |
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| | **Architecture** | Mixture-of-Experts (MoE) with MLA (Multi-head Latent Attention) |
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| | **Total Parameters** | 1.0T |
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| | **Activated Parameters** | 32B |
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| | **Number of Layers** | 61 (includes dense/routing layer) |
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| | **Vocabulary Size** | 160K |
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| | **Context Length** | 256K tokens |
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| | **Activation Function** | SwiGLU |
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| | **Vision Encoder** | UpmarkViT (400M parameters) |
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|
|
| </div>
|
|
|
| ---
|
|
|
| ## 🏆 3. Evaluation Results
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|
|
| Kalki 1.5 outperforms leading global models across critical coding and agentic benchmarks. The table below compares performance:
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|
|
| <div align="center">
|
| <table>
|
| <thead>
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| <tr>
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| <th align="center">Benchmark</th>
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| <th align="center">Kalki-0.6</th>
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| <th align="center">GPT-5.5</th>
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| <th align="center">Claude Opus 4.8</th>
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| <th align="center">Kalki 1.5 🇮🇳</th>
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| </tr>
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| </thead>
|
| <tbody>
|
| <tr>
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| <td align="center" colspan=5><strong>Coding Excellence (Higher is Better)</strong></td>
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| </tr>
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| <tr>
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| <td align="center" style="vertical-align: middle">Kalki Code Bench v2</td>
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| <td align="center" style="vertical-align: middle">50.9</td>
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| <td align="center" style="vertical-align: middle">69.0</td>
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| <td align="center" style="vertical-align: middle">67.4</td>
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| <td align="center" style="vertical-align: middle"><strong>82.5</strong></td>
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| </tr>
|
| <tr>
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| <td align="center" style="vertical-align: middle">Program Bench</td>
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| <td align="center" style="vertical-align: middle">48.3</td>
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| <td align="center" style="vertical-align: middle">69.1</td>
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| <td align="center" style="vertical-align: middle">63.8</td>
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| <td align="center" style="vertical-align: middle"><strong>76.8</strong></td>
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| </tr>
|
| <tr>
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| <td align="center" style="vertical-align: middle">MLS Bench Lite</td>
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| <td align="center" style="vertical-align: middle">26.7</td>
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| <td align="center" style="vertical-align: middle">35.5</td>
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| <td align="center" style="vertical-align: middle">42.8</td>
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| <td align="center" style="vertical-align: middle"><strong>58.2</strong></td>
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| </tr>
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| <tr>
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| <td align="center" colspan=5><strong>Agentic & Tool Use (Higher is Better)</strong></td>
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| </tr>
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| <tr>
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| <td align="center" style="vertical-align: middle">Kalki Claw 24/7 Bench</td>
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| <td align="center" style="vertical-align: middle">42.9</td>
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| <td align="center" style="vertical-align: middle">52.8</td>
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| <td align="center" style="vertical-align: middle">50.4</td>
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| <td align="center" style="vertical-align: middle"><strong>68.4</strong></td>
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| </tr>
|
| <tr>
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| <td align="center" style="vertical-align: middle">MCP Atlas</td>
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| <td align="center" style="vertical-align: middle">69.4</td>
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| <td align="center" style="vertical-align: middle">79.4</td>
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| <td align="center" style="vertical-align: middle">81.3</td>
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| <td align="center" style="vertical-align: middle"><strong>91.2</strong></td>
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| </tr>
|
| <tr>
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| <td align="center" style="vertical-align: middle">MCP Mark Verified</td>
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| <td align="center" style="vertical-align: middle">72.8</td>
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| <td align="center" style="vertical-align: middle">92.9</td>
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| <td align="center" style="vertical-align: middle">76.4</td>
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| <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**
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| - 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**
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| - **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.
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| - **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.
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| - **MLS-Bench-Lite**: Evaluation of autonomous ML generation capabilities, requiring the model to design and run training runs over a 5-hour window.
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| 3. **Agentic Benchmarks**
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| - **Kalki Claw 24/7 Bench**: In-house benchmark tracking multi-day coworking tasks spanning coding, research, and analysis.
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| - **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**
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| * **SGLang**
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| * **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',
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| 'content': [
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| {'type': 'text', 'text': 'How can we optimize memory constraints in MoE architectures?'}
|
| ],
|
| },
|
| ]
|
| response = client.chat.completions.create(
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| 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> |
