⚡ Sarath OM 1.1

Intelligence. Innovation. Possibility.

Sarath OM 1.1 — AI-powered software and model integration experience

Ollama Model · Hugging Face Model · Official Website · GitHub

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🚀 Overview

Sarath OM 1.1 is a model listing in the Sarath OM ecosystem, available through the Ollama runtime.

The published Ollama listing advertises text and image input, vision, tool support, and thinking capabilities. It also displays a 256K context window and approximately 3.3 GB model size.

This README provides installation instructions, model information, API integration examples, potential application areas, and official project resources.

Project Scope

Sarath AI is a software application and integration platform, not an AI model-development or model-training project.

Sarath AI does not claim to create, train, or fine-tune proprietary foundation models. Its software connects application workflows with supported external AI providers, model runtimes, APIs, and services.

The listing of Sarath OM 1.1 does not imply ownership or authorship of the underlying foundation model. Its architecture, upstream origin, weight ownership, and training provenance should be determined from the actual published model configuration and upstream documentation.

✨ Published Capabilities

Capability	Published status
Text input	Listed
Image input	Listed
Vision	Listed
Tool calling	Listed
Thinking	Listed
Context window	256K tokens displayed
Local execution	latest variant
Cloud execution	cloud variant
Runtime	Ollama

Supported functionality may vary according to the model variant, runtime version, application, and configuration.

📊 Model Specifications

Property	Details
Model name	Sarath OM 1.1
Ollama namespace	sarath-om
Model identifier	sarath-om/sarath_om-1.1
Default tag	latest
Cloud tag	cloud
Published model size	Approximately 3.3 GB
Published context window	256K tokens
Input modalities	Text and images
Vision capability	Listed
Tool support	Listed
Thinking support	Listed
Runtime	Ollama
Project	Sarath OM
Developer and project owner	Rishabh Srivastava
Architecture	Refer to published model configuration
Base model	Not independently verified
Parameter count	Not yet confirmed
Quantization	Verify from selected model variant
Training datasets	Not documented
Benchmark results	No independently verified results included
Hardware requirements	Depend on model configuration and runtime
License	Verify model and upstream distribution terms

Resource note: The displayed 3.3 GB model size is not a guarantee that 3.3 GB of RAM or VRAM is sufficient. Actual resource requirements depend on context size, runtime settings, quantization, and hardware.

📦 Getting Started

1. Install Ollama

Download Ollama for your operating system:

https://ollama.com/download

Verify your installation:

ollama --version

2. Download Sarath OM 1.1

Pull the default model variant:

ollama pull sarath-om/sarath_om-1.1

3. Run Sarath OM 1.1

ollama run sarath-om/sarath_om-1.1

Start with a prompt:

Hello! Introduce yourself and explain what you can help me with.

Use /bye to exit the interactive session.

4. Inspect Model Details

ollama show sarath-om/sarath_om-1.1

List downloaded models:

ollama list

Check currently running models:

ollama ps

Inspect detailed metadata using the local API:

curl http://localhost:11434/api/show \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sarath-om/sarath_om-1.1",
    "verbose": true
  }'

☁️ Ollama Cloud

The Ollama listing also displays a cloud variant.

Sign in to Ollama:

ollama signin

Run the cloud variant:

ollama run sarath-om/sarath_om-1.1:cloud

Cloud inference uses Ollama’s hosted service and requires an internet connection and eligible account access.

Privacy: Cloud requests are processed by the hosted service. Review Ollama’s privacy and data-handling terms before sending confidential or sensitive information.

Documentation: https://docs.ollama.com/cloud

💻 API Integration

Ollama provides a local HTTP API for connecting the model to applications.

cURL — Chat API

curl http://localhost:11434/api/chat \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sarath-om/sarath_om-1.1",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant. Provide clear and accurate answers."
      },
      {
        "role": "user",
        "content": "Explain modern software architecture."
      }
    ],
    "stream": false
  }'

The generated response is available in message.content.

Python Integration

Install the official Ollama Python package:

pip install ollama

Example:

from ollama import chat

MODEL = "sarath-om/sarath_om-1.1"


def main() -> None:
    response = chat(
        model=MODEL,
        messages=[
            {
                "role": "system",
                "content": "You are a helpful assistant.",
            },
            {
                "role": "user",
                "content": "Explain how modern APIs work.",
            },
        ],
        stream=False,
    )

    print(response.message.content or "")


if __name__ == "__main__":
    main()

JavaScript / Node.js Integration

Install the Ollama JavaScript package:

npm install ollama

Example:

import ollama from "ollama";

const MODEL = "sarath-om/sarath_om-1.1";

async function main() {
  try {
    const response = await ollama.chat({
      model: MODEL,
      messages: [
        {
          role: "user",
          content: "Explain modern application architecture.",
        },
      ],
      stream: false,
    });

    console.log(response.message.content);
  } catch (error) {
    console.error("Ollama request failed:", error);
    process.exitCode = 1;
  }
}

main();

🖼️ Vision and Image Input

The published Ollama listing advertises image input and vision support.

Example using Python:

from ollama import chat

MODEL = "sarath-om/sarath_om-1.1"

response = chat(
    model=MODEL,
    messages=[
        {
            "role": "user",
            "content": "Describe the objects and important details in this image.",
            "images": ["./sample-image.jpg"],
        }
    ],
    stream=False,
)

print(response.message.content or "")

Replace ./sample-image.jpg with the path to your image.

Image formats, sizes, and processing limits depend on the underlying model configuration and runtime.

🧠 Thinking Support

The Ollama listing displays thinking support.

For models that expose thinking controls, Ollama provides a think option.

Example:

from ollama import chat

response = chat(
    model="sarath-om/sarath_om-1.1",
    messages=[
        {
            "role": "user",
            "content": "Compare REST APIs and GraphQL APIs.",
        }
    ],
    think=True,
    stream=False,
)

print(response.message.content or "")

Verify the selected model variant and installed Ollama client version for the exact supported thinking options.

🛠️ Tool Calling

The Ollama listing advertises tool support.

Tool calling allows an application to provide function definitions that the model can request during a conversation.

A typical integration workflow:

1. Define available functions and their input schemas.
2. Send the functions with the user conversation.
3. Inspect any tool calls returned by the model.
4. Validate function names and arguments.
5. Check user authorization before executing actions.
6. Send the tool result back to the conversation.

Never execute arbitrary shell commands, external API calls, or sensitive actions simply because the model requested them.

🔌 Compatible Application Integrations

The Ollama model listing provides launch examples for compatible developer applications:

Application	Launch command
Claude Code	ollama launch claude --model sarath-om/sarath_om-1.1
OpenCode	ollama launch opencode --model sarath-om/sarath_om-1.1
Hermes Agent	ollama launch hermes --model sarath-om/sarath_om-1.1
OpenClaw	ollama launch openclaw --model sarath-om/sarath_om-1.1

These integrations depend on compatible application versions, Ollama configuration, and account access where required.

🎯 Potential Use Cases

Depending on verified model behavior, potential areas for evaluation include:

• Conversational interfaces.
• Image understanding workflows.
• Long-context document interaction.
• Developer productivity applications.
• Tool-assisted workflows.
• Structured information processing.
• AI-powered software prototypes.
• Local AI experimentation.

These are potential application areas, not guarantees of accuracy or production suitability.

🔍 Evaluation and Limitations

No independently verified benchmark scores or comparative performance results are included in this README.

AI-generated responses may be inaccurate, incomplete, inconsistent, or unsuitable for particular tasks. Performance can vary with hardware, prompts, context size, model variant, and runtime configuration.

Before production deployment, evaluate representative tasks for accuracy, reliability, latency, resource use, safety, and privacy implications.

Do not use AI output as the sole basis for consequential medical, legal, financial, or other high-impact decisions.

🔐 Security and Responsible Use

• Keep API credentials out of frontend code and public repositories.
• Protect local API endpoints from untrusted network access.
• Validate tool calls, function names, arguments, and external actions.
• Apply authorization, rate limits, and request timeouts.
• Review cloud data-handling practices before submitting sensitive content.
• Verify AI-generated information before relying on it.
• Respect applicable licenses, third-party rights, and organizational security requirements.

🧭 Official Resources

Ollama Resources

• Sarath OM 1.1 model: https://ollama.com/sarath-om/sarath_om-1.1
• Sarath OM profile: https://ollama.com/sarath-om
• Ollama downloads: https://ollama.com/download
• Ollama documentation: https://docs.ollama.com/
• Ollama API: https://docs.ollama.com/api
• Ollama Cloud: https://docs.ollama.com/cloud
• Ollama Python library: https://github.com/ollama/ollama-python
• Ollama JavaScript library: https://github.com/ollama/ollama-js

Hugging Face Resources

• Sarath OM 1.1 model: https://huggingface.co/sarath-om/sarath_om-1.1
• Sarath OM profile: https://huggingface.co/sarath-om
• Hugging Face Models: https://huggingface.co/models
• Hugging Face documentation: https://huggingface.co/docs
• Model Cards documentation: https://huggingface.co/docs/hub/model-cards

Sarath OM Official Resources

• Official website: https://sarath.openflip.in/
• GitHub organization: https://github.com/Sarath-Om
• LinkedIn company page: https://www.linkedin.com/company/sarath-ai

👨‍💻 Owner Details

Project: Sarath OM
Developer and Owner: Rishabh Srivastava

Developer Portfolio and Social Media

• Portfolio: https://rishabhsrivastava.figma.site/
• X: https://x.com/RishabhSrivast_
• Instagram: https://www.instagram.com/rishabh__.srivastava/
• Threads: https://www.threads.com/@rishabh__.srivastava
• Facebook: https://www.facebook.com/rishabhsrivastava.i/
• LinkedIn: https://www.linkedin.com/in/rishabh-srivastava-rishabh
• GitHub: https://github.com/rishabhsrivastavarishabh
• YouTube: https://www.youtube.com/@RISHABH_SRIVASTAVA_OFFICIAL
• Kaggle: https://www.kaggle.com/rishabhsrivastava2

🌐 Sarath AI — Official Social Media

• Website: https://sarath.openflip.in/
• GitHub: https://github.com/Sarath-Om
• LinkedIn: https://www.linkedin.com/company/sarath-ai
• Instagram: https://www.instagram.com/sarath_ai/
• Threads: https://www.threads.com/@sarath_ai
• Facebook: https://www.facebook.com/sarath.ai.om
• YouTube: https://www.youtube.com/@Sarath_Ai

📄 Version Information

Field	Value
Model	Sarath OM 1.1
Release	1.1
Ollama identifier	sarath-om/sarath_om-1.1
Default variant	latest
Cloud variant	cloud
Runtime	Ollama

Keep this README synchronized with the actual published model configuration, available variants, verified features, and applicable license terms.

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⚡ SARATH OM
Intelligence. Innovation. Possibility.

Building useful digital experiences through practical technology and responsible integration.