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⚡ Sarath OM

Sarath OM

Intelligence. Innovation. Possibility.

Sarath OM — AI-powered software and technology ecosystem

Ollama Model · Hugging Face Model · Official Website · GitHub

🚀 Overview

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

The published Ollama listing describes the project as designed for greater capability, faster inference, higher throughput, and scaling to larger models. Its current published variant is listed with text input, a 128K context window, and approximately 2.0 GB model size.

This Model Card documents the model listing, setup instructions, integration examples, intended evaluation areas, limitations, and official resources.

Project Scope

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

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 inclusion of Sarath OM in the ecosystem does not imply ownership or authorship of the underlying foundation model. Architecture, upstream origin, training provenance, and model-weight ownership must be established from the actual published model configuration and upstream documentation.

✨ Published Model Information

Capability Published details
Text input Listed
Tool support Advertised
Image input Not listed
Vision Not listed
Context window 128K tokens
Model size Approximately 2.0 GB
Available variant latest
Runtime Ollama

Capabilities should be tested against the actual published model and runtime configuration.

📊 Model Specifications

Property Details
Model name Sarath OM
Hugging Face repository sarath-om/sarath-om
Ollama identifier sarath-om/sarath-om
Ollama namespace sarath-om
Default tag latest
Project Sarath OM
Developer and project owner Rishabh Srivastava
Distribution runtime Ollama
Listed model size Approximately 2.0 GB
Listed context window 128K tokens
Input modality Text
Tool support Advertised on Ollama
Architecture Not independently documented here
Base model Not yet verified
Parameter count Not yet confirmed
Quantization Confirm from model configuration
Training datasets Not documented
Evaluation benchmarks No independently verified results included
Hardware requirements Depend on model configuration and runtime
License Verify model and upstream distribution terms

The listed download size is not a guarantee of required RAM or VRAM. Actual memory consumption depends on runtime configuration, context length, and hardware.

🎯 Intended Use

Potential applications to evaluate, depending on the verified model behavior, include:

  • Text-based conversational interfaces.
  • General instruction-following workflows.
  • Developer productivity applications.
  • Tool-assisted application workflows.
  • Structured information processing.
  • Long-context text interaction.
  • AI application prototypes and experimentation.

These are potential evaluation areas, not guarantees that the model performs every task accurately.

📦 Getting Started with Ollama

Ollama is the documented runtime for this model listing.

1. Install Ollama

Download Ollama:

https://ollama.com/download

Verify the installation:

ollama --version

2. Download Sarath OM 

ollama pull sarath-om/sarath-om

3. Run Sarath OM 

ollama run sarath-om/sarath-om

Try a prompt:

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

Use /bye to exit the interactive session.

4. Inspect the Model 

ollama show sarath-om/sarath-om

List downloaded models:

ollama list

Check running models:

ollama ps

Inspect detailed metadata through the local API:

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

💻 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",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant. Provide clear and accurate responses."
      },
      {
        "role": "user",
        "content": "Explain modern software architecture."
      }
    ],
    "stream": false
  }'

The generated response is available in message.content.

Python Integration

Install the Ollama Python package:

pip install ollama

Example:

from ollama import chat

MODEL = "sarath-om/sarath-om"


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";

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();

🛠️ 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 production application should:

  1. Define available functions and their input schemas.
  2. Send the function definitions with the conversation.
  3. Inspect any tool calls returned by the model.
  4. Validate function names and arguments.
  5. Check user permissions and authorization.
  6. Execute only approved application functions.
  7. Return tool results to the conversation.

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

🔌 Compatible Application Integrations

The Ollama listing provides launch examples for compatible developer applications:

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

These integrations depend on compatible application versions and Ollama configuration.

⚙️ Context and Runtime Configuration

The Ollama listing displays a 128K context window.

Actual usable context and resource consumption depend on the model configuration, runtime settings, and available system memory.

Inspect model metadata:

ollama show sarath-om/sarath-om

For advanced runtime configuration, see:

Measure memory usage and latency on the target hardware before selecting production settings.

🧪 Evaluation and Performance

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

A reproducible evaluation should examine:

  • Instruction-following accuracy.
  • Text generation quality.
  • Long-context retrieval and consistency.
  • Structured output reliability.
  • Tool selection and argument accuracy.
  • Latency and hardware utilization.
  • Failure cases and known limitations.

Record the model identifier, runtime version, hardware, context configuration, test dataset, and evaluation methodology.

The listing's design description is not a measured performance benchmark.

🔍 Limitations and Risks

  • AI-generated responses may be incorrect, incomplete, or inconsistent.
  • Model quality can vary with prompts, context length, runtime settings, and hardware.
  • Image and vision support are not listed for the current Ollama variant.
  • Tool calls require application-side validation and authorization.
  • Bias, privacy, reliability, and misuse risks should be evaluated before deployment.
  • The model should not be the sole authority for high-impact medical, legal, or financial decisions.

🔐 Responsible Use and Security

  • Keep API credentials out of frontend code and public repositories.
  • Protect local Ollama endpoints from untrusted network access.
  • Validate tool calls, arguments, and external actions.
  • Apply authorization, rate limits, and request timeouts.
  • Review cloud and external service data-handling policies before submitting sensitive information.
  • Verify important AI-generated information independently.
  • Respect applicable licenses and third-party rights.

🧭 Resources — Ollama and Hugging Face

Ollama Resources

Hugging Face Resources

Official Sarath OM Resources

👨‍💻 Owner Details

Project: Sarath OM
Developer and Project Owner: Rishabh Srivastava

Developer Portfolio and Social Media

🌐 Sarath AI — Official Social Media

📄 Version Information

Field Value
Model Sarath OM
Repository sarath-om/sarath-om
Ollama identifier sarath-om/sarath-om
Published variant latest
Listed size 2.0 GB
Listed context 128K
Runtime Ollama

Keep this Model Card synchronized with the actual published model configuration, available variants, verified capabilities, and applicable license terms.

⚡ SARATH OM
Intelligence. Innovation. Possibility.

Building useful digital experiences through practical technology and responsible integration.

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