Models4↗open-source on Hugging Face Downloads94k+↗downloads on Hugging Face

kitefish / models

$open-source model registry

01Minnow-OCR-1Bopen weights↗02Minnow-Em1-0.6Bopen weights↗03Reasoning-L1-10Bopen weights↗04Sonar-A1-TTSopen weights↗

no gate / no waitlistdownload →

Hindi

Sovereign Small Models

fine-tuned for your industry

We build compact, high-performance language models for BFSI, healthcare, pharma, and beyond. Open-source base models, sovereign deployment, built for India and the world.

Request Early Access→View Models

On-prem / air-gapped / cloud private

Open-source models

Built small.Reasoning big.

Compact models across OCR, embeddings, reasoning, and speech workloads, ready for domain fine-tuning and private deployment.

Document OCR

Minnow-OCR-1B

Model

Compact OCR model for document digitization and structured extraction workflows.

1BSize

HFDownloads

CurrentUpdated

Quick start / 1B

Minnow-OCR-1B

Document OCR

Open HF

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="KiteFishAI/Minnow-OCR-1B",
)

prompt = "Extract the invoice number, date, and total from this document."
response = pipe(prompt, max_new_tokens=200)

print(response[0]["generated_text"])

Quick start / 1B

Minnow-OCR-1B

Document OCR

Open HF

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="KiteFishAI/Minnow-OCR-1B",
)

prompt = "Extract the invoice number, date, and total from this document."
response = pipe(prompt, max_new_tokens=200)

print(response[0]["generated_text"])

Quick start / 10B

Reasoning-L1-10B

MoE reasoning

Open HF

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "KiteFishAI/Reasoning-L1-10B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

prompt = "Reason step by step: if a train covers 180 km in 3 hours, what is its speed?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=180)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Industry fine-tuning

One model family.Every domain.

Base models are the foundation. We adapt them into industry language, workflows, and edge cases with pilot partners.

BFSI workflows

Banking & Finance

Compliance document analysis, credit risk reasoning, regulatory Q&A, and financial report summarisation tuned for Indian BFSI workflows.

Risk reviewCredit memoRegulatory Q&A

Talk to us →View models

Why KiteFish

Small, sovereign,domain-ready.

Four design choices shape every KiteFish model: private deployment, compact size, industry depth, and open foundations.

01 / Sovereign by design

Run entirely on-premise

Your data never leaves your infrastructure. KiteFish models are built to deploy inside your environment — air-gapped, compliant, and fully under your control.

Learn more →

02 / Right-sized

Small models, real intelligence

From 0.6B to 10B parameters, our models are designed to run efficiently on hardware you already own, without sacrificing domain accuracy for size.

Learn more →

03 / Domain depth

Fine-tuned for your industry

General-purpose LLMs are trained for everything. KiteFish models are built to be fine-tuned deep into your specific domain — the language, the tasks, the edge cases.

Learn more →

04 / Open by default

Open-source foundation

All our base models are released on Hugging Face. Download, evaluate, and fine-tune freely. Enterprise support and custom deployments available on request.

Learn more →

The Team

Practitioners building for practitioners.

Founded by senior practitioners from Accenture's financial services and technology practice — with hands-on experience delivering AI at enterprise scale across India's largest organisations.

Combined patents across financial technology and pharmaceutical domains. Backed by Microsoft for Startups with active Azure A100 infrastructure and a published research paper on model compression.

Microsoft for StartupsAccenture AlumniMulti-domain patentsAzure A100LM_eval Benchmarked

Minnow-OCR-1B Released

Document OCR model

Minnow-Em1-0.6B Released

Embedding model

Reasoning-L1-10B Released

Flagship MoE reasoning model · 11B

Research Paper Published

MoE compression methodology

Microsoft for Startups Grant

Azure A100 infrastructure active

Sonar-A1-TTS and Sonar-A1-STT Released

Voice generation and transcription

Pitching Industry Partners

BFSI · Healthcare · Pharma · Legal

Founders story

Why we are building sovereign small models.

KiteFishAI started with a practical belief: advanced AI should be usable by teams that cannot send sensitive data outside their own systems. Our founders are building smaller, domain-ready models that enterprises can evaluate openly and deploy with control.

Built for teams that need AI inside their own infrastructure.

Focused on compact models that can be evaluated, owned, and deployed privately.

Designed around regulated workflows where accuracy, control, and cost all matter.

Meet KiteFishAI

// Model access

Build with KiteFishAI models.

OCR, embeddings, reasoning, speech, vision-language, and math models are ready for evaluation and domain fine-tuning.

Industry-tuned

BFSI, healthcare & pharma

Base models

Available on Hugging Face

Pilot access

Co-develop domain fine-tunes

Request Early Access→Download Base Models