Sophon-OSS-1B-v1 🧠

⚠️ Status: Model card prepared. Model weights coming soon (Q2 2026).

This repository is set up in advance. The actual model is currently in training. Follow @monodox for updates!

Sophon-OSS-1B-v1 is Monodox's first open-source language model, a 1-billion parameter model with primary focus on Malayalam language. Built for efficiency, accessibility, and research.

🇮🇳 Built in India, for the world.

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📊 Model Details

Attribute	Value
Organization	Monodox Technologies Pvt Ltd
Model Type	Causal Language Model (Transformer)
Parameters	1 Billion (1B)
Context Length	2048 tokens
License	Apache 2.0
Release Date	February 2026
Primary Languages	Malayalam + English

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� Why Malayalam First?

Malayalam is our home language and represents an underserved market in AI:

• 50M+ speakers worldwide
• Rich literary tradition spanning 1000+ years
• High digital literacy in Kerala
• Limited AI models available compared to Hindi/English
• Complex script with unique linguistic features

Starting with Malayalam allows us to:

• Build deep expertise in low-resource languages
• Serve our local community first
• Create techniques applicable to other Dravidian languages
• Prove our approach before scaling

Future languages: Hindi, Tamil, Telugu, Kannada, Bengali (2026-2027)

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�🎯 Key Features

✨ Multilingual by Design

• Native support for 2 languages
• Strong performance on Malayalam
• Code-switching capabilities

⚡ Efficient & Accessible

• Runs on consumer hardware (4GB+ VRAM)
• Mobile-friendly inference
• Low latency generation

🔬 Research-First

• Open weights and architecture
• Reproducible training
• Designed for fine-tuning

🌍 Community-Driven

• Apache 2.0 license (commercial use allowed)
• Active community support
• Regular updates

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🚀 Quick Start

Installation

pip install transformers torch

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model
model_name = "monodox/sophon-oss-1b-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
prompt = "The future of AI in India is"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_length=100,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Multilingual Example

# English
prompt_en = "Artificial Intelligence is"

# Malayalam
prompt_ml = "ആർട്ടിഫിഷ്യൽ ഇന്റലിജൻസ്"

# Generate in any language
for prompt in [prompt_en, prompt_ml]:
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=50)
    print(tokenizer.decode(outputs[0]))

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📐 Model Architecture

• Base Architecture: GPT-style Transformer decoder
• Layers: 24
• Hidden Size: 2048
• Attention Heads: 16
• Vocabulary Size: 50,000 (multilingual)
• Positional Encoding: Learned
• Activation: GELU

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📚 Training Details

Training Data

Sophon-OSS-1B-v1 was trained on a diverse multilingual corpus:

• Web Crawl: Common Crawl, OSCAR
• Indian Languages: IndicCorp, AI4Bharat datasets
• Code: GitHub repositories
• Books & Articles: Multilingual text
• Wikipedia: All supported languages

Total Tokens: ~200 Billion tokens
Data Curation: Extensive filtering for quality, safety, and diversity

Training Configuration

• Framework: PyTorch 2.0 + Hugging Face Transformers
• Hardware: NVIDIA A100 GPUs
• Training Time: 10 days
• Batch Size: 512
• Learning Rate: 3e-4
• Optimizer: AdamW
• Warmup Steps: 2000
• Precision: Mixed precision (FP16)

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📊 Performance

Benchmarks

Coming soon - benchmarks on MMLU, HellaSwag, ARC, and Indian language tasks

Supported Languages

Language	ISO Code	Performance
Malayalam	ml	⭐⭐⭐⭐
English	en	⭐⭐⭐⭐

Size Comparison

Model	Parameters	Languages	Malayalam Support
GPT-2	1.5B	English	❌ None
Llama-2	7B	Multilingual	⚠️ Limited
IndicBERT	110M	12 Indian	✅ Basic
Sophon-OSS-1B	1B	ML+EN	✅ Native

Sophon prioritizes quality over quantity, focusing deeply on Malayalam.

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💻 Hardware Requirements

Inference

Configuration	Min VRAM	Recommended
FP16	4GB	8GB
8-bit Quantized	2GB	4GB
4-bit Quantized	1GB	2GB

Compatible Hardware

✅ RTX 3060 (12GB)
✅ RTX 4070 (12GB)
✅ Apple M1/M2 (8GB+ unified memory)
✅ Google Colab (Free tier with limitations)
✅ High-end mobile devices (quantized)

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🎓 Use Cases

✅ Recommended

• Text generation and completion
• Conversational AI (chatbots)
• Multilingual applications
• Educational tools
• Content creation assistance
• Code completion (basic)
• Research and experimentation

⚠️ Limitations

• Not suitable for complex reasoning tasks
• Limited context window (2048 tokens)
• May generate biased or incorrect content
• Requires fact-checking for critical applications
• Not recommended for medical/legal advice

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⚖️ Bias, Risks & Limitations

Known Limitations

• Size: 1B parameters limit reasoning capability
• Context: 2048 token limit restricts long documents
• Knowledge Cutoff: Training data up to [date]
• Hallucinations: May generate plausible but incorrect information

Bias Considerations

• Trained on internet data (may contain biases)
• Gender, cultural, and regional biases possible
• Content filtering applied during training
• Users should verify outputs for fairness

Safety Measures

• Content filtering during training
• Red-teaming for harmful outputs
• Clear documentation of limitations
• Community reporting mechanism

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🔧 Fine-Tuning

Sophon-OSS-1B-v1 is designed for fine-tuning:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./sophon-finetuned",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    learning_rate=5e-5,
    # ... your config
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
)

trainer.train()

Recommended for:

• Domain-specific applications
• Custom language pairs
• Specialized tasks
• Personal assistants

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📖 Citation

If you use Sophon-OSS-1B-v1 in your research, please cite:

@misc{sophon-oss-1b-v1-2026,
  title={Sophon-OSS-1B-v1: A Multilingual Language Model for Indian Languages},
  author={Monodox Technologies Pvt Ltd},
  year={2026},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/monodox/sophon-oss-1b-v1}}
}

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🤝 Community & Support

Get Help

• 💬 Discussions
• 🐛 Report Issues
• 📧 Email: research@monodox.ai
• 🌐 Website: monodox.com

Contributing

We welcome contributions!

• Fine-tune for new languages
• Report bugs and issues
• Share use cases
• Improve documentation

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� Training Progress

We'll update this section as training progresses!

Current Status: Data collection and preprocessing
Last Updated: February 13, 2026

Timeline

• ✅ Model architecture finalized
• ✅ Training data collected
• 🔄 Data preprocessing (in progress)
• ⏳ Model training (starting Q1 2026)
• ⏳ Evaluation and benchmarking
• ⏳ Public release (Q2 2026)

Follow our blog for detailed progress updates.

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�🙏 Acknowledgments

Built with:

• Hugging Face Transformers
• PyTorch
• AI4Bharat for Indic language resources
• The open-source ML community

Special thanks to Kerala Startup Mission and supporters who made this possible.

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📜 License

This model is released under the Apache 2.0 License.

You are free to:

• ✅ Use commercially
• ✅ Modify and distribute
• ✅ Use privately
• ✅ Use for research

See LICENSE for full details.

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🔮 What's Next?

Upcoming Models:

• 🔥 Sophon-Lite-7B (Q3 2026)
• 🌍 Sophon-Indic-7B (Q4 2026)
• 💻 Sophon-Code-3B (2027)

🗺️ Sophon Roadmap

2026 Q2  →  Sophon-OSS-1B-v1 (Malayalam + English)
2026 Q3  →  Add Hindi, Tamil
2026 Q4  →  Sophon-Indic-7B (10+ languages)
2027 Q1  →  Sophon-Code-3B (Coding specialist)
2027 Q2+  →  Sophon-Lite-7B (Production-ready)

Follow us:

• 🐦 X: @monodox
• 💼 LinkedIn: Monodox Technologies Pvt Ltd
• 🌟 GitHub: github.com/monodox

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