README.md

---
license: apache-2.0
language:
- en
- hi
- bn
- gu
- kn
- ml
- mr
- or
- pa
- ta
- te
library_name: transformers
pipeline_tag: text-generation
tags:
- cilo
- conversational
- assistant
- indic
- multilingual
---

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# Cilo

**A multilingual conversational AI assistant for Indian languages**

*Developed by Provizoraq Labs — Project Astrix*

[Website](https://astrix.network) · [Provizoraq Labs](https://astrix.network)

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---

## Overview

Cilo is a 24B-parameter instruction-tuned assistant optimized for natural,
helpful conversation across English and major Indian languages. It is designed
for production assistant workloads where responsiveness, multilingual fluency,
and a consistent assistant persona matter.

## Language Support

Cilo supports **English and 10 Indic languages**:

| | | |
|---|---|---|
| English | Hindi | Bengali |
| Gujarati | Kannada | Malayalam |
| Marathi | Odia | Punjabi |
| Tamil | Telugu | |

> Conversational quality is strongest in **English and Hindi**; other supported
> languages are inherited from the base model's broad Indic capabilities.

## Highlights

- **Multilingual** — fluent responses across English and major Indian languages, including code-switching (e.g. Hinglish).
- **Instruction-tuned** — aligned for clear, task-oriented, conversational responses.
- **24B parameters** — strong reasoning and instruction-following at a deployable scale.
- **Consistent persona** — reliable assistant identity across turns.

## Intended Use

Conversational assistants, customer support, education, and general-purpose
multilingual text generation.

**Out of scope:** high-stakes decisions (legal, medical, financial) without
human review, and any use prohibited by the license.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "masterjiii/cilo"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

messages = [
    {"role": "system", "content": "You are Cilo, a helpful AI assistant."},
    {"role": "user", "content": "Introduce yourself."},
]
inputs = tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to(model.device)
out = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
```

## Chat Template

Cilo uses a system / user / assistant chat format. Always provide a system
message to anchor the assistant persona for best results.

## Training

Cilo was instruction-tuned with a curated conversational and identity dataset
using parameter-efficient fine-tuning (LoRA), then merged to a standalone model.

## Limitations

- May produce inaccurate or outdated information; verify important facts.
- Conversational quality is strongest in English and Hindi.
- Like all LLMs, it can be sensitive to prompt phrasing.

## License

Released under the **Apache 2.0** license.

## Citation

```bibtex
@misc{cilo2025,
  title  = {Cilo: A Multilingual Conversational Assistant for Indian Languages},
  author = {Provizoraq Labs},
  year   = {2025},
  note   = {Project Astrix},
  url    = {https://astrix.network}
}
```

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<sub>Built by Provizoraq Labs · Project Astrix · astrix.network</sub>
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