Cilo

A multilingual conversational AI assistant for Indian languages

Developed by Provizoraq Labs — Project Astrix

Website · Provizoraq Labs

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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

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• May produce inaccurate or outdated information; verify important facts.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

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

_{Built by Provizoraq Labs · Project Astrix · astrix.network}