Instructions to use Astrixnet/cilo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Astrixnet/cilo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Astrixnet/cilo", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Astrixnet/cilo", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Astrixnet/cilo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Astrixnet/cilo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Astrixnet/cilo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Astrixnet/cilo

SGLang

How to use Astrixnet/cilo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Astrixnet/cilo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Astrixnet/cilo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Astrixnet/cilo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Astrixnet/cilo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Astrixnet/cilo with Docker Model Runner:
```
docker model run hf.co/Astrixnet/cilo
```

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Cilo

A multilingual conversational AI assistant for Indian languages

Developed by Provizoraq Labs — Project Astrix

Website · Provizoraq Labs

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}

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Safetensors

Model size

24B params

Tensor type

F16