What are the hardware requirements for Chandra?

Chandra requires approximately 17.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Chandra and how large is it?

Chandra is developed by datalab-to, a model with 8.8b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Chandra?

Chandra is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Chandra by datalab-to — VRAM 17.5GB

Name: Chandra
Author: datalab-to

Chandra is an open-source language model by datalab-to. Features: 8.8b LLM, VRAM: 17.5GB, License: openrail, LLM Explorer Score: 0.34.

Conversational Deploy:azure Endpoints compatible Eval-results Image-text-to-text Ocr Qwen3 vl Region:us Safetensors Sharded Tensorflow Vlm

Model Card on HF 🤗: https://huggingface.co/datalab-to/chandra

Chandra Parameters and Internals

LLM Name	Chandra
Repository 🤗	https://huggingface.co/datalab-to/chandra
Model Size	8.8b
Required VRAM	17.5 GB
Updated	2026-10-02
Maintainer	datalab-to
Model Type	qwen3_vl
Model Files	5.0 GB: 1-of-4 4.9 GB: 2-of-4 4.9 GB: 3-of-4 2.7 GB: 4-of-4
Model Architecture	Qwen3VLForConditionalGeneration
License	openrail
Model Max Length	262144
Transformers Version	4.57.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Errors	replace

Rank the Chandra capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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