What are the hardware requirements for Vemana Base?

Vemana Base requires approximately 1.1 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Vemana Base and how large is it?

Vemana Base is developed by udgamlabs, a model with 278.2m 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 Vemana Base?

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

Vemana Base by udgamlabs — VRAM 1.1GB, 4K context

Name: Vemana Base
Author: udgamlabs

Vemana Base is an open-source language model by udgamlabs. Features: 278.2m LLM, VRAM: 1.1GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.37.

Base-model Dataset:ai4bharat/sangraha Dataset:huggingfacefw/fineweb-... Endpoints compatible Gemma4 Gemma4 text Region:us Safetensors Small-language-model Te Telugu Trained-from-scratch Udgamlabs

Model Card on HF 🤗: https://huggingface.co/udgamlabs/Vemana-Base

Vemana Base Parameters and Internals

LLM Name	Vemana Base
Repository 🤗	https://huggingface.co/udgamlabs/Vemana-Base
Model Size	278.2m
Required VRAM	1.1 GB
Updated	2026-10-31
Maintainer	udgamlabs
Model Type	gemma4_text
Model Files	1.1 GB
Supported Languages	te
Model Architecture	Gemma4ForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	32000

Rank the Vemana Base 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