What are the hardware requirements for BhagavadGita?

BhagavadGita requires approximately 14.5 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed BhagavadGita and how large is it?

BhagavadGita is developed by bajrangCoder, a model with 7.2b 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 BhagavadGita?

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

BhagavadGita by bajrangCoder — VRAM 14.5GB

Name: BhagavadGita
Author: bajrangCoder

BhagavadGita is an open-source language model by bajrangCoder. Features: 7.2b LLM, VRAM: 14.5GB, License: mit, Quantized, LLM Explorer Score: 0.12.

Conversational Dataset:bajrangcoder/bhagvatgi... Endpoints compatible Gguf Gita Mistral Pytorch Quantized Region:us Safetensors Sft Sharded Tensorflow Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/bajrangCoder/BhagavadGita

BhagavadGita Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

Spiritual Guidance, Educational Tool, Philosophical Inquiry

Primary Use Cases:

Obtain advice and spiritual insights inspired by the Bhagavad Gita, Aid in the study and understanding of the Bhagavad Gita, Explore philosophical questions through the lens of the Bhagavad Gita

LLM Name	BhagavadGita
Repository 🤗	https://huggingface.co/bajrangCoder/BhagavadGita
Model Size	7.2b
Required VRAM	14.5 GB
Updated	2026-07-12
Maintainer	bajrangCoder
Model Type	mistral
Model Files	4.4 GB 7.7 GB 5.0 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3 5.0 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	AutoModel
License	mit
Model Max Length	32768
Tokenizer Class	LlamaTokenizer
Padding Token	[control_768]

Rank the BhagavadGita 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

Best Alternatives to BhagavadGita

Best Alternatives	Context / RAM	Downloads	Likes
Mistral7b V0.3 Ultrachat200k	0K / 14.5 GB	35	2
ThaliaBeta GGUF	0K / 4.4 GB	6	0

Note: green Score (e.g. "73.2") means that the model is better than bajrangCoder/BhagavadGita.