What are the hardware requirements for Legal Rag Model?

Legal Rag Model requires approximately 2.2 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Legal Rag Model and how large is it?

Legal Rag Model is developed by Callmeyash11, a model with 3.3b 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 Legal Rag Model?

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

Legal Rag Model by Callmeyash11 — VRAM 2.2GB, 128K context

Name: Legal Rag Model
Author: Callmeyash11

Legal Rag Model is an open-source language model by Callmeyash11. Features: 3.3b LLM, VRAM: 2.2GB, Context: 128K, License: apache-2.0, LLM Explorer Score: 0.23.

4-bit Base model:callmeyash11/legal-... Base model:quantized:callmeyas... Bitsandbytes Conversational En Endpoints compatible Llama Region:us Safetensors Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/Callmeyash11/legal-rag-model

Legal Rag Model Parameters and Internals

LLM Name	Legal Rag Model
Repository 🤗	https://huggingface.co/Callmeyash11/legal-rag-model
Base Model(s)	Legal Rag Model Callmeyash11/legal-rag-model
Model Size	3.3b
Required VRAM	2.2 GB
Updated	2026-08-14
Maintainer	Callmeyash11
Model Type	llama
Model Files	2.2 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	5.5.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256

Rank the Legal Rag Model capabilities

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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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