What are the hardware requirements for FRS Sum?

FRS Sum requires a context window of 0.5K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed FRS Sum and how large is it?

FRS Sum is developed by venky1, a model with 10.5m 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 FRS Sum?

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

FRS Sum by venky1 — 0.5K context

Name: FRS Sum
Author: venky1

FRS Sum is an open-source language model by venky1. Features: 10.5m LLM, Context: 0.5K, License: mit, LLM Explorer Score: 0.32.

Attendance En Hr-assistant Llama Region:us Safetensors Slm

Model Card on HF 🤗: https://huggingface.co/venky1/FRS_sum

FRS Sum Parameters and Internals

LLM Name	FRS Sum
Repository 🤗	https://huggingface.co/venky1/FRS_sum
Model Size	10.5m
Required VRAM	0 GB
Updated	2026-09-09
Maintainer	venky1
Model Type	llama
Model Files	0.0 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	mit
Context Length	512
Model Max Length	512
Transformers Version	5.16.1
Tokenizer Class	TokenizersBackend
Padding Token	<\|pad\|>
Vocabulary Size	2028

Rank the FRS Sum 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 FRS Sum

Best Alternatives	Context / RAM	Downloads	Likes
10.5B V1	4K / 20.7 GB	8	0
St Vicuna V1.3 10.5B Ppl	2K / 20.9 GB	13	9
St Vicuna V1.3 10.5B Taylor	2K / 20.9 GB	6	9