What are the hardware requirements for Quietstar 8 Ahead?

Quietstar 8 Ahead requires approximately 14.5 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Quietstar 8 Ahead and how large is it?

Quietstar 8 Ahead is developed by ezelikman, a model with 7.3b 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 Quietstar 8 Ahead?

Quietstar 8 Ahead is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Quietstar 8 Ahead by ezelikman — VRAM 14.5GB, 32K context

Name: Quietstar 8 Ahead
Author: ezelikman

Quietstar 8 Ahead is an open-source language model by ezelikman. Features: 7.3b LLM, VRAM: 14.5GB, Context: 32K, LLM Explorer Score: 0.12.

Arxiv:2403.09629 Dataset:open-web-math/open-web... Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/ezelikman/quietstar-8-ahead

Quietstar 8 Ahead Parameters and Internals

Model Type

text generation

Training Details

Data Sources:

open-web-math/open-web-math

Methodology:

continued pretraining using Quiet-STaR for generating 8 thought tokens before each output token.

LLM Name	Quietstar 8 Ahead
Repository 🤗	https://huggingface.co/ezelikman/quietstar-8-ahead
Model Size	7.3b
Required VRAM	14.5 GB
Updated	2026-07-23
Maintainer	ezelikman
Model Type	mistral
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.6 GB: 3-of-3
Model Architecture	MistralForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	bfloat16

Rank the Quietstar 8 Ahead 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 Quietstar 8 Ahead

Best Alternatives	Context / RAM	Downloads	Likes
Dictalm2.0 Instruct	32K / 14.5 GB	2791	25
My Tokenizer 100 10000	32K / 22.3 GB	12	0
My Tokenizer 50 20000	32K / 14.6 GB	7	0
Dictalm2.0	32K / 14.5 GB	368	28
Mis	32K / 14.6 GB	6	0
Dictalm2 It Qa Fine Tune	32K / 14.5 GB	26	7
Misjava Api 060924 V3 Merged	32K / 14.6 GB	5	0
... Fine Tuned Alpaca Gpt4 Hebrew	32K / 14.5 GB	201	0
Dictalm2.0 Instruct Fine Tuned	32K / 14.5 GB	21	0
Swallow Hermes St V1	4K / 14.6 GB	14	14

Note: green Score (e.g. "73.2") means that the model is better than ezelikman/quietstar-8-ahead.