What are the hardware requirements for Gpt2023?

Gpt2023 requires approximately 0.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gpt2023 and how large is it?

Gpt2023 is developed by crumb, a model with 137m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gpt2023 perform on standard benchmarks?

Gpt2023 has the following published scores: MMLU 25.05, LMSYS ELO 1345. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gpt2023 by crumb — VRAM 0.3GB

Name: Gpt2023
Author: crumb

Gpt2023 is an open-source language model by crumb. Features: 137m LLM, VRAM: 0.3GB, License: mit, HF Score: 28.2, LLM Explorer Score: 0.27, ELO: 1345, Arc: 21.9, HellaSwag: 31.1, MMLU: 25.1, TruthfulQA: 40.7, WinoGrande: 50.1, GSM8K: 0.3.

En Endpoints compatible Gpt2 Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/crumb/gpt2023

Gpt2023 Benchmarks

LMSys ELO: 1345 vs 1272 (so35)^5.7%

ARC: 21.93 vs 96.7 (so35)^-77.3%

HellaSwag: 31.11 vs 95.3 (gpt4)^-67.4%

MMLU: 25.05 vs 88.3 (so35)^-71.6%

TruthfulQA: 40.71 vs 59 (gpt4)^-31%

WinoGrande: 50.12 vs 87.5 (gpt4)^-42.7%

GSM8K: 0.3 vs 96.4 (so35)^-99.7%

LLME Score: 0.26922

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

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Gpt2023 Parameters and Internals

Model Type

causal-lm

Use Cases

Areas:

Research

Limitations:

Lack of awareness of some recent events due to finetuning on a limited dataset

Supported Languages

en (Fluent)

Training Details

Data Sources:

common crawl sites, ArXiv, GitHub

Data Volume:

2.23 billion tokens

Methodology:

Finetuning on existing GPT-2 model with learning rate adjustments

Context Length:

1024

Training Time:

79.32 hours

Hardware Used:

12GB RTX3060

Model Architecture:

Transformer-based architecture, left-to-right causal language model

Input Output

Input Format:

Text input, up to 1024 tokens

Accepted Modalities:

text

Output Format:

Text generation

Performance Tips:

Setting a seed can help achieve reproducible results

LLM Name	Gpt2023
Repository 🤗	https://huggingface.co/crumb/gpt2023
Model Size	137m
Required VRAM	0.3 GB
Updated	2026-04-12
Maintainer	crumb
Model Type	gpt2
Model Files	0.3 GB 0.3 GB
Supported Languages	en
Model Architecture	GPT2LMHeadModel
License	mit
Model Max Length	1024
Transformers Version	4.29.0.dev0
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	50257
Torch Data Type	bfloat16
Activation Function	gelu_new

Best Alternatives to Gpt2023

Best Alternatives	Context / RAM	Downloads	Likes
Gpt2	0K / 0.5 GB	15964184	3230
GPT2 137M Reasoner V1.0	0K / 0.5 GB	9	1
Phantasor 137M	0K / 0.5 GB	211	1
Phantasor V0.1 137M	0K / 0.5 GB	93	1
Phantasor V0.2 137M	0K / 0.5 GB	73	1
Phantasor V0.3 137M	0K / 0.5 GB	37	1
Gpt2 Auth	0K / 0.5 GB	69	0
My GPT2	0K / 0.5 GB	809	0
Gpt2 Test	0K / 0.5 GB	1017	0
Xuanxuan	0K / 0.3 GB	7	0

Rank the Gpt2023 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Release v20260328a

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Gpt2023 by crumb

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Gpt2023 Benchmarks

Gpt2023 Parameters and Internals

Best Alternatives to Gpt2023

Rank the Gpt2023 Capabilities

What open-source LLMs or SLMs are you in search of? 53570 in total.