What are the hardware requirements for Code Gpt2?

Code Gpt2 requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Code Gpt2 and how large is it?

Code Gpt2 is developed by Sharathhebbar24, a model with 124.4m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Code Gpt2 perform on standard benchmarks?

Code Gpt2 has the following published scores: MMLU 25.03. Compare against reference models on this page or on the LLM Explorer leaderboards.

Code Gpt2 by Sharathhebbar24 — VRAM 0.2GB

Name: Code Gpt2
Author: Sharathhebbar24

Code Gpt2 is an open-source language model by Sharathhebbar24. Features: 124.4m LLM, VRAM: 0.2GB, License: apache-2.0, Instruction-Based, Code Generating, LLM Explorer Score: 0.16, Arc: 23.3, HellaSwag: 31, MMLU: 25.

Code Codegen Dataset:huggingfaceh4/ultracha... Dataset:intel/orca dpo pairs Dataset:mlabonne/codellama-2-2... Dataset:sharathhebbar24/evol-i... Dataset:sharathhebbar24/sql-cr... Dpo En Endpoints compatible Gpt2 Instruct Model-index Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Sharathhebbar24/code_gpt2

Code Gpt2 Parameters and Internals

Model Type

text generation

Training Details

Data Sources:

Sharathhebbar24/Evol-Instruct-Code-80k-v1

Model Architecture:

GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion.

LLM Name	Code Gpt2
Repository 🤗	https://huggingface.co/Sharathhebbar24/code_gpt2
Model Size	124.4m
Required VRAM	0.2 GB
Updated	2026-08-08
Maintainer	Sharathhebbar24
Model Type	gpt2
Instruction-Based	Yes
Model Files	0.2 GB
Supported Languages	en
Generates Code	Yes
Model Architecture	GPT2LMHeadModel
License	apache-2.0
Model Max Length	1024
Transformers Version	4.35.2
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	50257
Torch Data Type	float16
Activation Function	gelu_new

Rank the Code Gpt2 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