What are the hardware requirements for Distilgpt2?

Distilgpt2 requires approximately 0.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Distilgpt2 and how large is it?

Distilgpt2 is developed by distilbert, a model with 88.2m 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 Distilgpt2?

Distilgpt2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Distilgpt2 by distilbert — VRAM 0.4GB

Name: Distilgpt2
Author: distilbert

Distilgpt2 is an open-source language model by distilbert. Features: 88.2m LLM, VRAM: 0.4GB, License: apache-2.0, LLM Explorer Score: 0.12.

Model Card on HF 🤗: https://huggingface.co/distilbert/distilgpt2

Distilgpt2 Parameters and Internals

Model Type

Transformer-based Language Model, Text Generation

Use Cases

Areas:

Research applications, Commercial applications

Applications:

Writing assistance, Creative writing and art, Entertainment

Primary Use Cases:

Text generation

Limitations:

Bias related to race and gender, Not suitable for applications where factual correctness is required

Considerations:

Careful consideration of the bias and context of usage. Not recommended for direct human interaction without thorough bias assessment.

Additional Notes

DistilGPT2 is designed to facilitate faster, resource-efficient text generation applications.

Supported Languages

English (native)

Training Details

Data Sources:

OpenWebTextCorpus (an open-source reproduction of OpenAI’s WebText dataset)

Methodology:

Knowledge distillation

Hardware Used:

8 16GB V100 GPUs

Model Architecture:

Transformer

Responsible Ai Considerations

Fairness:

DistilGPT2 suffers from persistent bias issues similar to those described for GPT-2. The distilled versions have shown reductions in toxicity and bias compared to their teacher models, yet still present statistically significant bias.

Mitigation Strategies:

Ongoing research to mitigate bias using additional techniques for distilled models.

Input Output

Input Format:

Text input for generating text

Accepted Modalities:

text

Output Format:

Generated text

Performance Tips:

Use of seed setting for reproducibility

LLM Name	Distilgpt2
Repository 🤗	https://huggingface.co/distilbert/distilgpt2
Model Size	88.2m
Required VRAM	0.4 GB
Updated	2026-07-25
Maintainer	distilbert
Model Type	gpt2
Model Files	0.4 GB 0.4 GB
Supported Languages	en
Model Architecture	GPT2LMHeadModel
License	apache-2.0
Model Max Length	1024
Vocabulary Size	50257
Activation Function	gelu_new

Rank the Distilgpt2 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 Distilgpt2

Best Alternatives	Context / RAM	Downloads	Likes
Leo DistilGPT Medical	0K / 0.4 GB	15	1
Distilgpt2 HC3	0K / 0.3 GB	222	1
Distilgpt2 Emailgen V2	0K / 0.3 GB	373	5
Distilgpt2 Emailgen	0K / 0.4 GB	212	4
Distilgpt2 Fables Demo	0K / 0.3 GB	12	0
Distilgpt2 Nepali	0K / 0.3 GB	54	7
Distilgpt2 Tiny Conversational	0K / 0.3 GB	386	5

Note: green Score (e.g. "73.2") means that the model is better than distilbert/distilgpt2.