What are the hardware requirements for EleutherAI GPT Neo 2.7B 4bits?

EleutherAI GPT Neo 2.7B 4bits requires approximately 1.7 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed EleutherAI GPT Neo 2.7B 4bits and how large is it?

EleutherAI GPT Neo 2.7B 4bits is developed by RichardErkhov, a model with 2.7b 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 EleutherAI GPT Neo 2.7B 4bits?

EleutherAI GPT Neo 2.7B 4bits is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

EleutherAI GPT Neo 2.7B 4bits by RichardErkhov — VRAM 1.7GB, 2K context

Name: EleutherAI GPT Neo 2.7B 4bits
Author: RichardErkhov

EleutherAI GPT Neo 2.7B 4bits is an open-source language model by RichardErkhov. Features: 2.7b LLM, VRAM: 1.7GB, Context: 2K, License: mit, LLM Explorer Score: 0.12.

Arxiv:2101.00027 4-bit Bitsandbytes Endpoints compatible Gpt neo Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neo-2.7B-4bits

EleutherAI GPT Neo 2.7B 4bits Parameters and Internals

Model Type

text generation, causal-lm

Use Cases

Areas:

Research, Commercial applications

Applications:

Text generation

Primary Use Cases:

Generating text from a prompt

Limitations:

Potentially socially unacceptable text, biases from training dataset

Considerations:

Human curation recommended to improve quality and censor undesirable content.

Supported Languages

languages_supported (en), proficiency_level (fluent)

Training Details

Data Sources:

EleutherAI/pile

Data Volume:

800GB

Methodology:

Masked autoregressive language model with cross-entropy loss

Training Time:

420 billion tokens over 400,000 steps

Model Architecture:

Transformer based architecture replicating GPT-3

Safety Evaluation

Ethical Considerations:

Model may produce socially unacceptable text; filtering recommended.

Responsible Ai Considerations

Mitigation Strategies:

Human curation or filtering of outputs is recommended to censor undesirable content and improve result quality.

Input Output

Input Format:

text input for text generation

Accepted Modalities:

text

Output Format:

generated text

LLM Name	EleutherAI GPT Neo 2.7B 4bits
Repository 🤗	https://huggingface.co/RichardErkhov/EleutherAI_-_gpt-neo-2.7B-4bits
Model Size	2.7b
Required VRAM	1.7 GB
Updated	2026-07-18
Maintainer	RichardErkhov
Model Type	gpt_neo
Model Files	1.7 GB
Supported Languages	en
Model Architecture	GPTNeoForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Transformers Version	4.39.3
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	50257
Torch Data Type	float16
Activation Function	gelu_new
Errors	replace

Rank the EleutherAI GPT Neo 2.7B 4bits capabilities

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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 EleutherAI GPT Neo 2.7B 4bits

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ChildModel 02	2K / 5.3 GB	8	0
GPT Neo 2.7B Lama	2K / 10.6 GB	5	0
PRIME2 Openai	2K / 6.6 GB	4	1
GPT Neo 2.7B	2K / 10.7 GB	56842	503
Pygmalion 2.7B	2K / 5.4 GB	214	59
... Style Transfer Using Examples	2K / 5.4 GB	5	1
Pygmalion 2.7B	2K / 0 GB	3	1
GPT Neo 2.7B Horni	2K / 6.6 GB	7	0
GPT Neo 2.7B Shinen	2K / 6.6 GB	5	0
GPT Neo 2.7B Horni LN	2K / 6.6 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than RichardErkhov/EleutherAI_-_gpt-neo-2.7B-4bits.