What are the hardware requirements for GPT Peter 2.7B?

GPT Peter 2.7B requires approximately 11.1 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 GPT Peter 2.7B and how large is it?

GPT Peter 2.7B is developed by pszemraj, 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 GPT Peter 2.7B?

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

GPT Peter 2.7B by pszemraj — VRAM 11.1GB, 2K context

Name: GPT Peter 2.7B
Author: pszemraj

GPT Peter 2.7B is an open-source language model by pszemraj. Features: 2.7b LLM, VRAM: 11.1GB, Context: 2K, LLM Explorer Score: 0.01.

Base model:eleutherai/gpt-neo-... Base model:finetune:eleutherai... Chatbot Gpt-neo Gpt-peter Gpt neo Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/pszemraj/gpt-peter-2.7B

GPT Peter 2.7B Parameters and Internals

Model Type

text generation

Use Cases

Areas:

research, commercial applications

Applications:

chatbot

Limitations:

The model is too large to use the inference API

Training Details

Data Sources:

WhatsApp texts, iMessage texts

Data Volume:

80k

Methodology:

fine-tuning

Hardware Used:

multi-GPU

Input Output

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Adding generation parameters such as `no_repeat_ngram_size` are recommended to get better generations

LLM Name	GPT Peter 2.7B
Repository 🤗	https://huggingface.co/pszemraj/gpt-peter-2.7B
Base Model(s)	EleutherAI/gpt-neo-2.7B EleutherAI/gpt-neo-2.7B
Model Size	2.7b
Required VRAM	11.1 GB
Updated	2026-07-31
Maintainer	pszemraj
Model Type	gpt_neo
Model Files	11.1 GB 11.1 GB 0.0 GB
Model Architecture	GPTNeoForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.18.0
Tokenizer Class	GPT2Tokenizer
Beginning of Sentence Token	<\|endoftext\|>
End of Sentence Token	<\|endoftext\|>
Unk Token	<\|endoftext\|>
Vocabulary Size	50257
Torch Data Type	bfloat16
Activation Function	gelu_new
Errors	replace

Rank the GPT Peter 2.7B 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 GPT Peter 2.7B

Best Alternatives	Context / RAM	Downloads	Likes
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
EleutherAI GPT Neo 2.7B 4bits	2K / 1.7 GB	9	0
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

Note: green Score (e.g. "73.2") means that the model is better than pszemraj/gpt-peter-2.7B.