What are the hardware requirements for Opt Peter 2.7B?

Opt Peter 2.7B requires approximately 10.6 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 Opt Peter 2.7B and how large is it?

Opt 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 Opt Peter 2.7B?

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

Opt Peter 2.7B by pszemraj — VRAM 10.6GB, 2K context

Name: Opt Peter 2.7B
Author: pszemraj

Opt Peter 2.7B is an open-source language model by pszemraj. Features: 2.7b LLM, VRAM: 10.6GB, Context: 2K, License: other|apache-2.0, LLM Explorer Score: 0.01.

Ai-msgbot Base model:facebook/opt-2.7b Base model:finetune:facebook/o... Chatbot Dialogue Doi:10.57967/hf/0233 Endpoints compatible Generated from trainer Non-commercial Opt Pytorch Region:us Safetensors

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

Opt Peter 2.7B Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

non-commercial

Applications:

dialogue, conversational applications, chatbot

Primary Use Cases:

text generation

Limitations:

Model is too large for API use, require high RAM

Considerations:

Use responsibly, as it is a fine-tuned version of the base model.

Additional Notes

Sharded checkpoint available due to large size of model file.

Training Details

Data Sources:

WhatsApp text messages, iMessages

Data Volume:

about 80k messages

Methodology:

Fine-tuning

LLM Name	Opt Peter 2.7B
Repository 🤗	https://huggingface.co/pszemraj/opt-peter-2.7B
Base Model(s)	facebook/opt-2.7b facebook/opt-2.7b
Model Size	2.7b
Required VRAM	10.6 GB
Updated	2026-07-21
Maintainer	pszemraj
Model Type	opt
Model Files	10.6 GB 10.6 GB 0.0 GB
Model Architecture	OPTForCausalLM
License	other\|apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.19.2
Beginning of Sentence Token	</s>
End of Sentence Token	</s>
Unk Token	</s>
Vocabulary Size	50265
Torch Data Type	float32
Activation Function	relu
Errors	replace

Rank the Opt 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 Opt Peter 2.7B

Best Alternatives	Context / RAM	Downloads	Likes
LLmRa 2.7B	2K / 10.6 GB	642	1
Opt 2.7B	2K / 5.3 GB	35827	89
OPT 2.7B Nerybus Mix	2K / 5.5 GB	194	10
OPT 2.7B Erebus	2K / 5.3 GB	380	42
OPT 2.7B Nerys V2	2K / 5.3 GB	124	6
Opt 2.7B Realtime Chat V2	2K / 11.1 GB	9	0
LongForm OPT 2.7B	2K / 5.3 GB	5	3
Opt 2.7B Wikitext2	2K / 11.1 GB	6	0
Opt 2.7B Realtime Chat	2K / 11.1 GB	7	2
Opt 2.7B AWQ	2K / 1.6 GB	5	0

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