What are the hardware requirements for Opt 125M Email Generation?

Opt 125M Email Generation requires approximately 0.5 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 125M Email Generation and how large is it?

Opt 125M Email Generation is developed by pszemraj, a model with 125m 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 125M Email Generation?

Opt 125M Email Generation is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Opt 125M Email Generation by pszemraj — VRAM 0.5GB, 2K context

Name: Opt 125M Email Generation
Author: pszemraj

Opt 125M Email Generation is an open-source language model by pszemraj. Features: 125m LLM, VRAM: 0.5GB, Context: 2K, License: other, LLM Explorer Score: 0.03.

125m Auto-complete Base model:facebook/opt-125m Base model:finetune:facebook/o... Dataset:aeslc Email Endpoints compatible Generated from trainer Non-commercial Opt Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/pszemraj/opt-125m-email-generation

Opt 125M Email Generation Parameters and Internals

Model Type

text generation, email autocomplete

Use Cases

Areas:

non-commercial

Applications:

email communication

Primary Use Cases:

email autocomplete, text generation

Additional Notes

There is currently a bug with the Huggingface API for OPT models, users are advised to test via Colab notebook.

Training Details

Data Sources:

aeslc dataset

Methodology:

fine-tuning

Input Output

Input Format:

text input

Accepted Modalities:

text

Output Format:

text generation

Performance Tips:

Generate longer emails by using it in a text-generation pipeline object.

LLM Name	Opt 125M Email Generation
Repository 🤗	https://huggingface.co/pszemraj/opt-125m-email-generation
Base Model(s)	Opt 125M facebook/opt-125m
Model Size	125m
Required VRAM	0.5 GB
Updated	2026-05-26
Maintainer	pszemraj
Model Type	opt
Model Files	0.5 GB 0.5 GB 0.0 GB
Model Architecture	OPTForCausalLM
License	other
Context Length	2048
Model Max Length	2048
Transformers Version	4.20.1
Tokenizer Class	GPT2Tokenizer
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 125M Email Generation 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 125M Email Generation

Best Alternatives	Context / RAM	Downloads	Likes
Opt 125M	2K / 0.3 GB	9414291	252
...25M Gqa Ub 6 Best For KV Cache	2K / 0.5 GB	207	0
...125M Qcqa Ub 6 Best For Q Loss	2K / 0.5 GB	57	0
...5M Qcqa Ub 6 Best For KV Cache	2K / 0.5 GB	58	0
Galactica 125M Cot	2K / 0.5 GB	5	0
Galactica Ref	2K / 0.5 GB	5	0
Galactica 125M DPO	2K / 0.5 GB	7	0
Galactica 125M DPO Pos	2K / 0.5 GB	5	0
BertQA	2K / 0.5 GB	5	0
BertQA	2K / 0.5 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than pszemraj/opt-125m-email-generation.