What are the hardware requirements for Dadjokes Tuned Opt?

Dadjokes Tuned Opt requires approximately 1.3 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 Dadjokes Tuned Opt and how large is it?

Dadjokes Tuned Opt is developed by gnumanth, a model with 350m 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 Dadjokes Tuned Opt?

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

Dadjokes Tuned Opt by gnumanth — VRAM 1.3GB, 2K context

Name: Dadjokes Tuned Opt
Author: gnumanth

Dadjokes Tuned Opt is an open-source language model by gnumanth. Features: 350m LLM, VRAM: 1.3GB, Context: 2K, License: mit, LLM Explorer Score: 0.11.

Base model:facebook/opt-350m Base model:finetune:facebook/o... Dataset:gnumanth/dad-jokes En Endpoints compatible Gnumanth/dadjokes-trained-opt Opt Region:us Safetensors Sft Tensorboard Trl

Model Card on HF 🤗: https://huggingface.co/gnumanth/dadjokes-tuned-opt

Dadjokes Tuned Opt Parameters and Internals

Model Type

text-generation

Supported Languages

en (full proficiency)

Training Details

Data Sources:

https://huggingface.co/datasets/gnumanth/dad-jokes

Methodology:

SFT Trained simple model for fun!

LLM Name	Dadjokes Tuned Opt
Repository 🤗	https://huggingface.co/gnumanth/dadjokes-tuned-opt
Base Model(s)	Opt 350M facebook/opt-350m
Model Size	350m
Required VRAM	1.3 GB
Updated	2026-08-04
Maintainer	gnumanth
Model Type	opt
Model Files	1.3 GB 0.0 GB
Supported Languages	en
Model Architecture	OPTForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Transformers Version	4.38.1
Tokenizer Class	GPT2Tokenizer
Padding Token	<pad>
Vocabulary Size	50272
Torch Data Type	float32
Activation Function	relu
Errors	replace

Rank the Dadjokes Tuned Opt 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

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Note: green Score (e.g. "73.2") means that the model is better than gnumanth/dadjokes-tuned-opt.