What are the hardware requirements for Opt 350M Instruct?

Opt 350M Instruct 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 Opt 350M Instruct and how large is it?

Opt 350M Instruct is developed by nnpy, 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 Opt 350M Instruct?

Opt 350M Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Opt 350M Instruct by nnpy — VRAM 1.3GB, 2K context

Name: Opt 350M Instruct
Author: nnpy

Opt 350M Instruct is an open-source language model by nnpy. Features: 350m LLM, VRAM: 1.3GB, Context: 2K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.07.

Dataset:openchat/openchat shar... Endpoints compatible Instruct Opt Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/nnpy/opt-350m-instruct

Opt 350M Instruct Parameters and Internals

Model Type

text generation

Input Output

Accepted Modalities:

text

LLM Name	Opt 350M Instruct
Repository 🤗	https://huggingface.co/nnpy/opt-350m-instruct
Model Size	350m
Required VRAM	1.3 GB
Updated	2026-07-12
Maintainer	nnpy
Model Type	opt
Instruction-Based	Yes
Model Files	1.3 GB 1.3 GB
Model Architecture	OPTForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.30.1
Beginning of Sentence Token	</s>
End of Sentence Token	</s>
Unk Token	</s>
Vocabulary Size	50272
Torch Data Type	float32
Activation Function	relu
Errors	replace

Rank the Opt 350M Instruct 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 350M Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Aira OPT 350M	2K / 0 GB	9	0
LongForm OPT 350M	2K / 1.3 GB	9	5
...speed Chat Step2 Model Opt350m	2K / 0.7 GB	4	1
Chat Opt 350M Reward Deepspeed	2K / 0.7 GB	17	7

Note: green Score (e.g. "73.2") means that the model is better than nnpy/opt-350m-instruct.