What are the hardware requirements for Oc E Non V1?

Oc E Non V1 requires approximately 8 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Oc E Non V1 and how large is it?

Oc E Non V1 is developed by p-e-w, a model with 4b 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 Oc E Non V1?

Oc E Non V1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Oc E Non V1 by p-e-w — VRAM 8GB, 256K context

Name: Oc E Non V1
Author: p-e-w

Oc E Non V1 is an open-source language model by p-e-w. Features: 4b LLM, VRAM: 8GB, Context: 256K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.42.

Arxiv:2505.09388 Base model:finetune:qwen/qwen3... Base model:qwen/qwen3-4b-instr... Conversational Endpoints compatible Heretic Instruct Qwen3 Region:us Safetensors Sharded Tensorflow Terse

Model Card on HF 🤗: https://huggingface.co/p-e-w/Oc-e-Non-v1

Oc E Non V1 Parameters and Internals

LLM Name	Oc E Non V1
Repository 🤗	https://huggingface.co/p-e-w/Oc-e-Non-v1
Base Model(s)	Qwen3 4B Instruct 2507 Qwen/Qwen3-4B-Instruct-2507
Model Size	4b
Required VRAM	8 GB
Updated	2026-11-09
Maintainer	p-e-w
Model Type	qwen3
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-2 3.0 GB: 2-of-2
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	5.18.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

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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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