What are the hardware requirements for Sebot Hf?

Sebot Hf requires approximately 16.4 GB of VRAM and supports a context window of 40K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Sebot Hf and how large is it?

Sebot Hf is developed by Precontation, a model with 8.2b 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 Sebot Hf?

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

Sebot Hf by Precontation — VRAM 16.4GB, 40K context

Name: Sebot Hf
Author: Precontation

Sebot Hf is an open-source language model by Precontation. Features: 8.2b LLM, VRAM: 16.4GB, Context: 40K, License: apache-2.0, LLM Explorer Score: 0.32.

Base model:finetune:precontati... Base model:precontation/sebot-... Conversational En Endpoints compatible Qwen3 Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/Precontation/sebot-hf

Sebot Hf Parameters and Internals

LLM Name	Sebot Hf
Repository 🤗	https://huggingface.co/Precontation/sebot-hf
Base Model(s)	Sebot Hf Precontation/sebot-hf
Model Size	8.2b
Required VRAM	16.4 GB
Updated	2026-09-26
Maintainer	Precontation
Model Type	qwen3
Model Files	4.0 GB: 1-of-5 4.0 GB: 2-of-5 4.0 GB: 3-of-5 3.2 GB: 4-of-5 1.2 GB: 5-of-5
Supported Languages	en
Model Architecture	Qwen3ForCausalLM
License	apache-2.0
Context Length	40960
Model Max Length	40960
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Sebot Hf capabilities

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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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