What are the hardware requirements for Typtop10?

Typtop10 requires approximately 5.8 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Typtop10 and how large is it?

Typtop10 is developed by ChevalierJoseph, a model with 8b 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 Typtop10?

Typtop10 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Typtop10 by ChevalierJoseph — VRAM 5.8GB, 128K context

Name: Typtop10
Author: ChevalierJoseph

Typtop10 is an open-source language model by ChevalierJoseph. Features: 8b LLM, VRAM: 5.8GB, Context: 128K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.19.

4-bit 4bit Bitsandbytes Conversational En Endpoints compatible Instruct Llama Quantized Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/ChevalierJoseph/typtop10

Typtop10 Parameters and Internals

LLM Name	Typtop10
Repository 🤗	https://huggingface.co/ChevalierJoseph/typtop10
Base Model(s)	unsloth/meta-llama-3.1-8b-instruct-bnb-4bit unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
Model Size	8b
Required VRAM	5.8 GB
Updated	2026-08-11
Maintainer	ChevalierJoseph
Model Type	llama
Instruction-Based	Yes
Model Files	4.7 GB: 1-of-2 1.1 GB: 2-of-2
Supported Languages	en
Quantization Type	4bit
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.53.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Typtop10 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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