What are the hardware requirements for Backtalk 80M?

Backtalk 80M requires approximately 0.2 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 Backtalk 80M and how large is it?

Backtalk 80M is developed by eventvoid, a model with 80m 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 Backtalk 80M?

Backtalk 80M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Backtalk 80M by eventvoid — VRAM 0.2GB, 2K context

Name: Backtalk 80M
Author: eventvoid

Backtalk 80M is an open-source language model by eventvoid. Features: 80m LLM, VRAM: 0.2GB, Context: 2K, License: mit, LLM Explorer Score: 0.32.

Code Conversational En Endpoints compatible Llama Region:us Reversed-text Safetensors Small-model Trained-from-scratch

Model Card on HF 🤗: https://huggingface.co/eventvoid/backtalk-80m

Backtalk 80M Parameters and Internals

LLM Name	Backtalk 80M
Repository 🤗	https://huggingface.co/eventvoid/backtalk-80m
Model Size	80m
Required VRAM	0.2 GB
Updated	2026-10-08
Maintainer	eventvoid
Model Type	llama
Model Files	0.2 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	mit
Context Length	2048
Model Max Length	2048
Transformers Version	4.57.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
Vocabulary Size	49152
Torch Data Type	float16

Rank the Backtalk 80M 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

Best Alternatives to Backtalk 80M

Best Alternatives	Context / RAM	Downloads	Likes
Nuevamol 80M Base	8K / 0.2 GB	19	0
PicoLM 80M Instruct	2K / 0.2 GB	1016	2