What are the hardware requirements for Cognitive Hacker Gguf 0.2?

Cognitive Hacker Gguf 0.2 requires approximately 4.4 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Cognitive Hacker Gguf 0.2 and how large is it?

Cognitive Hacker Gguf 0.2 is developed by ebowwa, a model with 7b 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 Cognitive Hacker Gguf 0.2?

Cognitive Hacker Gguf 0.2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Cognitive Hacker Gguf 0.2 by ebowwa — VRAM 4.4GB

Name: Cognitive Hacker Gguf 0.2
Author: ebowwa

Cognitive Hacker Gguf 0.2 is an open-source language model by ebowwa. Features: 7b LLM, VRAM: 4.4GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.14.

4bit Base model:quantized:unsloth/m... Base model:unsloth/mistral-7b-... En Endpoints compatible Gguf Mistral Quantized Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/ebowwa/cognitive-hacker-gguf-0.2

Cognitive Hacker Gguf 0.2 Parameters and Internals

Model Type

text-generation-inference, transformers

Additional Notes

This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

LLM Name	Cognitive Hacker Gguf 0.2
Repository 🤗	https://huggingface.co/ebowwa/cognitive-hacker-gguf-0.2
Base Model(s)	unsloth/mistral-7b-v0.3-bnb-4bit unsloth/mistral-7b-v0.3-bnb-4bit
Model Size	7b
Required VRAM	4.4 GB
Updated	2026-07-19
Maintainer	ebowwa
Model Type	mistral
Model Files	4.4 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|4bit
Model Architecture	AutoModel
License	apache-2.0

Rank the Cognitive Hacker Gguf 0.2 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

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Note: green Score (e.g. "73.2") means that the model is better than ebowwa/cognitive-hacker-gguf-0.2.