What are the hardware requirements for Mistral Nemo Heretic LoRA Rank128?

Mistral Nemo Heretic LoRA Rank128 requires approximately 3.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mistral Nemo Heretic LoRA Rank128 and how large is it?

Mistral Nemo Heretic LoRA Rank128 is developed by yamatazen. 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 Mistral Nemo Heretic LoRA Rank128?

Mistral Nemo Heretic LoRA Rank128 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Mistral Nemo Heretic LoRA Rank128 by yamatazen — VRAM 3.6GB

Name: Mistral Nemo Heretic LoRA Rank128
Author: yamatazen

Mistral Nemo Heretic LoRA Rank128 is an open-source language model by yamatazen. Features: LLM, VRAM: 3.6GB, Instruction-Based, LLM Explorer Score: 0.24.

Adapter Base model:adapter:muxodious/m... Base model:muxodious/mistral-n... Finetuned Instruct Lora Mergekit Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/yamatazen/Mistral-Nemo-Heretic-LoRA-Rank128

Mistral Nemo Heretic LoRA Rank128 Parameters and Internals

LLM Name	Mistral Nemo Heretic LoRA Rank128
Repository 🤗	https://huggingface.co/yamatazen/Mistral-Nemo-Heretic-LoRA-Rank128
Base Model(s)	Mistral Nemo Instruct 2407 ... Instruct 2407 Absolute Heresy unsloth/Mistral-Nemo-Instruct-2407 MuXodious/Mistral-Nemo-Instruct-2407-absolute-heresy
Required VRAM	3.6 GB
Updated	2026-09-09
Maintainer	yamatazen
Instruction-Based	Yes
Model Files	3.6 GB
Model Architecture	Adapter
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|o_proj\|gate_proj\|v_proj\|down_proj\|k_proj\|up_proj
LoRA Alpha	128
LoRA Dropout	0
R Param	128

Rank the Mistral Nemo Heretic LoRA Rank128 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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