What are the hardware requirements for Dekillnovelist V2?

Dekillnovelist V2 requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Dekillnovelist V2 and how large is it?

Dekillnovelist V2 is developed by dekill, 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 Dekillnovelist V2?

Dekillnovelist V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Dekillnovelist V2 by dekill — VRAM 0.2GB

Name: Dekillnovelist V2
Author: dekill

Dekillnovelist V2 is an open-source language model by dekill. Features: 7b LLM, VRAM: 0.2GB, License: other, Instruction-Based, LLM Explorer Score: 0.3.

Autotrain Base model:finetune:mistralai/... Base model:mistralai/mistral-7... Conversational Endpoints compatible Instruct Lora Mistral Peft Region:us Safetensors Tensorboard

Model Card on HF 🤗: https://huggingface.co/dekill/dekillnovelist-v2

Dekillnovelist V2 Parameters and Internals

LLM Name	Dekillnovelist V2
Repository 🤗	https://huggingface.co/dekill/dekillnovelist-v2
Base Model(s)	mistralai/Mistral-7B-Instruct-v0.2 mistralai/Mistral-7B-Instruct-v0.2
Model Size	7b
Required VRAM	0.2 GB
Updated	2026-08-11
Maintainer	dekill
Instruction-Based	Yes
Model Files	0.2 GB 14.5 GB 0.0 GB
Model Architecture	AutoModelForCausalLM
License	other
Is Biased	none
Tokenizer Class	TokenizersBackend
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|up_proj\|k_proj\|gate_proj\|v_proj\|o_proj\|down_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16

Rank the Dekillnovelist V2 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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