What are the hardware requirements for Cve Model?

Cve Model requires approximately 0.3 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Cve Model and how large is it?

Cve Model is developed by mx003, 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 Cve Model?

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

Cve Model by mx003 — VRAM 0.3GB

Name: Cve Model
Author: mx003

Cve Model is an open-source language model by mx003. Features: 8b LLM, VRAM: 0.3GB, License: llama3.1, Instruction-Based, LLM Explorer Score: 0.23.

Axolotl Base model:adapter:unsloth/met... Base model:unsloth/meta-llama-... Conversational Dataset:mx003/cve Endpoints compatible Instruct Llama Lora Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/mx003/cve_model

Cve Model Parameters and Internals

LLM Name	Cve Model
Repository 🤗	https://huggingface.co/mx003/cve_model
Base Model(s)	unsloth/Meta-Llama-3.1-8B-Instruct unsloth/Meta-Llama-3.1-8B-Instruct
Model Size	8b
Required VRAM	0.3 GB
Updated	2026-04-18
Maintainer	mx003
Instruction-Based	Yes
Model Files	0.3 GB
Model Architecture	AutoModelForCausalLM
License	llama3.1
Model Max Length	131072
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	down_proj\|o_proj\|k_proj\|q_proj\|v_proj\|gate_proj\|up_proj
LoRA Alpha	64
LoRA Dropout	0.05
R Param	32

Rank the Cve Model 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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