What are the hardware requirements for Qwen3.8 27B MSVC9 Matcher LoRA?

Qwen3.8 27B MSVC9 Matcher LoRA requires approximately 0.9 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen3.8 27B MSVC9 Matcher LoRA and how large is it?

Qwen3.8 27B MSVC9 Matcher LoRA is developed by momusdigital, a model with 27b 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 Qwen3.8 27B MSVC9 Matcher LoRA?

Qwen3.8 27B MSVC9 Matcher LoRA is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen3.8 27B MSVC9 Matcher LoRA by momusdigital — VRAM 0.9GB

Name: Qwen3.8 27B MSVC9 Matcher LoRA
Author: momusdigital

Qwen3.8 27B MSVC9 Matcher LoRA is an open-source language model by momusdigital. Features: 27b LLM, VRAM: 0.9GB, License: apache-2.0, LLM Explorer Score: 0.28.

Adapter Base model:adapter:qwen/qwen3.... Base model:qwen/qwen3.8-27b Code-generation Decompilation En Finetuned Lora Msvc Nvfp4 Peft Qwen3.8 Region:us Reverse-engineering Safetensors X86

Model Card on HF 🤗: https://huggingface.co/momusdigital/Qwen3.8-27B-MSVC9-Matcher-LoRA

Qwen3.8 27B MSVC9 Matcher LoRA Parameters and Internals

LLM Name	Qwen3.8 27B MSVC9 Matcher LoRA
Repository 🤗	https://huggingface.co/momusdigital/Qwen3.8-27B-MSVC9-Matcher-LoRA
Base Model(s)	Qwen/Qwen3.8-27B Qwen/Qwen3.8-27B
Model Size	27b
Required VRAM	0.9 GB
Updated	2026-10-26
Maintainer	momusdigital
Model Files	0.9 GB
Supported Languages	en
Model Architecture	Adapter
License	apache-2.0
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|k_proj\|v_proj\|o_proj\|in_proj_qkv\|in_proj_z\|out_proj\|gate_proj\|up_proj\|down_proj
LoRA Alpha	64
LoRA Dropout	0
R Param	32

Rank the Qwen3.8 27B MSVC9 Matcher LoRA 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

Best Alternatives to Qwen3.8 27B MSVC9 Matcher LoRA

Best Alternatives	Context / RAM	Downloads	Likes
B.R.O Abliterated 27B	0K / GB	543	1
Herbert V0.5	0K / 0.9 GB	56	1
Herbert V0.8	0K / 0.9 GB	38	1
Jev Vision 27B	0K / 0.9 GB	59	2
Qwen3.8 27B Velocity	0K / 0.9 GB	83	1
DeltaRoutingAI	0K / 0.2 GB	26	1
...3.8 27B Abliterated Cyber LoRA	0K / 0.4 GB	22	1
Qwen38 Magnus V2 Lora	0K / 0.5 GB	22	0
...en3.8 27B Absolute Heresy LoRA	0K / 0.3 GB	27	4
...laude Opus Reasoning Distilled	0K / 0.9 GB	22	2

Note: green Score (e.g. "73.2") means that the model is better than momusdigital/Qwen3.8-27B-MSVC9-Matcher-LoRA.