What are the hardware requirements for Sat Socratic Qwen2.5 7B Lora?

Sat Socratic Qwen2.5 7B Lora requires approximately 0.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Sat Socratic Qwen2.5 7B Lora and how large is it?

Sat Socratic Qwen2.5 7B Lora is developed by LinhNgocBui, 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 Sat Socratic Qwen2.5 7B Lora?

Sat Socratic Qwen2.5 7B Lora is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Sat Socratic Qwen2.5 7B Lora by LinhNgocBui — VRAM 0.6GB

Name: Sat Socratic Qwen2.5 7B Lora
Author: LinhNgocBui

Sat Socratic Qwen2.5 7B Lora is an open-source language model by LinhNgocBui. Features: 7b LLM, VRAM: 0.6GB, Instruction-Based, LLM Explorer Score: 0.23.

Base model:adapter:qwen/qwen2.... Base model:qwen/qwen2.5-7b-ins... Conversational Instruct Lora Peft Region:us Safetensors Sft Trl

Model Card on HF 🤗: https://huggingface.co/LinhNgocBui/sat-socratic-qwen2.5-7b-lora

Sat Socratic Qwen2.5 7B Lora Parameters and Internals

LLM Name	Sat Socratic Qwen2.5 7B Lora
Repository 🤗	https://huggingface.co/LinhNgocBui/sat-socratic-qwen2.5-7b-lora
Model Name	checkpoints
Base Model(s)	Qwen/Qwen2.5-7B-Instruct Qwen/Qwen2.5-7B-Instruct
Model Size	7b
Required VRAM	0.6 GB
Updated	2026-07-24
Maintainer	LinhNgocBui
Instruction-Based	Yes
Model Files	0.6 GB 0.0 GB
Model Architecture	AutoModel
Model Max Length	131072
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	k_proj\|q_proj\|o_proj\|v_proj\|down_proj\|gate_proj\|up_proj
LoRA Alpha	128
LoRA Dropout	0.05
R Param	64
Errors	replace

Rank the Sat Socratic Qwen2.5 7B 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

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Note: green Score (e.g. "73.2") means that the model is better than LinhNgocBui/sat-socratic-qwen2.5-7b-lora.