What are the hardware requirements for Trialspec Qwen2.5 1.5B Lora Run4?

Trialspec Qwen2.5 1.5B Lora Run4 requires approximately 0.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Trialspec Qwen2.5 1.5B Lora Run4 and how large is it?

Trialspec Qwen2.5 1.5B Lora Run4 is developed by umairishuman, a model with 1.5b 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 Trialspec Qwen2.5 1.5B Lora Run4?

Trialspec Qwen2.5 1.5B Lora Run4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Trialspec Qwen2.5 1.5B Lora Run4 by umairishuman — VRAM 0.1GB

Name: Trialspec Qwen2.5 1.5B Lora Run4
Author: umairishuman

Trialspec Qwen2.5 1.5B Lora Run4 is an open-source language model by umairishuman. Features: 1.5b LLM, VRAM: 0.1GB, Instruction-Based, LLM Explorer Score: 0.3.

Arxiv:1910.09700 Base model:adapter:qwen/qwen2.... Base model:qwen/qwen2.5-1.5b-i... Conversational Instruct Lora Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/umairishuman/trialspec-qwen2.5-1.5b-lora-run4

Trialspec Qwen2.5 1.5B Lora Run4 Parameters and Internals

LLM Name	Trialspec Qwen2.5 1.5B Lora Run4
Repository 🤗	https://huggingface.co/umairishuman/trialspec-qwen2.5-1.5b-lora-run4
Base Model(s)	Qwen/Qwen2.5-1.5B-Instruct Qwen/Qwen2.5-1.5B-Instruct
Model Size	1.5b
Required VRAM	0.1 GB
Updated	2026-11-11
Maintainer	umairishuman
Instruction-Based	Yes
Model Files	0.1 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	up_proj\|o_proj\|v_proj\|down_proj\|q_proj\|gate_proj\|k_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16
Errors	replace

Rank the Trialspec Qwen2.5 1.5B Lora Run4 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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