What are the hardware requirements for T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch?

T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch requires approximately 15.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Where can I download or evaluate T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch?

T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch by JungZoona — VRAM 15.2GB, 32K context

Name: T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch
Author: JungZoona

T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch is an open-source language model by JungZoona. Features: 72b LLM, VRAM: 15.2GB, Context: 32K, License: other, Instruction-Based, LLM Explorer Score: 0.27.

Autotrain compatible Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-72b-in... Chat Conversational En Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/JungZoona/T3Q-Qwen2.5-7B-Instruct-it-8-4-0.0001-cosine-16-16-2epoch

T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch Parameters and Internals

LLM Name	T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch
Repository 🤗	https://huggingface.co/JungZoona/T3Q-Qwen2.5-7B-Instruct-it-8-4-0.0001-cosine-16-16-2epoch
Base Model(s)	Qwen/Qwen2.5-72B-Instruct Qwen/Qwen2.5-72B-Instruct
Model Size	72b
Required VRAM	15.2 GB
Updated	2025-03-06
Maintainer	JungZoona
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 4.3 GB: 3-of-4 1.1 GB: 4-of-4
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.49.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch 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 T3Q Qwen2.5 7B Instruct It 8 4 0.0001 Cosine 16 16 2epoch

Best Alternatives	Context / RAM	Downloads	Likes
EVA Qwen2.5 72B V0.2	128K / 146 GB	159	22
AceInstruct 72B	128K / 146 GB	88	18
...n2.5 72B 2x Instruct TIES V1.0	128K / 146.1 GB	78	1
...2B PT BR Instruct Experimental	128K / 146 GB	20	0
... BI Qwen2.5 72B PT BR Instruct	128K / 146 GB	5	0
...A Abliterated TIES Qwen2.5 72B	128K / 146.1 GB	8	1
72B Qwen2.5 Kunou V1	128K / 146 GB	77	26
EVA Qwen2.5 72B V0.1	128K / 146 GB	17	19
EVA Qwen2.5 72B V0.0	128K / 146 GB	10	5
Qwen2 72B Instruct	32K / 145.5 GB	61318	718

Note: green Score (e.g. "73.2") means that the model is better than JungZoona/T3Q-Qwen2.5-7B-Instruct-it-8-4-0.0001-cosine-16-16-2epoch.