What are the hardware requirements for Q2.5 EVACOT 7B?

Q2.5 EVACOT 7B requires approximately 15.2 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Q2.5 EVACOT 7B and how large is it?

Q2.5 EVACOT 7B is developed by Triangle104, 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 Q2.5 EVACOT 7B?

Q2.5 EVACOT 7B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Q2.5 EVACOT 7B by Triangle104 — VRAM 15.2GB, 128K context

Name: Q2.5 EVACOT 7B
Author: Triangle104

Q2.5 EVACOT 7B is an open-source language model by Triangle104. Features: 7b LLM, VRAM: 15.2GB, Context: 128K, License: apache-2.0, Instruction-Based, Merged, LLM Explorer Score: 0.25.

Merged Model Autotrain compatible Base model:eva-unit-01/eva-qwe... Base model:prithivmlmods/qwq-l... Conversational Endpoints compatible Instruct Model-index Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Triangle104/Q2.5-EVACOT-7b

Q2.5 EVACOT 7B Parameters and Internals

LLM Name	Q2.5 EVACOT 7B
Repository 🤗	https://huggingface.co/Triangle104/Q2.5-EVACOT-7b
Base Model(s)	EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1 QwQ LCoT2 7B Instruct EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1 prithivMLmods/QwQ-LCoT2-7B-Instruct
Merged Model	Yes
Model Size	7b
Required VRAM	15.2 GB
Updated	2025-09-23
Maintainer	Triangle104
Model Type	qwen2
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 4.9 GB: 2-of-4 5.0 GB: 3-of-4 0.3 GB: 4-of-4
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.48.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Q2.5 EVACOT 7B 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 Q2.5 EVACOT 7B

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Instruct 1M	986K / 15.4 GB	52534	373
Hush Qwen2.5 7B RP V1.4 1M	986K / 15.2 GB	10	2
Qwen2.5 7B RRP 1M	986K / 15.2 GB	5	6
Qwen2.5 7B CelestialHarmony 1M	986K / 14.8 GB	8	7
Q2.5 Instruct 1M Harmony	986K / 15.2 GB	4	2
Impish QWEN 7B 1M	986K / 15.2 GB	4	6
COCO 7B Instruct 1M	986K / 15.2 GB	9	4
Apollo 7B	986K / 15.2 GB	8	0
Graph R1 7B	986K / 15.3 GB	7	3
Qwen2.5 7B Sky R1 Mini	986K / 15.2 GB	6	0

Note: green Score (e.g. "73.2") means that the model is better than Triangle104/Q2.5-EVACOT-7b.