What are the hardware requirements for Qwen2 72B Instruct Mix Calibration 4B EXL2?

Qwen2 72B Instruct Mix Calibration 4B EXL2 requires approximately 38.8 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.

Who developed Qwen2 72B Instruct Mix Calibration 4B EXL2 and how large is it?

Qwen2 72B Instruct Mix Calibration 4B EXL2 is developed by Orion-zhen, a model with 72b 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 Qwen2 72B Instruct Mix Calibration 4B EXL2?

Qwen2 72B Instruct Mix Calibration 4B EXL2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2 72B Instruct Mix Calibration 4B EXL2 by Orion-zhen — VRAM 38.8GB, 32K context

Name: Qwen2 72B Instruct Mix Calibration 4B EXL2
Author: Orion-zhen

Qwen2 72B Instruct Mix Calibration 4B EXL2 is an open-source language model by Orion-zhen. Features: 72b LLM, VRAM: 38.8GB, Context: 32K, License: other, Quantized, Instruction-Based, LLM Explorer Score: 0.13.

Arxiv:2309.00071 4-bit Chat Conversational En Endpoints compatible Exl2 Instruct Quantized Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Orion-zhen/Qwen2-72B-Instruct-mix-calibration-4b-exl2

Qwen2 72B Instruct Mix Calibration 4B EXL2 Parameters and Internals

Model Type

text-generation

Use Cases

Areas:

research, commercial applications

Supported Languages

en (High)

Training Details

Methodology:

Pretrained with a large amount of data, and post-trained with both supervised finetuning and direct preference optimization.

Context Length:

131072

Model Architecture:

Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc.

Input Output

Accepted Modalities:

text

Performance Tips:

To handle extensive inputs exceeding 32,768 tokens, utilize YARN for enhancing model length extrapolation ensuring optimal performance on lengthy texts.

LLM Name	Qwen2 72B Instruct Mix Calibration 4B EXL2
Repository 🤗	https://huggingface.co/Orion-zhen/Qwen2-72B-Instruct-mix-calibration-4b-exl2
Model Size	72b
Required VRAM	38.8 GB
Updated	2026-07-14
Maintainer	Orion-zhen
Model Type	qwen2
Instruction-Based	Yes
Model Files	8.5 GB: 1-of-5 8.6 GB: 2-of-5 8.5 GB: 3-of-5 8.5 GB: 4-of-5 4.7 GB: 5-of-5
Supported Languages	en
Quantization Type	exl2
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.40.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2 72B Instruct Mix Calibration 4B EXL2 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 Qwen2 72B Instruct Mix Calibration 4B EXL2

Best Alternatives	Context / RAM	Downloads	Likes
...B Instruct 2.0bpw H Novel EXL2	125K / 23 GB	4	1
Qwen2 72B Instruct 4bit	32K / 40.9 GB	56	2
ECE ILAB Q1	32K / 77.8 GB	15	0
Qwen2.5 72B Instruct Bnb 4bit	32K / 41.4 GB	14954	8
Qwen2.5 72B Instruct 4bit	32K / 40.9 GB	1200	7
Athene V2 Chat 4.65bpw H6 EXL2	32K / 44.3 GB	6	6
Qwen2.5 72B Instruct 8bit	32K / 77.1 GB	614	3
Qwen2 72B Instruct Bpw5.5 EXL2	32K / 51.8 GB	1	1
Qwen2 72B Instruct 8bit	32K / 77.1 GB	48	1
Qwen2 72B Instruct Bpw4 EXL2	32K / 38.7 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than Orion-zhen/Qwen2-72B-Instruct-mix-calibration-4b-exl2.