What are the hardware requirements for Qwen2.5 7B Bnb 4bit?

Qwen2.5 7B Bnb 4bit requires approximately 5.5 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 Qwen2.5 7B Bnb 4bit and how large is it?

Qwen2.5 7B Bnb 4bit is developed by unsloth, 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 Qwen2.5 7B Bnb 4bit?

Qwen2.5 7B Bnb 4bit is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 7B Bnb 4bit by unsloth — VRAM 5.5GB, 128K context

Name: Qwen2.5 7B Bnb 4bit
Author: unsloth

Qwen2.5 7B Bnb 4bit is an open-source language model by unsloth. Features: 7b LLM, VRAM: 5.5GB, Context: 128K, License: apache-2.0, Quantized, LLM Explorer Score: 0.18.

Arxiv:2407.10671 4-bit 4bit Ara Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-7b Bitsandbytes Deu Endpoints compatible Eng Fra Ita Jpn Kor Por Quantized Qwen2 Region:us Rus Safetensors Spa Tha Unsloth Vie Zho

Model Card on HF 🤗: https://huggingface.co/unsloth/Qwen2.5-7B-bnb-4bit

Qwen2.5 7B Bnb 4bit Parameters and Internals

Model Type

Causal Language Model

Use Cases

Areas:

Research, Coding, Mathematics, Multilingual Applications

Applications:

Chatbots, Structured Data Understanding, Role-playing Implementations

Primary Use Cases:

instruction following, generating long texts, understanding structured data, generating structured outputs

Limitations:

Not recommended for conversations without further training

Considerations:

Use post-training methods for conversations like SFT, RLHF

Additional Notes

Supports multilingual capabilities with improved role-play conditions for chatbots.

Supported Languages

English (high), Chinese (high), French (medium), Spanish (medium), Portuguese (medium), German (medium), Italian (medium), Russian (medium), Japanese (medium), Korean (medium), Vietnamese (medium), Thai (medium), Arabic (medium), other_languages (basic)

Training Details

Data Sources:

specialized expert models in coding and mathematics

Context Length:

131072

Model Architecture:

transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias

Input Output

Input Format:

text

Accepted Modalities:

text

Output Format:

text

Performance Tips:

Use the latest version of 'transformers' to avoid errors.

LLM Name	Qwen2.5 7B Bnb 4bit
Repository 🤗	https://huggingface.co/unsloth/Qwen2.5-7B-bnb-4bit
Base Model(s)	Qwen/Qwen2.5-7B Qwen/Qwen2.5-7B
Model Size	7b
Required VRAM	5.5 GB
Updated	2026-07-19
Maintainer	unsloth
Model Type	qwen2
Model Files	5.5 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.49.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 7B Bnb 4bit 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 Qwen2.5 7B Bnb 4bit

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Instruct 1M 4bit	986K / 4.3 GB	444	11
...wen2.5 7B Instruct 1M Bnb 4bit	986K / 5.5 GB	267	1
Qwen2.5 7B Instruct 1M 8bit	986K / 8.1 GB	63	4
Qwen2.5 7B Instruct 1M 6bit	986K / 6.2 GB	52	2
...B Instruct 1M Unsloth Bnb 4bit	986K / 7.5 GB	37	3
Legal Qwen2.5 7B Sft	128K / 15.2 GB	152	0
A1 V002	128K / 15.2 GB	7	0
Mini Pathfinder	128K / 15.2 GB	16	0
A1 V0.0.1	128K / 15.2 GB	8	0
CogitoDistil	128K / 15.2 GB	14	0

Note: green Score (e.g. "73.2") means that the model is better than unsloth/Qwen2.5-7B-bnb-4bit.