What are the hardware requirements for Qwen2.5 0.78B Instruct?

Qwen2.5 0.78B Instruct requires approximately 1.6 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 Qwen2.5 0.78B Instruct and how large is it?

Qwen2.5 0.78B Instruct is developed by sethuiyer, a model with 0.5b 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 Qwen2.5 0.78B Instruct?

Qwen2.5 0.78B Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 0.78B Instruct by sethuiyer — VRAM 1.6GB, 32K context

Name: Qwen2.5 0.78B Instruct
Author: sethuiyer

Qwen2.5 0.78B Instruct is an open-source language model by sethuiyer. Features: 0.5b LLM, VRAM: 1.6GB, Context: 32K, Instruction-Based, Merged, LLM Explorer Score: 0.15.

Merged Model Autotrain compatible Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-0.5b-i... Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/sethuiyer/Qwen2.5-0.78B-Instruct

Qwen2.5 0.78B Instruct Parameters and Internals

LLM Name	Qwen2.5 0.78B Instruct
Repository 🤗	https://huggingface.co/sethuiyer/Qwen2.5-0.78B-Instruct
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Merged Model	Yes
Model Size	0.5b
Required VRAM	1.6 GB
Updated	2024-11-27
Maintainer	sethuiyer
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.0 GB: 1-of-2 0.6 GB: 2-of-2
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.46.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 0.78B Instruct 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 0.78B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2 0.5B Abyme Merge3	128K / 1.3 GB	18	1
Qwen2 0.5B Abyme Merge2	128K / 0.3 GB	15	0
Qwen2.5 0.5B Instruct	64K / 1 GB	16	0
Qwen2.5 0.5B Instruct	32K / 1 GB	6324790	572
Pulsar AI Final	32K / 1 GB	185	2
Chichu 2.0 500M Instruct	32K / 1 GB	212	2
Sakthai Context 0.5B Tools	32K / 1 GB	474	0
Qwen R1 0.5B	32K / 1 GB	202	2
Veda Labs 0.5B Instruct Base	32K / 1 GB	30	2
...ext Aware Abstention Qwen 0.5B	32K / 2 GB	48	0

Note: green Score (e.g. "73.2") means that the model is better than sethuiyer/Qwen2.5-0.78B-Instruct.