What are the hardware requirements for Qwen2.5 Coder 0.5B Instruct?

Qwen2.5 Coder 0.5B Instruct requires approximately 1 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 Coder 0.5B Instruct and how large is it?

Qwen2.5 Coder 0.5B Instruct is developed by Qwen, 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 Coder 0.5B Instruct?

Qwen2.5 Coder 0.5B Instruct is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 Coder 0.5B Instruct by Qwen — VRAM 1GB, 32K context

Name: Qwen2.5 Coder 0.5B Instruct
Author: Qwen

Qwen2.5 Coder 0.5B Instruct is an open-source language model by Qwen. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Instruction-Based, Code Generating, LLM Explorer Score: 0.23.

Arxiv:2407.10671 Arxiv:2409.12186 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-coder-... Chat Code Codegen Codeqwen Conversational Deploy:azure En Endpoints compatible Instruct Qwen Qwen-coder Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct

Qwen2.5 Coder 0.5B Instruct Benchmarks

LLME Score: 0.23355

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Qwen2.5 Coder 0.5B Instruct (Qwen/Qwen2.5-Coder-0.5B-Instruct)

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Qwen2.5 Coder 0.5B Instruct Parameters and Internals

Model Type

Causal Language Models

Use Cases

Areas:

research, commercial applications

Applications:

code generation, code reasoning, code fixing, Code Agents, mathematics

Supported Languages

en (fluent)

Training Details

Data Sources:

source code, text-code grounding, Synthetic data

Data Volume:

5.5 trillion tokens

Methodology:

Pretraining & Post-training

Context Length:

32768

Model Architecture:

transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings

Input Output

Input Format:

structured prompt

Accepted Modalities:

text

Output Format:

generated text response

Release Notes

Notes:

Significant improvements in code generation, reasoning, and fixing.

LLM Name	Qwen2.5 Coder 0.5B Instruct
Repository 🤗	https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct
Base Model(s)	Qwen/Qwen2.5-Coder-0.5B Qwen/Qwen2.5-Coder-0.5B
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-05-20
Maintainer	Qwen
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.0 GB
Supported Languages	en
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.43.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Quantized Models of the Qwen2.5 Coder 0.5B Instruct

Model	Likes	Downloads
...5 Coder 0.5B Instruct Bnb 4bit	4	3768
...en2.5 Coder 0.5B Instruct 4bit	6	825
...wen2.5 Coder 0.5B Instruct AWQ	3	134

Best Alternatives to Qwen2.5 Coder 0.5B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Qwen 2.5 Coder 0.5B SQL Writer	32K / 1 GB	14	0
Qwen CoMa 0.5B	32K / 1.3 GB	0	1
Qwen2.5 Coder 0.5B Instruct	32K / 1 GB	2039	5
Qwen2.5 Coder 0.5B Instruct	32K / 1 GB	6	0
Qwen2.5 Coder 0.5B Instruct	32K / 1 GB	5	0
...nstruct MIFT En Manywords 8000	32K / 2 GB	6	0
...truct PIFT Enja Manywords 8000	32K / 2 GB	6	0
...truct PIFT Enja Manywords 6000	32K / 2 GB	6	0
...B Instruct BIFT Manywords 2000	32K / 2 GB	5	0
...truct PIFT Jaen Manywords 2000	32K / 2 GB	7	0

Rank the Qwen2.5 Coder 0.5B Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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

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Qwen2.5 Coder 0.5B Instruct by Qwen