What are the hardware requirements for Qwen2.5 Coder 7B Instruct?

Qwen2.5 Coder 7B Instruct requires approximately 15.2 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 7B Instruct and how large is it?

Qwen2.5 Coder 7B Instruct is developed by Qwen, 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 Coder 7B Instruct?

Qwen2.5 Coder 7B 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 7B Instruct by Qwen — VRAM 15.2GB, 32K context

Name: Qwen2.5 Coder 7B Instruct
Rating: 3 (2 reviews)
Author: Qwen

Qwen2.5 Coder 7B Instruct is an open-source language model by Qwen. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, License: apache-2.0, Instruction-Based, Code Generating, LLM Explorer Score: 0.37.

Arxiv:2309.00071 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 Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct

Qwen2.5 Coder 7B Instruct Benchmarks

IFEval: 61.47 vs 88 (so35)^-30.1%

MATH Lvl 5: 3.1

LLME Score: 0.36538

^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 7B Instruct (Qwen/Qwen2.5-Coder-7B-Instruct)

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Qwen2.5 Coder 7B Instruct Parameters and Internals

Model Type

text generation

Use Cases

Areas:

research, commercial applications

Applications:

code generation, code reasoning, code fixing

Additional Notes

vLLM is recommended for deployment when processing long contexts.

Supported Languages

en (full)

Training Details

Data Sources:

source code, text-code grounding, Synthetic data

Data Volume:

5.5 trillion tokens

Methodology:

Pretraining & Post-training

Context Length:

131072

Model Architecture:

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

Input Output

Input Format:

text prompts

Accepted Modalities:

text

Output Format:

generated text

Performance Tips:

Utilize YaRN for long contexts

LLM Name	Qwen2.5 Coder 7B Instruct
Repository 🤗	https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct
Base Model(s)	Qwen/Qwen2.5-Coder-7B Qwen/Qwen2.5-Coder-7B
Model Size	7b
Required VRAM	15.2 GB
Updated	2026-04-14
Maintainer	Qwen
Model Type	qwen2
Instruction-Based	Yes
Model Files	4.9 GB: 1-of-4 4.9 GB: 2-of-4 4.3 GB: 3-of-4 1.1 GB: 4-of-4
Supported Languages	en
Generates Code	Yes
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.44.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Quantized Models of the Qwen2.5 Coder 7B Instruct

Model	Likes	Downloads	VRAM
....5 Coder 7B Instruct GPTQ Int4	13	993569	5 GB
Qwen2.5 Coder 7B Instruct AWQ	21	256217	5 GB
...2.5 Coder 7B Instruct Bnb 4bit	12	67913	5 GB
Qwen 2.5 7B Threatflux	6	62	15 GB
...en2.5.1 Coder 7B Instruct 4bit	3	653	4 GB
...en2.5.1 Coder 7B Instruct 8bit	3	156	8 GB
...der 7B Instruct Abliterated V1	2	22	15 GB
....5 Coder 7B Instruct GPTQ Int8	4	486	8 GB

Best Alternatives to Qwen2.5 Coder 7B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Rebase	986K / 15.2 GB	73	2
Qwen2.5 7B Rebase	986K / 15.2 GB	16	2
StockQwen 2.5 7B	128K / 15.2 GB	25	6
DareQwen 2.5 7B	128K / 15.2 GB	8	1
Tessa T1 7B	117K / 15.2 GB	12	10
UIGEN T1.5 7B	117K / 15.2 GB	7	6
AstraGPTCoder 7B	32K / 15.2 GB	321	0
Qwen2.5 Coder 7B Instruct MLX	32K / 15.2 GB	799	2
Hemlock2 Coder 7B	32K / 15.2 GB	192	0
Qwen2.5 7B MS Destroyer	32K / 15.2 GB	7	1

Rank the Qwen2.5 Coder 7B 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 7B Instruct by Qwen