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Open-weight model · Text generation

Qwen2.5-Coder-7B-Instruct

by Qwen Qwen/Qwen2.5-Coder-7B-Instruct

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers.

Parameters7.6B
Context32,768
Weights15.2 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads2.7M

Runs On

What it takes to serve Qwen2.5-Coder-7B-Instruct (7.6B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 15.2 GB 18.3 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 7.6 GB 9.1 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 3.8 GB 4.6 GB 1x MI300X (192 GB)
Vultr		 $1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.

SAVRN's Notes on Qwen2.5-Coder-7B-Instruct

Code is the job here, generation, reasoning and fixing, on the 7B rung of a series spanning 0.5 to 32 billion parameters, trained on 5.5 trillion tokens over Qwen2.5. The 7.6B parameters need 18.3 GB at 16-bit, 9.1 GB at 8-bit and 4.6 GB at 4-bit, any of which fits a 192 GB MI300X at $1.85 per hour. We would never run it alone on that card; pack copies or sessions onto it.

The license is Apache 2.0, commercial use, modification and redistribution included, notices kept, changes stated. It is tuned from Qwen/Qwen2.5-Coder-7B, the base to start from with your own code. Confirm the window your stack honors: the configuration says 32,768 tokens, but arXiv:2309.00071 on YaRN context extension sits among its papers. Then Nscale on the Index, $0.01 in and $0.03 out per million tokens, the rate your card hour must beat before owning wins.

SAVRN Research, 2026-09-18

Model Card

By Qwen, published under apache-2.0, revision c03e6d358207.

Introduction

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:

  • Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
  • A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
  • Long-context Support up to 128K tokens.

Read the full model card (760 words)

Configuration

Architecture
Qwen2ForCausalLM
Context length (tokens)
32,768
Layers
28
Hidden size
3,584
Feed-forward size
18,944
Attention heads
28
Key/value heads
4
Vocabulary size
152,064
Sliding window (tokens)
131,072
RoPE base
1e+06
Stored precision
bfloat16
Model type
qwen2

Identity and Version

Repository
Qwen/Qwen2.5-Coder-7B-Instruct
Publisher
Qwen
Task
Text generation
Modality
Text
Library
transformers
Parameters
7.6B parameters
Languages
en
Revision
c03e6d358207e414f1eca0bb1891e29f1db0e242
First published
2024-09-17
Last updated
2025-01-12

Files and Weights

14 files, 15.2 GB in total. The weights are 4 files totalling 15.2 GB in safetensors.

Weights4 files · 15.2 GB
Configuration3 files · 28.7 KB
Tokenizer4 files · 11.5 MB
Documentation2 files · 17.7 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00004.safetensorsWeights4.9 GB 0b6f069918b0
model-00002-of-00004.safetensorsWeights4.9 GB c3d46733e7aa
model-00003-of-00004.safetensorsWeights4.3 GB 9fe45dacee08
model-00004-of-00004.safetensorsWeights1.1 GB 5aa6e5cbe642
config.jsonConfiguration663 B —
generation_config.jsonConfiguration242 B —
model.safetensors.index.jsonConfiguration27.8 KB —
LICENSEDocumentation11.3 KB —
README.mdDocumentation6.4 KB —
.gitattributesRepository1.5 KB —
merges.txtTokenizer1.7 MB —
tokenizer.jsonTokenizer7.0 MB —
tokenizer_config.jsonTokenizer7.3 KB —
vocab.jsonTokenizer2.8 MB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
15.2 GB
Download from Qwen

Released by Qwen through ModelScope. Read the license.

Built From

Memory Requirements

PrecisionWeights in memory
As published15.2 GB
16-bit15.2 GB
8-bit7.6 GB
4-bit3.8 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Hosted Prices

HostInput / outputUnitObserved
Nscale$0.01 / $0.03input / output, per million tokensSep 18, 2026

From the SAVRN Index.

Built on This Model

Compare Qwen2.5-Coder-7B-Instruct

Questions About Qwen2.5-Coder-7B-Instruct

How much GPU memory does Qwen2.5-Coder-7B-Instruct need?

About 18.3 GB at 16-bit and 4.6 GB at 4-bit: the weights (7.6B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Qwen2.5-Coder-7B-Instruct on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use Qwen2.5-Coder-7B-Instruct commercially?

Yes. Qwen2.5-Coder-7B-Instruct is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

What is Qwen2.5-Coder-7B-Instruct's context length?

32,768 tokens, from the maximum position embeddings in its published configuration.

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