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Open-weight model · Text generation

Qwen2.5-Coder-7B

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

Qwen2.5-Coder-7B is an open-weight model for text generation from Qwen, released under Apache License 2.0. It has 7.6B parameters and a 32,768-token context. At 16-bit it needs about 18.3 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index. It draws 715.9k downloads a month.

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 Downloads715.9k

Runs On

What it takes to serve Qwen2.5-Coder-7B (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 Oct 16, 2026.

Qwen2.5-Coder-7B on every accelerator the SAVRN Index prices, at every precision

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

18.3 GB at 16-bit, 4.6 GB at 4-bit, one MI300X at $1.85 an hour: same memory envelope as the general-purpose Qwen2.5-7B next door, because it is derived from it — 7.6B parameters, 32,768 tokens of context, four safetensors shards totaling 15.2 GB. The single-card fit at 4-bit is what keeps this size class popular for code completion and CI-adjacent tooling.

Apache 2.0 permits commercial use with notices kept. The catch is in the name: this is the base Coder checkpoint, not an instruct model, so it completes code rather than answering questions about it. The Qwen2.5-Coder family runs from 0.5B to 32B, and this midpoint is the smallest size we would put on a real completion workload. No evaluations are reported on this page — rely on your own repo-level benchmarks.

SAVRN Research

Model Card

By Qwen, published under apache-2.0, revision 0396a76181e1.

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 (684 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
Publisher
Qwen
Task
Text generation
Modality
Text
Library
transformers
Parameters
7.6B parameters
Languages
en
Revision
0396a76181e127dfc13e5c5ec48a8cee09938b02
First published
2024-09-16
Last updated
2024-11-18

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 · 28.5 KB
Tokenizer4 files · 11.5 MB
Documentation2 files · 16.5 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
model-00001-of-00004.safetensorsWeights4.9 GB 2296315c41cc
model-00002-of-00004.safetensorsWeights4.9 GB 9db3a8f68a41
model-00003-of-00004.safetensorsWeights4.3 GB 292de283f4dc
model-00004-of-00004.safetensorsWeights1.1 GB 0167e1c1b01c
config.jsonConfiguration668 B —
generation_config.jsonConfiguration122 B —
model.safetensors.index.jsonConfiguration27.8 KB —
LICENSEDocumentation11.3 KB —
README.mdDocumentation5.2 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.

Built on This Model

Compare Qwen2.5-Coder-7B

Questions About Qwen2.5-Coder-7B

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

Yes. Qwen2.5-Coder-7B 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's context length?

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

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