EnlistedGhost/Devstral-Small-2507-Vision-GGUF

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- Model Details and Specifications: -
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Devstral Small 2507 Vision GGUF (Ollama & Llama.cpp)

This release contains:
Llama.cpp and Ollama compatible GGUF converted and Quantized model files (Compatible with both Ollama, and Llama.cpp)
(More information and an updates to the ModelCard (this page) coming soon!)

Quantized GGUF version of:

• EnlistedGhost/Devstral-Small-2507-Vision
  (by EnlistedGhost)

Original Model Link:

• EnlistedGhost/Devstral-Small-2507-Vision

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- Conversion and GGUF Quantization: -
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Software used to convert Safetensors to GGUF:

• llama.cpp

Software used to create Quantized GGUF Files:

• llama.cpp

Specific GitHub Commit Point:

• b7099

Converted to GGUF and Quantized by:

• EnlistedGhost

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---- Updates & News ----
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Model Updates (as of: December 14th, 2025)

• Uploaded: Some GGUF Converted and Quantized model files
• Created: ModelCard
  (this page)

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---- How to run this Model ----
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Compatible Software (Required to use this Model)
You can run this model by using either Ollama (or) Llama.cpp
(Below are instruction on running these GGUF files with Ollama)

How to run this Model using Ollama
You can run this model by using the "ollama run" command.
Simply copy & paste one of the commands from the list below into
your console, terminal or power-shell window.

Quant Type	File Size	Command
QX_X	0.00 GB	(Currently Uploading Files, Check again very soon!)

Vision Projector (Files)
mmproj (Vision Projector) Files

Quant Type	File Size	Download Link
Q8_0	465 MB
F16	870 MB
F32	1.74 GB

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---- Original Info ----
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(Crossposted from the link in the above section: "Model Details"):

Devstral Small 1.1

Devstral is an agentic LLM for software engineering tasks built under a collaboration between Mistral AI and All Hands AI 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positions it as the #1 open source model on this benchmark.

It is finetuned from Mistral-Small-3.1, therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from Mistral-Small-3.1 the vision encoder was removed.

For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.

Learn more about Devstral in our blog post.

Updates compared to Devstral Small 1.0:

• Improved performance, please refer to the benchmark results.
• Devstral Small 1.1 is still great when paired with OpenHands. This new version also generalizes better to other prompts and coding environments.
• Supports Mistral's function calling format.

Key Features:

• Agentic coding: Devstral is designed to excel at agentic coding tasks, making it a great choice for software engineering agents.
• lightweight: with its compact size of just 24 billion parameters, Devstral is light enough to run on a single RTX 4090 or a Mac with 32GB RAM, making it an appropriate model for local deployment and on-device use.
• Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
• Context Window: A 128k context window.
• Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.

Benchmark Results

SWE-Bench

Devstral Small 1.1 achieves a score of 53.6% on SWE-Bench Verified, outperforming Devstral Small 1.0 by +6,8% and the second best state of the art model by +11.4%.

Model	Agentic Scaffold	SWE-Bench Verified (%)
Devstral Small 1.1	OpenHands Scaffold	53.6
Devstral Small 1.0	OpenHands Scaffold	46.8
GPT-4.1-mini	OpenAI Scaffold	23.6
Claude 3.5 Haiku	Anthropic Scaffold	40.6
SWE-smith-LM 32B	SWE-agent Scaffold	40.2
Skywork SWE	OpenHands Scaffold	38.0
DeepSWE	R2E-Gym Scaffold	42.2

When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B.

Usage

We recommend to use Devstral with the OpenHands scaffold. You can use it either through our API or by running locally.