--- license: apache-2.0 language: - en - ru - uk - de - es - pl - sv - ar - tr - be - bn base_model: - EnlistedGhost/Devstral-Small-2507-Vision new_version: EnlistedGhost/Devstral-Small-2507-Vision-GGUF pipeline_tag: image-text-to-text tags: - Code - Dev - Devops - Devstral - Mistral - MistralAI - Image-Text-to-Text - GGUF - Quantized - Ollama - Llama.cpp - Coding - Vision datasets: - mistralai/MM-MT-Bench library_name: transformers --- Devstral image ## -----------------------------------------------
- Model Details and Specifications: -
----------------------------------------------- # 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](https://huggingface.co/EnlistedGhost/Devstral-Small-2507-Vision) ---------------------------------------------- ## ---------------------------------------------------
- Conversion and GGUF Quantization: -
--------------------------------------------------- **Software used to convert Safetensors to GGUF:** - llama.cpp **Software used to create Quantized GGUF Files:** - llama.cpp **Specific GitHub Commit Point:** - b7266 **Converted to GGUF and Quantized by:** - [EnlistedGhost](https://huggingface.co/EnlistedGhost) ---------------------------------------------- ## -------------------------------
---- Updates & News ----
------------------------------- **Model Updates (as of: December 28th, 2025)** - Uploaded: All remaining GGUF Converted and Quantized model files (Q5_K, Q6_K, Q8_0) - Updated: ModelCard
(this page) --------------------------------------------- ### --------------------------------------
---- How to run this Model ----
-------------------------------------- **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 | Run/Pull Command (Coming 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 | | ----------------------------------------------- ## ---------------------------
---- Original Info ----
--------------------------- *(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](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. 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](#benchmark-results). It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), 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](https://mistral.ai/news/devstral-2507). **Updates compared to [`Devstral Small 1.0`](https://huggingface.co/mistralai/Devstral-Small-2505):** - Improved performance, please refer to the [benchmark results](#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](https://mistralai.github.io/mistral-common/usage/tools/). ## 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. ![SWE Benchmark](assets/swe_benchmark.png) ## Usage We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold. You can use it either through our API or by running locally.