ProseFlow-v1-1.5B-Instruct

ProseFlow-v1-1.5B-Instruct

ProseFlow-v1-1.5B-Instruct is a versatile instruction-tuned model designed to be the local AI engine for the ProseFlow desktop application. This model excels at a wide variety of text-processing and code-related tasks, making it an ideal choice for users who want a high-performance, private, and offline-capable AI assistant integrated into their daily workflow.

This model was fine-tuned from Qwen/Qwen2.5-Coder-1.5B-Instruct, inheriting its strong coding and logical reasoning capabilities, and has been further specialized to follow the specific, structured prompts used by ProseFlow "Actions".

The model was fine-tuned on the ProseFlow-Actions-v1 dataset.

Model Description

ProseFlow is a universal AI text processor that works via global hotkeys. Users create "Actions" – reusable instructions for the AI – to perform tasks like proofreading, summarizing, refactoring code, or changing the tone of a text. This model is the brain that executes those instructions.

ProseFlow-v1-1.5B-Instruct is the recommended and primary local model for the application. It strikes an excellent balance between performance, resource requirements, and capability.

Key Strengths

Based on comprehensive evaluations against the ProseFlow-Actions-v1 dataset, this model demonstrates:

• Excellent Task Comprehension: The model consistently understands the intent behind a wide variety of instructions, from simple text manipulation to complex business and logical tasks.
• Superior Code Intelligence: Thanks to its Qwen2.5-Coder base, the model is exceptionally proficient at code-related tasks like explaining code snippets, finding bugs, refactoring for efficiency, and adding comments.
• High-Quality Text Generation: It produces coherent, high-quality output for summarization, expansion, and creative writing prompts.
• Strong Reasoning Capabilities: The model can successfully solve multi-step word problems and perform logical deductions required by more advanced actions.
• Versatility: It performs reliably across dozens of distinct tasks, including sentiment analysis, data extraction (JSON conversion), and professional email drafting.

Intended Use

This model is primarily intended to be used within the ProseFlow desktop application. Its prompt format and output style are specifically tuned to work seamlessly with the app's "Action" system.

When used in ProseFlow, it provides a powerful, private, and offline alternative to cloud-based AI services.

Primary Use Cases:

• Code refactoring, debugging, and documentation.
• Drafting and improving professional emails and documents.
• Summarizing long articles or meeting notes.
• Proofreading and enhancing creative or technical writing.
• Brainstorming ideas and generating structured content.

Limitations and Considerations

While highly capable, this model has a few known behaviors:

• Instruction Following vs. Helpfulness: The model is so well-aligned to be a helpful assistant that it sometimes adds conversational headers or brief explanations to its output (e.g., "Here is the refactored code:"). This violates the strict "output only" constraint in some of the training prompts. In the context of the ProseFlow application, this is generally a minor issue, but it's a deviation from the prompt instructions.
• Complex Logic: While it can handle multi-step logic, it may fail on more abstract or tricky logical puzzles (e.g., identifying an anomaly in a nuanced list).

How to Use in ProseFlow

1. Download and install the ProseFlow application.
2. Navigate to the Providers -> Local Provider tab.
3. Click "Manage Models..." and download ProseFlow-v1-1.5B-Instruct from the "Available for Download" list.
4. Once downloaded, select it from the "My Models" list.
5. Set your "Primary Service Type" in ProseFlow to Local.
6. You're all set! The application will now use this model for all AI actions.

Training Details

• Base Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
• Dataset: LSXPrime/ProseFlow-Actions-v1
• Fine-tuning Library: Unsloth
• Fine-tuning Method: Supervised fine-tuning on a dataset of structured instruction-input-output triplets.

License

This model is licensed under the Apache License, Version 2.0.