| ---
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| base_model: HuggingFaceTB/SmolLM-360M-Instruct
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| language:
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| - en
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| library_name: transformers
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| license: apache-2.0
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| datasets:
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| - LSXPrime/ProseFlow-Actions-v1
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| tags:
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| - text-generation
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| - instruction
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| - proseflow
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| - unsloth
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| - smollm
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| - writing-assistant
|
| ---
|
|
|
| # ProseFlow-v1-360M-Instruct
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|
|
| **ProseFlow-v1-360M-Instruct** is a lightweight, experimental instruction-tuned model created for the [ProseFlow desktop application](https://github.com/LSXPrime/ProseFlow). This model is a fine-tune of HuggingFace's [**SmolLM-360M-Instruct**](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct) and was created to explore the capabilities of smaller language models on a diverse set of text-processing tasks.
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|
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| The model was fine-tuned on the [**ProseFlow-Actions-v1**](https://huggingface.co/datasets/LSXPrime/ProseFlow-Actions-v1) dataset.
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|
|
| **Note:** This model is provided for research and experimental purposes and low-resource devices. For the best user experience in the ProseFlow application, the larger and more capable [`ProseFlow-v1-1.5B-Instruct`](https://huggingface.co/LSXPrime/ProseFlow-v1-1.5B-Instruct) model is strongly recommended.
|
|
|
| ## Model Description
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|
|
| ProseFlow is a universal AI text processor that allows users to create and execute custom AI "Actions" on text in any application. This model was an experiment to see if a ~360M parameter model could reliably perform the wide range of tasks defined in the training dataset.
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|
|
| ### Performance and Capabilities
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|
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| Evaluations show that while this model is extremely fast and has very low resource requirements, its capabilities are limited.
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|
|
| #### Strengths:
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| * **Extremely Lightweight:** Can run on devices with very limited RAM and computational power.
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| * **Strict Formatting Adherence (sometimes):** In some cases where it understands the task, it can follow rigid formatting instructions (like creating a bulleted list) more strictly than its larger counterpart.
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| * **Simple Data Extraction:** It shows some capability in basic data extraction and formatting tasks, such as creating Markdown tables or extracting contact information.
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|
|
| #### Weaknesses & Limitations:
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| * **Poor Reasoning:** The model struggles significantly with tasks that require logical reasoning, inference, or multi-step problem-solving. It often fails on word problems and logical puzzles.
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| * **Limited Creativity:** It is not effective at creative writing tasks like continuing a story or generating novel content. Its outputs are often repetitive or nonsensical.
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| * **Instructional Failures:** The model frequently violates the "no extra text" rule by adding conversational chatter. In many cases, it fails the task entirely and repeats the input verbatim.
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| * **Hallucination:** On some tasks (e.g., `To Paragraph`), the model hallucinates content completely unrelated to the input.
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| * **Unreliable for Complex Tasks:** It is not suitable for complex tasks like code refactoring, bug finding, or drafting professional business correspondence.
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|
|
| ### Intended Use
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|
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| This model is intended for **experimental use** and for users on **extremely resource-constrained systems** who are willing to accept a significant trade-off in performance and reliability. It may be suitable for a very limited subset of simple, repetitive text-formatting tasks.
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| It is designed to be used within the **ProseFlow desktop application**, but it is **not the recommended model for general use**.
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|
|
| ## How to Use in ProseFlow
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|
|
| 1. [Download and install the ProseFlow application](https://github.com/LSXPrime/ProseFlow/releases).
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| 2. Navigate to the **Providers -> Local Provider** tab.
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| 3. Click "Manage Models..." and download `ProseFlow-v1-360M-Instruct` from the "Available for Download" list.
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| 4. Once downloaded, select it from the "My Models" list.
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| 5. Set your "Primary Service Type" in ProseFlow to **Local**.
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| 6. Be aware of the limitations described above when executing actions.
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|
|
| ## Training Details
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|
|
| * **Base Model:** [HuggingFaceTB/SmolLM-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct)
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| * **Dataset:** [LSXPrime/ProseFlow-Actions-v1](https://huggingface.co/datasets/LSXPrime/ProseFlow-Actions-v1)
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| * **Fine-tuning Library:** [Unsloth](https://github.com/unslothai/unsloth)
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| * **Fine-tuning Method:** Supervised fine-tuning on a dataset of structured instruction-input-output triplets.
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|
|
| ## License
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|
|
| This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
