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---
base_model: HuggingFaceTB/SmolLM-360M-Instruct
language:
- en
library_name: transformers
license: apache-2.0
datasets:
- LSXPrime/ProseFlow-Actions-v1
tags:
- text-generation
- instruction
- proseflow
- unsloth
- smollm
- writing-assistant
---
# ProseFlow-v1-360M-Instruct
**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.
The model was fine-tuned on the [**ProseFlow-Actions-v1**](https://huggingface.co/datasets/LSXPrime/ProseFlow-Actions-v1) dataset.
**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
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.
### Performance and Capabilities
Evaluations show that while this model is extremely fast and has very low resource requirements, its capabilities are limited.
#### Strengths:
* **Extremely Lightweight:** Can run on devices with very limited RAM and computational power.
* **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.
* **Simple Data Extraction:** It shows some capability in basic data extraction and formatting tasks, such as creating Markdown tables or extracting contact information.
#### Weaknesses & Limitations:
* **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.
* **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.
* **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.
* **Hallucination:** On some tasks (e.g., `To Paragraph`), the model hallucinates content completely unrelated to the input.
* **Unreliable for Complex Tasks:** It is not suitable for complex tasks like code refactoring, bug finding, or drafting professional business correspondence.
### Intended Use
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.
It is designed to be used within the **ProseFlow desktop application**, but it is **not the recommended model for general use**.
## How to Use in ProseFlow
1. [Download and install the ProseFlow application](https://github.com/LSXPrime/ProseFlow/releases).
2. Navigate to the **Providers -> Local Provider** tab.
3. Click "Manage Models..." and download `ProseFlow-v1-360M-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. Be aware of the limitations described above when executing actions.
## Training Details
* **Base Model:** [HuggingFaceTB/SmolLM-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct)
* **Dataset:** [LSXPrime/ProseFlow-Actions-v1](https://huggingface.co/datasets/LSXPrime/ProseFlow-Actions-v1)
* **Fine-tuning Library:** [Unsloth](https://github.com/unslothai/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](https://www.apache.org/licenses/LICENSE-2.0). |