Initial upload of ProseFlow-v1-360M-Instruct-GGUF

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README.md

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+---
+base_model: LSXPrime/ProseFlow-v1-360M-Instruct
+base_model_relation: quantized
+language:
+  - en
+library_name: gguf
+pipeline_tag: text-generation
+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.
+
+## Provided Files & Quantization Details
+
+This repository provides multiple versions of the model, allowing users to choose the best balance of performance and
+resource usage for their specific hardware. All quantized versions are provided in the GGUF format for broad
+compatibility.
+
+| File Name (Quantization) | VRAM Usage (Approx.) | Performance                                        | Recommended Use Case                        |
+|:-------------------------|:---------------------|:---------------------------------------------------|:--------------------------------------------|
+| `Q8_0`                   | ~1 GB                | **Best Overall.** Nearly identical to FP16.        | **The recommended default for most users.** |
+| `Q4_K_M`                 | ~900 MB              | **Low Quality.** Noticeable degradation in nuance. | For maximum speed on low-power devices.     |
+
+**Note on Quantization:** To maintain the highest possible quality, the token embeddings and the final output layer were
+kept at F16 precision. Additionally, an importance matrix was used for calibration during the quantization process. This
+is why the quantized files are larger than what might typically be expected, as this method significantly improves their
+performance and coherence.
+
+#### 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 select the desired version of `ProseFlow-v1-360M-Instruct` from the "Available for
+   Download" list. **We recommend starting with `Q8_0`.**
+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 using LoRA 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).