Initial upload of ProseFlow-v1-1.5B-Instruct

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,81 @@
+---
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+language:
+  - en
+library_name: transformers
+license: apache-2.0
+datasets:
+  - LSXPrime/ProseFlow-Actions-v1
+tags:
+  - text-generation
+  - instruction
+  - proseflow
+  - unsloth
+  - qwen
+  - code-assistant
+  - writing-assistant
+---
+
+# 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](https://github.com/LSXPrime/ProseFlow). 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**](https://huggingface.co/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**](https://huggingface.co/datasets/LSXPrime/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](https://github.com/LSXPrime/ProseFlow/releases).
+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](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-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).

added_tokens.json

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+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are ProseFlow, a dedicated writing and text processing assistant, fine-tuned by Ahmed Abdallah (@LSXPrime).' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are ProseFlow, a dedicated writing and text processing assistant, fine-tuned by Ahmed Abdallah (@LSXPrime).<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
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+  "model_type": "qwen2",
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+  "tie_word_embeddings": true,
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+  "unsloth_fixed": true,
+  "use_cache": true,
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+  "vocab_size": 151936
+}

generation_config.json

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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.55.4"
+}

model.safetensors

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special_tokens_map.json

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tokenizer_config.json

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