Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,187 @@
+---
+language:
+- en
+license: mit
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+- text-generation
+- fine-tuned
+- lora
+- gguf
+- speech-to-text
+- text-cleanup
+- unsloth
+- qwen2
+pipeline_tag: text-generation
+datasets:
+- Abdullahu5mani/flowscribe-dataset
+---
+
+# FlowScribe — Qwen2.5-0.5B Speech Transcript Formatter
+
+A fine-tuned version of [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) that converts raw, messy speech-to-text output into clean, formatted text across multiple writing styles.
+
+**GitHub:** [github.com/Abdullahu5mani/flowscribe](https://github.com/Abdullahu5mani/flowscribe)
+
+---
+
+## The Problem
+
+Voice dictation tools like Whisper produce transcripts full of filler words (`um`, `uh`, `like`), self-corrections (`make it 5... no wait, 6`), and no punctuation or formatting. This model post-processes those transcripts into polished text, with awareness of the desired output style.
+
+---
+
+## Styles
+
+| Style | Behavior |
+|---|---|
+| `Auto` | Intelligent default — removes fillers, fixes grammar, handles self-corrections, applies structure |
+| `Professional` | Formal business tone, structured layout, perfect grammar |
+| `Casual` | Keeps the speaker's voice, light cleanup, contractions preserved |
+| `Verbatim` | Preserves exact wording, only strips `um`/`uh` and applies spoken formatting commands |
+| `Software_Dev` | Formats code terms, variable names (`camelCase`, `snake_case`), technical jargon |
+| `Enthusiastic` | High energy, exclamation marks, positive phrasing |
+
+---
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "Abdullahu5mani/flowscribe-qwen2.5-0.5b"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+def format_transcript(raw_text, style="Auto"):
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant that transcribes and formats text based on a specific style instruction."
+        },
+        {
+            "role": "user",
+            "content": f"Transcribe and format this with style: {style}\nInput: {raw_text}"
+        }
+    ]
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer([text], return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
+    output_ids = outputs[0][len(inputs.input_ids[0]):]
+    return tokenizer.decode(output_ids, skip_special_tokens=True)
+
+# Examples
+print(format_transcript(
+    "um so the meeting is at 5... no wait make it 6 and uh we need to discuss the q3 budget",
+    style="Professional"
+))
+# → "The meeting is at 6 PM to discuss the Q3 budget."
+
+print(format_transcript(
+    "the api endpoint is slash api slash users new line it takes a POST request with JSON",
+    style="Software_Dev"
+))
+# → "The API endpoint is `/api/users`\nIt takes a POST request with JSON."
+```
+
+---
+
+## GGUF (Quantized) Usage
+
+A Q4_K_M quantized GGUF version is included in this repository for fast CPU/GPU inference via [llama-cpp-python](https://github.com/abetlen/llama-cpp-python).
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="model_q4_k_m.gguf",
+    n_ctx=2048,
+    n_gpu_layers=-1,   # Set to 0 for CPU-only
+    verbose=False
+)
+
+response = llm.create_chat_completion(
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a helpful assistant that transcribes and formats text based on a specific style instruction."
+        },
+        {
+            "role": "user",
+            "content": "Transcribe and format this with style: Casual\nInput: hey um so i was thinking we could like grab lunch tomorrow you know around noon ish"
+        }
+    ],
+    max_tokens=256,
+    temperature=0.1,
+)
+print(response["choices"][0]["message"]["content"])
+# → "Hey, I was thinking we could grab lunch tomorrow around noon."
+```
+
+---
+
+## Model Details
+
+| Property | Value |
+|---|---|
+| Base model | Qwen/Qwen2.5-0.5B-Instruct |
+| Fine-tuning method | LoRA (via [Unsloth](https://github.com/unslothai/unsloth)) |
+| Parameters | ~500M |
+| Training epochs | 3 |
+| Learning rate | 2e-5 |
+| Effective batch size | 16 (batch 2 × grad accumulation 8) |
+| Sequence length | 2048 |
+| Optimizer | AdamW 8-bit |
+| Training hardware | NVIDIA RTX 3060 8GB VRAM |
+| Chat template | ChatML |
+| Quantization | Q4_K_M (via llama.cpp) |
+
+---
+
+## Training Data
+
+Trained on ~19,800 synthetically generated examples from [flowscribe-dataset](https://huggingface.co/datasets/Abdullahu5mani/flowscribe-dataset).
+
+Each example is an Alpaca-style JSON object:
+```json
+{
+  "instruction": "Transcribe and format this with style: Professional",
+  "input": "um so like the uh proposal is due friday and we need to finalize the, i mean confirm the budget",
+  "output": "The proposal is due Friday and we need to confirm the budget."
+}
+```
+
+Data was generated using Google Gemini (primary) and 16 free OpenRouter models (fallback) across 10 domain scenarios: business email, software dev, personal messages, productivity lists, medical notes, and more.
+
+---
+
+## Limitations
+
+- Optimized for English only
+- Training data is synthetic — real-world dictation edge cases may vary
+- The 0.5B parameter size prioritizes speed and local deployment over raw capability
+- Dataset reached ~19.8K examples (target was 50K); further training on more data would improve robustness
+
+---
+
+## Files
+
+| File | Description |
+|---|---|
+| `model.safetensors` | Full-precision fine-tuned weights |
+| `model_q4_k_m.gguf` | Q4_K_M quantized GGUF for llama.cpp |
+| `config.json` | Model configuration |
+| `tokenizer.json` | Tokenizer |
+| `chat_template.jinja` | ChatML chat template |
+
+---
+
+## License
+
+MIT — see [LICENSE](https://github.com/Abdullahu5mani/flowscribe/blob/main/LICENSE)