README.md

---
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 4070 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)