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Model: Abdullahu5mani/flowscribe-qwen2.5-0.5b-v2
Source: Original Platform

.gitattributes

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README.md

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+---
+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
+- conversational
+pipeline_tag: text-generation
+datasets:
+- Abdullahu5mani/flowscribe-dataset
+---
+
+# FlowScribe — Qwen2.5-0.5B Speech Transcript Formatter (v2)
+
+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-v2"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+def format_transcript(raw_text, style="Auto"):
+    messages = [
+        {
+            "role": "system",
+            "content": "You are Flowscribe, an expert Speech-to-Text post-processing AI. You accurately transcribe and format 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 Flowscribe, an expert Speech-to-Text post-processing AI. You accurately transcribe and format 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 |
+|---|---|
+| Version | v2 |
+| Base model | Qwen/Qwen2.5-0.5B-Instruct |
+| Fine-tuning method | LoRA (via [Unsloth](https://github.com/unslothai/unsloth)) |
+| Parameters | ~500M (72.4% trained) |
+| Training epochs | 3 |
+| Learning rate | 2e-5 |
+| Effective batch size | 16 (batch 8 × grad accumulation 2) |
+| Sequence length | 2048 |
+| Optimizer | AdamW 8-bit |
+| Final training loss | 0.616 |
+| Training hardware | NVIDIA RTX 4070 Laptop GPU 8GB |
+| Chat template | ChatML |
+| Quantization | Q4_K_M (via llama.cpp) |
+
+---
+
+## Training Data
+
+Trained on ~27,400 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
+
+---
+
+## Files
+
+| File | Description |
+|---|---|
+| `model.safetensors` | Full-precision fine-tuned weights (BF16) |
+| `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)

added_tokens.json

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+{
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+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
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+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- 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 Qwen, created by Alibaba Cloud. You are a helpful assistant.<|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"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 896,
+  "initializer_range": 0.02,
+  "intermediate_size": 4864,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 14,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "pad_token_id": 151665,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.3.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2026.3.18",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
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+    151643
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+  "max_length": 32768,
+  "pad_token_id": 151665,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.3.0"
+}

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model_q4_k_m.gguf

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special_tokens_map.json

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+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
+    "content": "<|vision_pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}