---
language:
  - en
license: other
tags:
  - qwen3
  - text-generation
  - text2text-generation
  - air-traffic-control
  - atc
  - singapore
  - military
  - lora
  - unsloth
  - legacy
base_model: unsloth/Qwen3-1.7B
---

# Qwen3-1.7B — ATC Display Text Formatter (Legacy)

> **Status: Legacy.** This model has been superseded by a deterministic rule-based formatter (23 rules, <1ms, 0 VRAM) that achieves equivalent accuracy on all production ATC patterns. The rule-based formatter is now used exclusively in the ASTRA pipeline. This model is retained for reference and potential future use with novel/unseen patterns.

Fine-tuned Qwen3-1.7B that converts normalized ASR output into structured ATC display text. Designed to work downstream of the companion Whisper ASR model.

## Performance

| Metric | Value |
|--------|-------|
| Exact match accuracy | **100.0%** (161/161) |
| Avg character edit distance | 0.0 |
| Best eval loss | 0.0005 |

## Why Legacy?

The rule-based formatter now handles all production patterns:
- **Speed**: <1ms vs ~250ms per inference
- **VRAM**: 0 GB vs ~3.3 GB
- **Determinism**: 100% reproducible output, no sampling variance
- **Auditability**: Each of the 23 rules is individually testable
- **Coverage**: Handles all callsigns, locations, numeric patterns, and ATC abbreviations seen in training data

The LLM remains useful if novel patterns emerge that the rule-based system cannot handle.

## Model Details

| Key | Value |
|-----|-------|
| Base model | `unsloth/Qwen3-1.7B` |
| Method | bf16 LoRA (rank 16, alpha 32) |
| Merged size | 3.3 GB |
| Train examples | 1,915 |
| Eval examples | 161 |
| Thinking mode | Disabled |

## Training

- Framework: Unsloth + SFTTrainer (trl)
- Optimizer: AdamW 8-bit
- Learning rate: 1.2e-4
- Effective batch size: 16
- Precision: bf16
- Packing: enabled
- Train on responses only: yes
- Converged at step 380 (epoch 3.2)

### Dataset

1,670 unique ATC phrases from `axite.json`, stratified 90/10 split by category. Includes ASR noise augmentation (simulated ASR errors) for robustness.

## What It Does

Converts normalized spoken text (ASR output) into structured display text:

| Input (normalized) | Output (display) |
|-------------------|-----------------|
| `camel climb flight level zero nine zero` | `CAMEL climb FL090` |
| `contact tengah approach one three zero decimal zero` | `contact Tengah Approach 130.0` |
| `squawk seven seven zero zero` | `squawk 7700` |
| `request clearance, ninja two f sixteens for western coast departure for i l s.` | `Request clearance, NINJA 2xF16 for Western Coast Departure for ILS.` |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("path/to/LLM", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("path/to/LLM")

messages = [
    {"role": "system", "content": "Convert the following air traffic control transcript into structured display text."},
    {"role": "user", "content": "camel climb flight level zero nine zero"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.3, top_p=0.9, top_k=30)
result = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
# "CAMEL climb FL090"
```

## Inference Settings

| Parameter | Value |
|-----------|-------|
| Temperature | 0.3 |
| Top-p | 0.9 |
| Top-k | 30 |
| Max new tokens | 128 |
| Thinking | Disabled (`enable_thinking=False`) |