---
license: mit
language:
- en
base_model: LiquidAI/LFM2.5-350M-Base
tags:
- speech-to-text
- transcript-cleanup
- text-correction
- asr-post-processing
- LFM
- LiquidAI
- grpo
- full-fine-tune
- inverse-text-normalization
pipeline_tag: text-generation
datasets:
- juanquivilla/sotto-transcript-cleanup
---

# SottoASR Transcript Cleanup — LFM2.5-350M (Full Precision, soup_30)

[sottoasr.app](https://sottoasr.app) · [MLX 5-bit (recommended)](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit) · [MLX 4-bit (smaller)](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-4bit) · [Training Dataset](https://huggingface.co/datasets/juanquivilla/sotto-transcript-cleanup)

## Overview

Full-precision bf16 fine-tune of [LiquidAI/LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base) for on-device speech-to-text transcript cleanup. This is the **training artifact** — for on-device deployment on Apple Silicon, use the [5-bit MLX variant](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit).

## What's new in soup_30

soup_30 extends v45 with targeted training data covering five failure modes observed on real production transcripts:

- **Multi-number sentences** ("the score was 21 to 14")
- **Year-with-context drift** ("twenty eighteen and twenty twenty seven" → preserve both years)
- **Disconnected number lists** (no phone-number hallucination on "one two three eleven forty six")
- **Within-input duplicates** (same number stated twice — preserve consistency)
- **Long-form preservation** (multi-paragraph dictations with embedded numbers)

The v37+v47 training data (~30K examples) was re-audited end-to-end with a Qwen3.6-27B
judge and bad rows were dropped. A new 66-case adversarial benchmark drives objective
evaluation.

## Headline numbers

| Capability | v36 | v45 | **soup_30 (this model)** |
|---|---:|---:|---:|
| Number accuracy (171-sample stratified val) | 12.9% | 95.9% | **96.5%** |
| 66-case adversarial benchmark (greedy) | n/a | 76% | **86%** |
| 66-case adversarial benchmark (temp 0.7 × 4) | n/a | 77% | **85%** |
| Loops on 264 sampling-mode probes | n/a | 0 | **2** |
| Filler-free on 241 long inputs | 67.2% | 68.0% | **73.0%** |
| Sub-deletion >15% on 241 long inputs | 13.3% | 13.7% | **4.6%** |

Composite score (0.35×num + 0.30×bench_greedy + 0.15×bench_sample + 0.10×filler_long + 0.05×(1-sub15) + 0.05×(1-loops/N)): **89.45**

## Training pipeline

```
LiquidAI/LFM2.5-350M-Base
  → SFT v23 → GRPO v23 (paragraph emission)
  → GRPO v36: full FT with substantive-deletion-aware reward
  → SFT v39: + 12.7K augmented number examples (ITN)
  → GRPO v40-v45: chained refinement, fixed reward + amplified filler penalty
  → SFT/GRPO soup_30: + 5.5K gap-filling examples (multi-number, year-context,
                       disconnected, duplicates, long-form), Qwen-audited and cleaned
```

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "juanquivilla/sotto-cleanup-lfm25-350m",
    dtype=torch.bfloat16, trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("juanquivilla/sotto-cleanup-lfm25-350m")

text = "talk about server three sixty"
prompt = f"### Input:\n{text}\n\n### Output:\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
output = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
if "###" in output:
    output = output[:output.index("###")]
print(output.strip())
```

## All Variants

| Variant | Size | Use Case |
|---------|------|----------|
| **[Full precision (this)](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m)** | 676 MB | Training, GPU inference |
| **[MLX 5-bit](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-5bit)** | ~237 MB | **Recommended for Apple Silicon** |
| [MLX 4-bit](https://huggingface.co/juanquivilla/sotto-cleanup-lfm25-350m-mlx-4bit) | ~195 MB | Smallest |

## License

MIT