---
base_model: unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit
library_name: peft
language:
- bm
pipeline_tag: automatic-speech-recognition
tags:
- gemma3n
- lora
- peft
- unsloth
- audio
- bambara
---

# gemma-3n-bm-asr-01

A LoRA adapter for Gemma 3n E4B-it that adapts the speech path for Bambara, trained with
[Unsloth](https://github.com/unslothai/unsloth) and TRL.

Adapter weights only. Load onto `unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit` — a 4-bit NF4
checkpoint, and the adapter belongs on that same quantised base.

## Config

| | |
| --- | --- |
| Rank `r` | 16 |
| `lora_alpha` | 32 |
| `use_rslora` | `false` — effective scale `alpha / r` = 2.0 |
| `lora_dropout` | 0 |

Adapted: the decoder's attention and MLP projections, the 12 audio conformer blocks, and the
audio→text embedding projector.

## Usage

```python
import torch
from transformers import AutoProcessor, Gemma3nForConditionalGeneration
from peft import PeftModel

base = Gemma3nForConditionalGeneration.from_pretrained(
    "unsloth/gemma-3n-E4B-it-unsloth-bnb-4bit",
    dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
)
model = PeftModel.from_pretrained(base, "djelia/gemma-3n-bm-asr-01")
model.eval()

processor = AutoProcessor.from_pretrained("djelia/gemma-3n-bm-asr-01", padding_side="left")

messages = [{
    "role": "user",
    "content": [
        {"type": "audio", "path": "sample.wav"},
        {"type": "text", "text": "Transcribe this audio."},
    ],
}]
inputs = processor.apply_chat_template(
    messages, tokenize=True, return_dict=True,
    return_tensors="pt", add_generation_prompt=True,
).to(model.device)

out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(processor.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

## Notes

The processor resamples the audio itself. Gemma 3n's audio encoder emits about 6 tokens per
second, so long recordings consume context quickly.

`bitsandbytes` and `accelerate` are required for the 4-bit base.