| # HGA_Thinker_7B_v2_export |
|
|
| HGA-Thinker speech LM exported from SFT training (current train_sft.py format). |
| |
| ## Layout |
| - `bridge.pt` — HGA (s/b/c on 32 Whisper layers) + EMCA + audio boundary embeds |
| - `lora/` — PEFT LoRA adapter for the LLM (has_lora=True) |
| - `config.json` — HGAThinkerConfig (llm_dim=3584) |
| - `processor_config.json` — inference defaults |
| - `tokenizer.*` — Qwen2.5 tokenizer |
| - `preprocessor_config.json` — WhisperFeatureExtractor config |
| - `configuration_hga_thinker.py`, `modeling_hga_thinker.py`, `thinker/` — |
| frozen inference code (so this dir loads without the training repo) |
|
|
| ## Base models |
| - whisper: `/apdcephfs_hzlf/share_1227201/zefeng/whisper-large-v3` |
| - llm: `/apdcephfs_hzlf/share_1227201/zefeng/Qwen2.5-7B-Instruct` |
| - bundled: `False` trained_steps: `6000` |
| |
| ## Load & run |
| ```python |
| import sys; sys.path.insert(0, ".") # this dir |
| from modeling_hga_thinker import HGAThinkerForConditionalGeneration |
| |
| model = HGAThinkerForConditionalGeneration.from_pretrained(".", device="cuda") |
| # If base models moved since export, pass overrides: |
| # ...from_pretrained(".", whisper_path="/new/whisper", llm_name="/new/qwen") |
| |
| text = model.chat(audio="test.wav", query="Transcribe this audio.", |
| max_new_tokens=256) |
| print(text) |
| ``` |
| |
| Needs `peft` and (for audio path inputs) `torchaudio` installed. |
| |
