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

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.