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marccgrau/
eaa-gemma3-270m-w2vbert-emotion2vec

Name: eaa-gemma3-270m-w2vbert-emotion2vec
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EAA Fusion Head for Gemma (LoRA) + w2v-bert-2.0 + emotion2vec

This repo hosts the fusion head weights and code for the Emotion-Aware Audio LLM.

LoRA adapter lives at: marccgrau/eaa-gemma3-270m-adapter
Upstream encoders: facebook/w2v-bert-2.0 (semantic) and iic/emotion2vec_base (acoustic via FunASR)
LLM: google/gemma-3-270m

Files

fusion_head.pt — PyTorch state_dict of the fusion/regression head
eaa_config.json — minimal config (IDs, dims, hyperparams)
modeling_eaa.py — the fusion architecture (Dual X-Attn + pooling + [REG] head)

Quickload (Python)

python

import torch, json
from huggingface_hub import hf_hub_download
from modeling_eaa import EAAEmotionRegressor

# Download artifacts
cfg_path = hf_hub_download(repo_id="marccgrau/eaa-gemma3-270m-w2vbert-emotion2vec", filename="eaa_config.json")
with open(cfg_path) as f:
    cfg = json.load(f)

# Recreate Gemma + load LoRA adapter
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
tok = AutoTokenizer.from_pretrained(cfg["gemma_id"], trust_remote_code=True)
llm_base = AutoModelForCausalLM.from_pretrained(cfg["gemma_id"], trust_remote_code=True, torch_dtype=torch.float16).cuda()
llm = PeftModel.from_pretrained(llm_base, cfg["adapter_repo"]).eval()

# Build fusion head and load weights
head = EAAEmotionRegressor(
    d_sem=cfg["d_sem"], d_ac=cfg["d_ac"], llm_hidden=cfg["llm_hidden"],
    fusion_dim=cfg["fusion_dim"], num_audio_tokens=cfg["num_audio_tokens"]
).cuda().eval()
sd_path = hf_hub_download(repo_id="marccgrau/eaa-gemma3-270m-w2vbert-emotion2vec", filename="fusion_head.pt")
head.load_state_dict(torch.load(sd_path, map_location="cpu"))

# Now pass (sem_feats, ac_feats) and (input_ids) to head.forward(..., llm=llm)