omnisapiens_sft

OmniSapiens SFT

Fine-tuned Qwen2.5-Omni-7B for human behavior understanding.

Benchmark

Evaluated on keentomato/human_behavior_atlas.

Usage

Installation

pip install transformers torch huggingface_hub

Classification

import json, torch
from huggingface_hub import hf_hub_download
from transformers import Qwen2_5OmniThinkerForConditionalGeneration, AutoProcessor

MODEL_ID = "keentomato/omnisapiens_sft"

# 1. Load backbone and processor
model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(
    MODEL_ID, torch_dtype=torch.float16, device_map="auto"
)
processor = AutoProcessor.from_pretrained(MODEL_ID)

# 2. Load classification heads and label scheme
heads_path = hf_hub_download(MODEL_ID, "heads.bin")
label_path = hf_hub_download(MODEL_ID, "label_scheme.json")
heads_sd = torch.load(heads_path, map_location="cpu")
with open(label_path) as f:
    label_scheme = json.load(f)

# 3. Reconstruct domain heads
global_classes = label_scheme["meta"]["global_classes"]  # {domain: [{index, label}, ...]}
hidden_size = model.config.hidden_size
domain_names = list(global_classes.keys())
domain_heads = torch.nn.ModuleList([
    torch.nn.Linear(hidden_size, len(global_classes[d])) for d in domain_names
])
domain_heads.load_state_dict({k.replace("heads.", ""): v for k, v in heads_sd.items()})
domain_heads.eval().to(model.device).to(torch.float16)
domain_to_id = {d: i for i, d in enumerate(domain_names)}

# 4. Prepare multimodal inputs
# video_tensor: [T, C, H, W] tensor or list of PIL images
# audio_waveform: 1-D numpy array / tensor at 16 kHz
domain = "emotion"  # one of: "sentiment_intensity", "emotion", "mental_health_ptsd", "mental_health_depression", "mental_health_anxiety", "sarcasm", "humour"
messages = [{"role": "user", "content": [
    {"type": "video"},
    {"type": "audio"},
    {"type": "text", "text": "Classify the human behavior expressed."},
]}]
text = processor.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)
inputs = processor(text=[text], videos=[video_tensor], audio=[audio_waveform], return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}

# 5. Forward pass — pool penultimate hidden layer, route through domain head
with torch.no_grad():
    out = model(**inputs, output_hidden_states=True, use_cache=False)
    h = out.hidden_states[-2]                            # [B, T, H]
    mask = inputs["attention_mask"].unsqueeze(-1).float()
    pooled = (h * mask).sum(1) / mask.sum(1)             # [B, H]
    logits = domain_heads[domain_to_id[domain]](pooled.float())  # [B, K_d]
    pred_idx = logits.argmax(dim=-1).item()

label_name = global_classes[domain][pred_idx]["label"]
print(f"Predicted {domain}: {label_name}")

QA / Open-ended generation

messages = [{"role": "user", "content": [
    {"type": "video"},
    {"type": "audio"},
    {"type": "text", "text": "Describe the emotional state of the person in this video."},
]}]
text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = processor(text=[text], videos=[video_tensor], audio=[audio_waveform], return_tensors="pt")
inputs = {k: v.to(model.device) for k, v in inputs.items()}

with torch.no_grad():
    generated = model.generate(**inputs, max_new_tokens=128)

answer = processor.decode(generated[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(answer)