examples/README.md

# Examples

## `load_and_score.py`

End-to-end demo. Loads the v8a adapter on top of a frozen `Qwen/Qwen2.5-7B` and prints all four semiotic readouts for an input passage.

```bash
cd examples
pip install -r ../requirements.txt
python load_and_score.py --text "Vaccine mandates are an obvious public health win."
```

First run downloads `Qwen/Qwen2.5-7B` (~15 GB) from HuggingFace.

## Programmatic use

```python
import json, sys, torch
from pathlib import Path
sys.path.insert(0, "src")

from srt.config import (SRTConfig, MAHConfig, RRMConfig, BENConfig,
                        CommunityConfig, LossConfig)
from srt.adapter import SRTAdapter
from transformers import AutoTokenizer

raw = json.loads(Path("config.json").read_text())
config = SRTConfig(
    backbone_id        = raw["backbone_id"],
    backbone_dtype     = raw["backbone_dtype"],
    mah_layer_indices  = list(raw["mah_layer_indices"]),
    rrm_inject_indices = list(raw["rrm_inject_indices"]),
    community_layer_idx= raw["community_layer_idx"],
    num_mah_layers     = raw["num_mah_layers"],
    mah                = MAHConfig(**raw["mah"]),
    rrm                = RRMConfig(**raw["rrm"]),
    ben                = BENConfig(**raw["ben"]),
    community          = CommunityConfig(**raw["community"]),
    loss               = LossConfig(**{k: v for k, v in raw["loss"].items()
                                       if k in LossConfig.__dataclass_fields__}),
)

model = SRTAdapter(config).cuda().eval()
state = torch.load("adapter.pt", map_location="cuda")
model.load_state_dict(state, strict=False)

tok = AutoTokenizer.from_pretrained(config.backbone_id)
enc = tok("Freedom means different things to different people.",
          return_tensors="pt").to("cuda")

with torch.no_grad():
    out = model(input_ids=enc.input_ids, attention_mask=enc.attention_mask)

print("logits         :", out.logits.shape)                   # (1, T, V)
print("community vec  :", out.community_output.vector.shape)  # (1, 64)
print("divergences    :", [d.shape for d in out.divergences]) # 3× (1, T, 256)
print("r_hat          :", out.ben_output.r_hat.shape)         # (1, T)
print("regime logits  :", out.ben_output.regime_logits.shape) # (1, T, 2)
```