File size: 2,216 Bytes
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## `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)
```
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