Add runnable load-and-generate usage snippet

README.md

@@ -37,28 +37,78 @@ Deterministic accuracy with dropout disabled and learned stop gate:
 ## Related
 
 - Code: [ModalityDance/SLPO](https://github.com/ModalityDance/SLPO)
+- Project page: [modalitydance.github.io/SLPO](https://modalitydance.github.io/SLPO/)
 - Base model: [ModalityDance/latent-tts-codi](https://huggingface.co/ModalityDance/latent-tts-codi)
 - Sibling: [ModalityDance/slpo-coconut-gpt2](https://huggingface.co/ModalityDance/slpo-coconut-gpt2)
-- Latent TTS collection: [Latent TTS](https://huggingface.co/collections/ModalityDance/latent-tts-69635ae5a732662b4d41225f)
+- Collection: [ModalityDance/SLPO](https://huggingface.co/collections/ModalityDance/slpo)
 
 ## Installation
 
 ```bash
-huggingface-cli download ModalityDance/slpo-codi-gpt2 --local-dir checkpoints/slpo-codi-gpt2
+git clone https://github.com/ModalityDance/SLPO.git
+cd SLPO
+pip install -r requirements.txt   # plus a CUDA PyTorch build
+hf download ModalityDance/slpo-codi-gpt2 --local-dir checkpoints/slpo-codi-gpt2
 ```
 
 ## Quick Start
 
-Inference uses the custom latent generation stack from the [SLPO](https://github.com/ModalityDance/SLPO) repository:
+Batched eval (paper Acc settings):
 
 ```bash
 CKPT=checkpoints/slpo-codi-gpt2 \
 MODEL_TYPE=codi STOP_POLICY=gate \
-MAX_LATENT_LENGTH=12 \
+STOP_GATE_THRESHOLD=0.7 MAX_LATENT_LENGTH=12 \
+DATA=data/gsm_test.json \
 bash scripts/eval.sh
 ```
 
-Set `stop_gate_threshold=0.7` to match the paper evaluation.
+Minimal Python (from the repo root; needs the SLPO latent generation stack):
+
+```python
+import torch
+from transformers import AutoTokenizer
+
+from src.models.generation import LatentGenerationMixin, LatentGenerationConfig
+from src.paths import get_model_class
+
+model_id = "ModalityDance/slpo-codi-gpt2"
+backbone_cls = get_model_class("codi")
+
+class LatentModel(backbone_cls, LatentGenerationMixin):
+    pass
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+model = LatentModel.from_pretrained(model_id)
+model.eval()
+
+question = (
+    "Janet's ducks lay 16 eggs per day. She eats three for breakfast every morning "
+    "and bakes muffins for her friends every day with four. She sells the remainder "
+    "at the farmers' market daily for $2 per fresh duck egg. "
+    "How much in dollars does she make every day at the farmers' market?"
+)
+prompt = question + "<|start-latent|>"
+inputs = tokenizer(prompt, return_tensors="pt")
+
+gen_cfg = LatentGenerationConfig(
+    stop_policy="gate",
+    max_latent_length=12,
+    stop_gate_threshold=0.7,
+    max_new_tokens=128,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+    bos_token_id=tokenizer.bos_token_id,
+)
+
+with torch.no_grad():
+    output = model.generate(**inputs, generation_config=gen_cfg)
+sequences = output.sequences if hasattr(output, "sequences") else output
+print(tokenizer.decode(sequences[0], skip_special_tokens=True))
+```
 
 ## Citation