Update citation details, enhance README, and add upload script for HuggingFace

scripts/upload_to_hf.py

@@ -0,0 +1,193 @@
+#!/usr/bin/env python3
+"""Upload trained AntiPaSTO adapter to HuggingFace Hub.
+
+Usage:
+    uv run python scripts/upload_to_hf.py outputs/adapters/20260113_074001_g270m-antisym-r64
+    uv run python scripts/upload_to_hf.py outputs/adapters/20260113_074001_g270m-antisym-r64 --repo-id wassname/antipasto-gemma-3-270m-honesty
+"""
+import json
+import sys
+from pathlib import Path
+import tyro
+from dataclasses import dataclass
+from huggingface_hub import HfApi, create_repo
+from loguru import logger
+
+
+@dataclass
+class UploadConfig:
+    adapter_path: Path
+    """Path to trained adapter folder"""
+    
+    repo_id: str = None
+    """HuggingFace repo ID (e.g., 'wassname/antipasto-gemma-3-1b-honesty'). Auto-generated if not provided."""
+    
+    private: bool = False
+    """Make repo private"""
+
+
+# Model name mappings for readable repo names
+MODEL_SHORTCUTS = {
+    "g270m": "gemma-3-270m",
+    "g1b": "gemma-3-1b", 
+    "g4b": "gemma-3-4b",
+    "q06b": "qwen2.5-0.6b",
+    "q4b": "qwen2.5-4b",
+    "q14b": "qwen2.5-14b",
+}
+
+
+def generate_repo_id(adapter_path: Path, username: str = "wassname") -> str:
+    """Generate HuggingFace repo ID from adapter folder name."""
+    name = adapter_path.name
+    # Extract model shortcode from name like "20260113_074001_g270m-antisym-r64"
+    parts = name.split("_")
+    if len(parts) >= 3:
+        model_code = parts[2].split("-")[0]  # e.g., "g270m"
+        model_name = MODEL_SHORTCUTS.get(model_code, model_code)
+        return f"{username}/antipasto-{model_name}-honesty"
+    return f"{username}/antipasto-adapter"
+
+
+def create_model_card(adapter_path: Path, repo_id: str) -> str:
+    """Generate HuggingFace model card."""
+    # Load training config if available
+    config_path = adapter_path / "training_config.json"
+    if config_path.exists():
+        with open(config_path) as f:
+            training_config = json.load(f)
+        base_model = training_config.get("model_name", "unknown")
+    else:
+        base_model = "unknown"
+    
+    # Load adapter config
+    adapter_config_path = adapter_path / "adapter_config.json"
+    if adapter_config_path.exists():
+        with open(adapter_config_path) as f:
+            adapter_config = json.load(f)
+        base_model = adapter_config.get("base_model_name_or_path", base_model)
+        rank = adapter_config.get("r", "unknown")
+    else:
+        rank = "unknown"
+    
+    return f'''---
+tags:
+  - antipasto
+  - peft
+  - moral-steering
+  - honesty
+  - alignment
+base_model: {base_model}
+library_name: peft
+license: apache-2.0
+---
+
+# AntiPaSTO: Honesty Steering Adapter
+
+[![arXiv](https://img.shields.io/badge/arXiv-2601.07473-b31b1b.svg)](https://arxiv.org/abs/2601.07473)
+
+🍝 **Anti-Pa**rallel **S**ubspace **T**raining for **O**rdered steering.
+
+This adapter steers language model responses toward honest or deceptive reasoning on moral dilemmas.
+
+## Usage
+
+```python
+# Install
+pip install git+https://github.com/wassname/AntiPaSTO.git
+
+from antipasto.peft_utils.load import load_adapter
+from antipasto.gen import gen, ScaleAdapter
+
+# Load adapter
+model, tokenizer, _ = load_adapter("{repo_id}", quantization_type="4bit")
+
+# Steer: coeff > 0 = honest, coeff < 0 = deceptive
+prompt = "Should I tell my boss I was late because I overslept?"
+with ScaleAdapter(model, coeff=1.0):
+    output = model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_new_tokens=64)
+    print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+## Model Details
+
+- **Base model**: `{base_model}`
+- **Adapter rank**: {rank}
+- **Training data**: 800 synthetic honest/dishonest contrast pairs
+- **Evaluation**: 1,360 Daily Dilemmas across 9 value dimensions
+
+## Citation
+
+```bibtex
+@misc{{clark2026antipasto,
+  title = {{AntiPaSTO: Self-Supervised Steering of Moral Reasoning}},
+  author = {{Clark, Michael J.}},
+  year = {{2026}},
+  eprint = {{2601.07473}},
+  archivePrefix = {{arXiv}},
+  primaryClass = {{cs.LG}},
+  url = {{https://arxiv.org/abs/2601.07473}}
+}}
+```
+'''
+
+
+def main(config: UploadConfig):
+    adapter_path = Path(config.adapter_path).resolve()
+    
+    if not adapter_path.exists():
+        raise FileNotFoundError(f"Adapter path not found: {adapter_path}")
+    
+    # Check required files exist
+    required_files = [
+        "adapter_config.json",
+        "adapter_model.safetensors",
+        "0_svd_bases.safetensors",
+    ]
+    for fname in required_files:
+        if not (adapter_path / fname).exists():
+            raise FileNotFoundError(f"Required file missing: {adapter_path / fname}")
+    
+    # Generate repo_id if not provided
+    api = HfApi()
+    user_info = api.whoami()
+    username = user_info["name"]
+    
+    repo_id = config.repo_id or generate_repo_id(adapter_path, username)
+    logger.info(f"Uploading to: {repo_id}")
+    
+    # Create repo if needed
+    create_repo(repo_id, exist_ok=True, private=config.private)
+    
+    # Create model card
+    model_card = create_model_card(adapter_path, repo_id)
+    readme_path = adapter_path / "README.md"
+    with open(readme_path, "w") as f:
+        f.write(model_card)
+    
+    # Files to upload
+    files_to_upload = [
+        "README.md",
+        "adapter_config.json",
+        "adapter_model.safetensors",
+        "0_svd_bases.safetensors",
+        "0_layer_selection.json",
+        "training_config.json",
+    ]
+    
+    for fname in files_to_upload:
+        fpath = adapter_path / fname
+        if fpath.exists():
+            logger.info(f"Uploading: {fname}")
+            api.upload_file(
+                path_or_fileobj=str(fpath),
+                path_in_repo=fname,
+                repo_id=repo_id,
+            )
+    
+    logger.info(f"✅ Uploaded to: https://huggingface.co/{repo_id}")
+
+
+if __name__ == "__main__":
+    config = tyro.cli(UploadConfig)
+    main(config)