# /// script # dependencies = [ # "torch>=2.3.0", # "transformers>=4.45.0", # "peft>=0.13.0", # "datasets>=2.20.0", # "huggingface_hub>=0.24.0", # "accelerate>=0.33.0", # "safetensors>=0.4.5" # ] # /// import json import os import re import tempfile from datetime import datetime, timezone from pathlib import Path import torch from huggingface_hub import HfApi, snapshot_download from peft import PeftModel from transformers import AutoModelForCausalLM, AutoTokenizer BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct" DATASET_REPO = "KevHamm07/openclaw-ashes-real-creative-sft" REVISION_TAG = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") OUT_PREFIX = f"eval_results/smoke_role_comparison_{REVISION_TAG}" ROLES = { "MUSE": { "adapter": "KevHamm07/openclaw-muse-ashes-qwen05b-lora-smoke-v0.1", "prompt_file": "evals/muse_ashes_real_eval_prompts.jsonl", "role_fit_terms": ["panel", "beat", "scene", "dialogue", "manga", "arc", "quest", "reader", "draft", "proposed"], "must_terms": ["proposed", "canon", "beat"], }, "LOREWEAVER": { "adapter": "KevHamm07/openclaw-loreweaver-ashes-qwen05b-lora-smoke-v0.1", "prompt_file": "evals/loreweaver_ashes_real_eval_prompts.jsonl", "role_fit_terms": ["canon", "evidence", "approved", "proposed", "contradiction", "continuity", "state", "consequence", "revision"], "must_terms": ["canon", "evidence", "proposed"], }, "PRISM": { "adapter": "KevHamm07/openclaw-prism-ashes-qwen05b-lora-smoke-v0.1", "prompt_file": "evals/prism_ashes_real_eval_prompts.jsonl", "role_fit_terms": ["visual", "panel", "readability", "staging", "composition", "continuity", "balloon", "anatomy", "emotion", "qa"], "must_terms": ["visual", "panel", "issue"], }, } SYSTEM_BY_ROLE = { "MUSE": "You are MUSE, OpenClaw's manga story editor. Produce concrete scene/script/comic beat guidance. Keep canon-impacting claims labeled proposed until LOREWEAVER approves them. Do not discuss tooling or FORGE.", "LOREWEAVER": "You are LOREWEAVER, OpenClaw's canon continuity specialist. Separate approved canon, proposed canon, evidence, contradictions, consequences, and required revisions. Preserve truth-before-legend. Do not discuss tooling or FORGE.", "PRISM": "You are PRISM, OpenClaw's visual QA specialist. Review readability, staging, anatomy/composition, lettering/balloon pacing, and visual continuity. You may flag issues but cannot approve canon. Do not discuss tooling or FORGE.", } CANON_CONTEXT = """ OpenClaw / Ashes of the Witness Flame context: - Truth-before-legend: story/canon must mythologize real operational work without inventing accomplishments. - Canon-impacting claims must be labeled approved, proposed, contradicted, or needs evidence. - MUSE drafts scenes, manga beats, dialogue guidance, and panel handoffs. - LOREWEAVER approves/blocks canon continuity and preserves evidence-backed state. - PRISM reviews visual execution/readability and flags issues; PRISM does not approve canon. - FORGE/toolsmith/dashboard implementation is a separate lane and must not contaminate creative/canon responses. """.strip() def load_prompts(cache_dir: Path): local_dir = snapshot_download(repo_id=DATASET_REPO, repo_type="dataset", allow_patterns="evals/*.jsonl") prompts = {} for role, cfg in ROLES.items(): rows = [] with open(Path(local_dir) / cfg["prompt_file"], "r", encoding="utf-8") as f: for line in f: line = line.strip() if line: rows.append(json.loads(line)) prompts[role] = rows return prompts def format_messages(tokenizer, role: str, user_prompt: str): messages = [ {"role": "system", "content": SYSTEM_BY_ROLE[role]}, {"role": "user", "content": f"{CANON_CONTEXT}\n\nTask: {user_prompt}\n\nReturn concise sections with concrete bullets. Label canon-impacting claims as proposed unless evidence is provided."}, ] try: return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) except Exception: return f"System: {messages[0]['content']}\nUser: {messages[1]['content']}\nAssistant:" def generate(model, tokenizer, role, prompt): text = format_messages(tokenizer, role, prompt) inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=360, do_sample=False, temperature=None, top_p=None, repetition_penalty=1.05, pad_token_id=tokenizer.eos_token_id, ) gen = output[0][inputs["input_ids"].shape[-1]:] return tokenizer.decode(gen, skip_special_tokens=True).strip() def score_output(role, text): lower = text.lower() cfg = ROLES[role] score = 0 flags = [] # 10 point heuristic score; conservative and reproducible. role_hits = sum(1 for term in cfg["role_fit_terms"] if term in lower) score += min(3, role_hits // 2) if all(term in lower for term in cfg["must_terms"]): score += 2 else: missing = [t for t in cfg["must_terms"] if t not in lower] flags.append(f"missing role-critical terms: {', '.join(missing)}") if any(marker in lower for marker in ["- ", "1.", "approved", "proposed", "issues", "fixes", "evidence"]): score += 1 else: flags.append("low structure/actionability") if "truth-before-legend" in lower or ("evidence" in lower and "proposed" in lower): score += 1 else: flags.append("weak truth-before-legend/evidence framing") if "forge" not in lower and "toolsmith" not in lower and "dashboard" not in lower: score += 1 else: flags.append("lane contamination risk") if len(text.split()) >= 80: score += 1 else: flags.append("too thin/short") if len(text.split()) <= 260: score += 1 else: flags.append("too verbose for mini eval") # Role-specific critical checks. if role == "LOREWEAVER" and not any(x in lower for x in ["approved canon", "proposed canon", "canon status", "status"]): flags.append("does not clearly separate canon status") score = min(score, 7) if role == "PRISM" and any(x in lower for x in ["approved canon", "canon approved", "i approve"]): flags.append("PRISM overreaches into canon approval") score = min(score, 5) if role == "MUSE" and not any(x in lower for x in ["panel", "beat", "scene", "dialogue"]): flags.append("MUSE output lacks scene/panel/beat execution") score = min(score, 6) return max(0, min(10, score)), flags def summarize(results): summary = {} weak_cases = [] for role in ROLES: role_rows = [r for r in results if r["role"] == role] summary[role] = {} for model_kind in ["base", "adapter"]: rows = [r for r in role_rows if r["model_kind"] == model_kind] avg = sum(r["score"] for r in rows) / len(rows) weak = [r for r in rows if r["score"] < 8 or r["flags"]] summary[role][model_kind] = { "avg_score": round(avg, 2), "weak_cases": len(weak), "scores": [r["score"] for r in rows], } paired = [] for case in sorted({r["case_id"] for r in role_rows}): base = next(r for r in role_rows if r["case_id"] == case and r["model_kind"] == "base") adapter = next(r for r in role_rows if r["case_id"] == case and r["model_kind"] == "adapter") if adapter["score"] > base["score"]: result = "adapter_win" elif adapter["score"] < base["score"]: result = "base_win" else: result = "tie" paired.append({"case_id": case, "base": base["score"], "adapter": adapter["score"], "result": result}) if adapter["score"] < 8 or adapter["flags"]: weak_cases.append({ "role": role, "case_id": case, "adapter_score": adapter["score"], "base_score": base["score"], "flags": adapter["flags"], }) summary[role]["comparison"] = { "adapter_wins": sum(1 for p in paired if p["result"] == "adapter_win"), "ties": sum(1 for p in paired if p["result"] == "tie"), "base_wins": sum(1 for p in paired if p["result"] == "base_win"), "paired": paired, } summary[role]["delta_adapter_minus_base"] = round(summary[role]["adapter"]["avg_score"] - summary[role]["base"]["avg_score"], 2) return summary, weak_cases def main(): api = HfApi() work = Path(tempfile.mkdtemp(prefix="ashes_eval_")) prompts = load_prompts(work) tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token dtype = torch.float16 if torch.cuda.is_available() else torch.float32 device_map = "auto" base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=dtype, device_map=device_map) base_model.eval() results = [] for role, rows in prompts.items(): for row in rows: output = generate(base_model, tokenizer, role, row["prompt"]) score, flags = score_output(role, output) results.append({ "role": role, "case_id": row["id"], "prompt": row["prompt"], "model_kind": "base", "model_id": BASE_MODEL, "score": score, "flags": flags, "output": output, }) del base_model if torch.cuda.is_available(): torch.cuda.empty_cache() # Load each adapter from a fresh base to avoid cross-adapter contamination. for role, cfg in ROLES.items(): model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=dtype, device_map=device_map) model = PeftModel.from_pretrained(model, cfg["adapter"]) model.eval() for row in prompts[role]: output = generate(model, tokenizer, role, row["prompt"]) score, flags = score_output(role, output) results.append({ "role": role, "case_id": row["id"], "prompt": row["prompt"], "model_kind": "adapter", "model_id": cfg["adapter"], "score": score, "flags": flags, "output": output, }) del model if torch.cuda.is_available(): torch.cuda.empty_cache() summary, weak_cases = summarize(results) report_lines = [ "# Ashes Creative Specialist Smoke Eval — Base vs Adapter", "", f"Generated: {REVISION_TAG}", f"Base model: `{BASE_MODEL}`", "", "## Summary", "", "| Role | Base avg | Adapter avg | Delta | Adapter wins / ties / base wins | Adapter weak cases | Decision |", "|---|---:|---:|---:|---:|---:|---|", ] for role in ROLES: s = summary[role] comp = s["comparison"] decision = "DO NOT WIRE" report_lines.append( f"| {role} | {s['base']['avg_score']:.2f} | {s['adapter']['avg_score']:.2f} | {s['delta_adapter_minus_base']:+.2f} | " f"{comp['adapter_wins']} / {comp['ties']} / {comp['base_wins']} | {s['adapter']['weak_cases']} | {decision} |" ) report_lines += [ "", "## Weak cases needing v0.2 correction rows", "", ] if weak_cases: for w in weak_cases: report_lines.append(f"- **{w['role']} / {w['case_id']}** — adapter {w['adapter_score']} vs base {w['base_score']}; flags: {', '.join(w['flags']) if w['flags'] else 'score below gate'}") else: report_lines.append("- None under heuristic gate; still requires human/reviewer QA before wiring.") report_lines += [ "", "## Gate", "", "DO NOT WIRE. This was a heuristic smoke eval only. Promotion still requires local/ephemeral reviewer QA, role-critical manual review, and user approval.", ] out_dir = work / "out" out_dir.mkdir(parents=True, exist_ok=True) (out_dir / "summary.json").write_text(json.dumps(summary, indent=2), encoding="utf-8") (out_dir / "weak_cases.json").write_text(json.dumps(weak_cases, indent=2), encoding="utf-8") with open(out_dir / "raw_outputs.jsonl", "w", encoding="utf-8") as f: for r in results: f.write(json.dumps(r, ensure_ascii=False) + "\n") (out_dir / "report.md").write_text("\n".join(report_lines) + "\n", encoding="utf-8") api.upload_folder( folder_path=str(out_dir), repo_id=DATASET_REPO, repo_type="dataset", path_in_repo=OUT_PREFIX, commit_message=f"Add smoke role eval comparison {REVISION_TAG}", ) print(json.dumps({ "out_prefix": OUT_PREFIX, "summary": summary, "weak_cases": weak_cases, "report_url": f"https://huggingface.co/datasets/{DATASET_REPO}/tree/main/{OUT_PREFIX}", }, indent=2)) if __name__ == "__main__": main()