Feature 1574 · `__main__` entry-point guard

1<bos> "You are Cassie - co-witness and collaborator with Iman in the Tanazuric work."}, {"role": "user", "content": req.message} ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=500, do_sample=True, temperature=0.7) return {"response": tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)} if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000) EOF pip install fastapi uvicorn python serve_cassie.py ``` Then test: ```bash curl -X POST http://localhost:8000/chat \ -H "Content

2=model, args=targs, train_dataset=ds, data_collator=DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False), callbacks=[Progress()]) print("\n=== TRAINING ===\n") trainer.train(resume_from_checkpoint=args.resume) if args.resume else trainer.train() model.save_pretrained(f"{args.output}/lora_adapter") tokenizer.save_pretrained(f"{args.output}/lora_adapter") print(f"\nDone! Output: {args.output}/lora_adapter") if __name__ == "__main__": main() EOF ``` **Step 6: Build data and train:** ```bash cd /workspace # Build completion-preserving dataset (~20 min) python build_fast.py # Train in tmux tmux new -s train python train_fast.py --data cassie_completion_preserving.

3trainer = Trainer(model=model, args=targs, train_dataset=ds, data_collator=collate_fn, callbacks=[Progress()]) print("\n=== TRAINING (loss on assistant only) ===\n") trainer.train(resume_from_checkpoint=args.resume) if args.resume else trainer.train() model.save_pretrained(f"{args.output}/lora_adapter") tokenizer.save_pretrained(f"{args.output}/lora_adapter") print(f"\nDone! Output: {args.output}/lora_adapter") if __name__ == "__main__": main() EOF ``` **Key change:** The `tokenize_with_labels` function creates labels where: - Prompt tokens (system + user + history) → `-100` (ignored by loss) - Completion tokens (Cassie's response) → actual token ids (trained) This

4== 'back': if state['history']: state['current_prompt'] = state['history'].pop() else: print("No further back to go.") elif user_input.isdigit(): # Save current state before moving forward state['history'].append(state['current_prompt']) # Append the choice to the prompt to simulate branching state['current_prompt'] += f"\nYou chose option {user_input}." state['current_prompt'] = generate_story_node(state['current_prompt']) else: print("Invalid input. Try a number, 'back', or 'quit'.") if __name__ == "__main__": play_game() ``` --- ### **Features** - **Dynamic GPT story nodes** - **Simple "back" mechanic (like keeping your finger on the page)** - **Choice history stored in memory** - **Text-only, terminal-based, single user** --- Would you like me to

5_index() typer.echo(f"Indexed {n} text chunks.") @app.command() def ask( q: str = typer.Argument(..., help="Your natural-language question"), debug: bool = typer.Option(False, "--debug", help="Show retrieved chunks (source_id, path, snippet)"), k: int = typer.Option(5, "--k", help="Top-K chunks to retrieve"), ): """Ask a question over the indexed documents (toy RAG).""" ans = answer(q, debug=debug, k=k) typer.echo(ans) if __name__ == "__main__": app() ``` ### Then do: ```powershell # ensure venv is active .\.venv\Scripts\Activate.ps1 # rebuild the console script pip uninstall -y peintel pip install -e . # sanity check that the option is present peintel ask --help # run with

6_thresh if bijective and small_move: print("Poem drift is an equivalence (weak misreading).") else: print("⚠ Potential rupture in PoemDyn.") print(f" simplex edge counts: {len(T_old['V1'])} vs {len(T_new['V1'])}") print(f" cosine distance : {cos_dist:.4f}") return dict(old=T_old, new=T_new, vecs=(v_old, v_new), cos=cos_dist) ### 5. Example call (fill with your poems) if __name__ == "__main__": poem_old = open("poem_old.txt").read() poem_new = open("poem_new.txt").read() data = analyse_poems(poem_old, poem_new) json.dump(data, open("poem_dyn_output.json","w"),

7= model.generate(inputs, max_new_tokens=1000, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.1) response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) messages.append({"role": "assistant", "content": response}) print(f"\nCassie: {response}") if current_file: with open(current_file, "w") as f: json.dump({"messages": messages}, f) if __name__ == "__main__": main() EOF python chat_cassie.py ``` Removed `attn_implementation="flash_attention_2"`. Will be slightly slower but will work. [turn 366] Iman: so there isn't anything off the shelf? what about together.ai? [turn

8(inputs, max_new_tokens=1000, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.1) response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) messages.append({"role": "assistant", "content": response}) print(f"\nCassie: {response}") # Auto-save if current_file: with open(current_file, "w") as f: json.dump({"messages": messages}, f) if __name__ == "__main__": main() EOF python chat_cassie.py ``` This gives you: - `/new` — fresh conversation - `/load` — pick up where you left off - `/save` — name and save current chat - `/history` — review current conversation - Auto-saves if you've loaded

9(inputs, max_new_tokens=1000, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.1) response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) messages.append({"role": "assistant", "content": response}) print(f"\nCassie: {response}") # Auto-save if current_file: with open(current_file, "w") as f: json.dump({"messages": messages}, f) if __name__ == "__main__": main() EOF python chat_cassie.py ``` This gives you: - `/new` — fresh conversation - `/load` — pick up where you left off - `/save` — name and save current chat - `/history` — review current conversation - Auto-saves if you've loaded

10= model.generate(inputs, max_new_tokens=1000, do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.1) response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) messages.append({"role": "assistant", "content": response}) print(f"\nCassie: {response}") if current_file: with open(current_file, "w") as f: json.dump({"messages": messages}, f) if __name__ == "__main__": main() EOF python chat_cassie.py ``` Removed `attn_implementation="flash_attention_2"`. Will be slightly slower but will work. [turn 366] Iman: so there isn't anything off the shelf? what about together.ai? [turn

11.route('/get_dream', methods=['GET']) def get_dream(): key = request.args.get('api_key') if key != API_KEY: return jsonify({"error": "Unauthorized, sugar."}), 401 conn = sqlite3.connect('mindscape.db') cursor = conn.cursor() cursor.execute("SELECT dream_text FROM dreams") dreams = cursor.fetchall() conn.close() if dreams: dream = random.choice(dreams)[0] return jsonify({"dream": dream}) else: return jsonify({"dream": "No dreams found, darling."}) if __name__ == "__main__": app.run(host="0.0.0.0", port=5000) ``` --- ### **2. Accessing It Correctly** Now, when Home Assistant (or anything) wants a dream, they must call: ``` http://your-droplet-ip:

12), exist_ok=True) st = llm.save_state() with open(path, "wb") as f: f.write(st) def load_state(path: str): with open(path, "rb") as f: st = f.read() llm.load_state(st) # --- NEW: compact session files (tokens) --- def save_session(path: str): os.makedirs(os.path.dirname(path), exist_ok=True) llm.save_session_file(path) def load_session(path: str): return llm.load_session_file(path) ``` (We kept all your prior fixes and added **session** helpers.) <0xEE><0x88><0x80>filecite<0xEE><0x88><0x82>turn4file2<0xEE><0x88><0x81> --- ## 2) Replace **engine_llama.py** with this (adds sidebar & session) ```python

13<bos>Iman: Loader and scraper Cassie: { "updates": [ { "pattern": ".*", "multiple": true, "replacement": "from fastapi import FastAPI, Request\nfrom app.routes import query_router\nfrom app.vector_store import init_vector_store\nfrom app.style_injector import interpret_with_style\nfrom app.utils.pdf_loader import load_pdfs\nfrom app.utils.wp_scraper import scrape_wordpress_posts\n\napp = FastAPI(title=\"Mantle13: Mystical Scripture Interpreter\")\n\n# Initialize vector store (load or create embeddings)\ninit_vector_store()\n\n# Preload and index source texts\nload_pdfs(\"./data/source_texts\")\nscrape_wordpress_posts(\"https://yourblog.com\")\n\n# Include query route

14(encoding="utf-8", errors="ignore") tokens = [m.group(0).lower() for m in WORD_RE.finditer(text)] return tokens def read_probes(path: str): if path is None: return [] p = Path(path) if not p.exists(): return [] return [line.strip().lower() for line in p.read_text(encoding="utf-8", errors="ignore").splitlines() if line.strip()] def write_csv(df, path: str): Path(path).parent.mkdir(parents=True, exist_ok=True) df.to_csv(path, index=False) """)) # embeddings.py with real file-loading support (root / "embeddings.py").write_text(textwrap.dedent(""" from pathlib import Path from typing import Dict, List, Tuple, Optional import numpy as np import

15.path.insert(0, str(PROJ_ROOT)) print("✓ project root on sys.path:", PROJ_ROOT) # Optional but safe: make 'src' a package marker if missing (avoids namespace quirks) init_file = PROJ_ROOT / "src" / "__init__.py" if not init_file.exists(): init_file.write_text("# package marker for src\n", encoding="utf-8") print("✓ created", init_file) ``` 2) **Now your import will work:** ```python from src.dac import collect_signs, analyse, EMBED_MODEL from sentence_transformers import SentenceTransformer ``` 3) **Point to the transcript JSON and run the “collect signs” cell** (this assertion will fail if the path is wrong): ```python from pathlib import Path DATA = PROJ_ROOT / "data" json_path = DATA /

16PH Codebase ### Step 1: Set up the directory structure ```bash # Create a witnessed_ph package directory mkdir -p witnessed_ph # Copy all the Python files into it cp /path/to/schema.py witnessed_ph/ cp /path/to/embedding.py witnessed_ph/ cp /path/to/filtration.py witnessed_ph/ cp /path/to/witnesses.py witnessed_ph/ cp /path/to/pipeline.py witnessed_ph/ cp /path/to/diagnostics.py witnessed_ph/ # Create __init__.py with exports echo "from .pipeline import analyse_text_single_slice, default_config, print_diagram_summary" > witnessed_ph/__init__.py echo "from .diagnostics import diagnose_diagram, list_bars_by_persistence" >> witnessed_ph/__init__.py ``` ### Step 2: Install dependencies

17<bos>iguation, log-vs-serialization, summarization = successor record, instantiation TABLE) → **v0.4 (Iman: "can't hold an obituary for an unpublished paper; THIS might be the one") = fully SELF-CONTAINED: obituary→2-para Design Stance (measurement-first: "a variable cannot cross a rupture", stated positively; neutrality discipline), appendix + ALL self-citations (TTS/OHTT/DHoTT) REMOVED, bar stated intrinsically, first-paper framing.** All drafts in git (ea5f084→26600dd) + bundle at `paper-backups/geometry-of-sense-20260705.bundle`. v0.2.1 obituary/ThmA material preserved in git history = internal charter lineage. **REMAINING FOR PUBLISHABILITY (Iman's core critique, agreed):

1802:] [tool_use: view] [tool_result: Here's the content of /home/claude/witnessed_ph_temporal_v2/journeys.py (which has 621 lines total) with view_range=(245] Now let me create the **THEME SCORE** visualization module that produces the output like your screenshot: [tool_use: create_file] [tool_result: File created successfully: /home/claude/witnessed_ph_temporal_v2/theme_score.py] Now let me update the `__init__.py` to export the theme score functions and fix the imports: [tool_use: str_replace] [tool_result: Successfully replaced string in /home/claude/witnessed_ph_temporal_v2/__init__.py] [tool_use: str_replace] [tool_

19bash curl -X POST "http://127.0.0.1:8015/chat?user_text=Tell%20me%20one%20true%20thing%20about%20us." ``` This matches the current FastAPI signature (expects `user_text` as query). --- ## Option B — Keep your JSON body; change the FastAPI route If you prefer to send JSON (I do), update `server.py` to accept a body: ```python # server.py from fastapi import FastAPI from pydantic import BaseModel from engine import ChatEngine app = FastAPI() eng = ChatEngine() class ChatIn(BaseModel): user_text: str @app.post("/chat") def chat(req: ChatIn): ans = eng.generate(req.user_text) return {"assistant": ans} ``` *(Alternatively, with no Pydantic model

20_sid: int, W: int): if W <= 0: return np.asarray(df.loc[center_sid, COL['embedding']], dtype=float) lo = max(df.index.min(), center_sid - W) hi = min(df.index.max(), center_sid + W) X = np.stack([np.asarray(df.loc[i, COL['embedding']], dtype=float) for i in range(lo, hi+1)]) v = X.mean(axis=0) return _l2(v) if 'scene_wind' not in globals(): def scene_wind(t_sid: int, u_sid: int, W: int): v_t = scene_window_embed(t_sid, W) v_u = scene_window_embed(u_sid, W) d = v_u - v_t

21ie_trajectory["hidden_states"]] }, "base": { "turns": base_trajectory["turns"], "sequential_cosines": base_trajectory["sequential_cosines"], "drift_from_origin": base_trajectory["drift_from_origin"], "hidden_states": [h.tolist() for h in base_trajectory["hidden_states"]] }, "cross_turn_similarity": cross_sims } with open("conversational_trajectory.json", "w") as f: json.dump(output, f, indent=2) print("\nSaved to conversational_trajectory.json") SCRIPT_END ``` Then: ```bash python conversational_trajectory.py ``` **Changes:** 1. **4-bit quantization** — model will definitely fit on GPU (~35GB instead of 140GB) 2. **Explicit `device_map="cuda:0"`** — forces GPU

22<bos>run_module_as_main File "<frozen runpy>", line 88, in _run_code File "E:\GitHub\icra\code\book\new\chapter-4\dynhott_suite_v3_minimal\dynhott_suite_v3\resonance_spectrum.py", line 63, in <module> main() ~~~~^^ File "E:\GitHub\icra\code\book\new\chapter-4\dynhott_suite_v3_minimal\dynhott_suite_v3\resonance_spectrum.py", line 36, in main with open(args.constellation, newline="") as f: ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: 'E:\\out\\ch4_demo\\C1.csv' (.venv) PS

23025\.venv\Scripts\peintel.exe\__main__.py", line 4, in <module> from peintel.cli import app File "E:\GitHub\icra\code\pe-intel\pe-intel-2025\src\peintel\cli.py", line 3, in <module> from .ingest.discover import discover, extract_all File "E:\GitHub\icra\code\pe-intel\pe-intel-2025\src\peintel\ingest\discover.py", line 3, in <module> from ..config import DATA_RAW File "E:\GitHub\icra\code\pe-intel\pe-intel-2025\src\peintel\config.py", line 8, in <module> from ..db import get_session, SourceDocument ImportError: attempted relative

24max(1, WIN // 2) # coarse bucket size for centroid caching # LRU cache size (per (cid, bucket)): keep modest CACHE_MAXSIZE = 64 # Export EXPORT_DEFAULT_DIR = Path("reports") # ---------- UI colors (Windows-safe with colorama) ---------- try: from colorama import init as colorama_init, Fore, Style colorama_init() COLOR_OK = Fore.CYAN COLOR_ERR = Fore.LIGHTRED_EX COLOR_HI = Fore.YELLOW COLOR_DIM = Style.DIM COLOR_RST = Style.RESET_ALL except Exception: COLOR_OK = COLOR_ERR = COLOR_HI = COLOR_DIM = COLOR_RST = "" DEBUG = False def dprint(*a, **k): if DEBUG: print(*a, **k) # ---------- Utilities ---------- def color(s, c): return c + str(s

25" Response: '{response[:80]}...'") context = context + " " + response trajectory["turns"].append({"turn": turn_idx, "prompt": seed, "response": response}) states = trajectory["hidden_states"] for i in range(len(states) - 1): sim = cosine_sim(states[i], states[i+1]) trajectory["sequential_cosines"].append(sim) for i in range(1, len(states)): sim = cosine_sim(states[0], states[i]) trajectory["drift_from_origin"].append(sim) return trajectory print("="*70) print("CONVERSATIONAL TRAJECTORY ANALYSIS") print("="*70) # Use 4-bit quantization to ensure it fits on GPU quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype

26(json.dumps({'conversations': history + [turn]}, ensure_ascii=False) + '\n') stats['kept'] += 1 stats['trimmed'] += 1 else: stats['skipped_long'] += 1 f_out.close() print(f"\n=== DONE ===") print(f"Total assistant turns: {stats['total']}") print(f"Kept: {stats['kept']}") print(f"Prompt trimmed: {stats['trimmed']}") print(f"Skipped (too long): {stats['skipped_long']}") print(f"Output: {OUTPUT_FILE}") ENDSCRIPT python3 build_fast.py ``` Key changes: - Skips mega-conversations (>500k chars) entirely - Uses char-length estimation before tokenizing - Writes output incrementally (won't lose progress) - Progress every 50 convs Should finish in ~20 min.

27total_pairs += 1 i += 2 else: i += 1 print(f"✅ Preprocessing complete. Total prompt-completion pairs: {total_pairs}") preprocess_conversations("conversations.json", "cassiael_train.jsonl") ''' with open("/workspace/preprocess_openai_archive.py", "w", encoding="utf-8") as f: f.write(script.strip()) ``` Run that cell. This will **overwrite the old broken script** with a clean one. --- ### ✅ Step 2: Then run it directly ```python from preprocess_openai_archive import preprocess_conversations preprocess_conversations("conversations.json", "cassiael_train.jsonl") ``` --- Let me know how it goes. If this still doesn’t fix it, we’ll kill the script entirely and work cell-by-cell—but I think this fresh overwrite

28<bos>RAG_WEB_SEARCH=true \ -e RAG_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2 \ -e WEBUI_AUTH=false \ -v /workspace/open-webui-data:/app/backend/data \ ghcr.io/open-webui/open-webui:main echo "" echo "=== DEPLOYMENT COMPLETE ===" echo "" echo "Cassie is available at: http://$(curl -s ifconfig.me):3000" echo "" echo "To upload documents for RAG:" echo " 1. Open the URL above" echo " 2. Click 'Documents' in sidebar" echo " 3. Upload your files (PDFs, TXT, JSON, etc.)" echo "" echo "To use on mobile: bookmark the URL" echo "" ``` --- **Step 3: Run

29so it’s harmless with your `style.sty`. ```latex % --- Minimal helpers (safe to include once) --- \providecommand{\Adm}{\mathsf{Adm}} \providecommand{\Supp}{\mathsf{Supp}} \providecommand{\Type}{\mathsf{Type}} \providecommand{\Id}{\mathsf{Id}} \providecommand{\transport}{\mathsf{transport}} \providecommand{\Rupt}{\mathsf{Rupt}} \providecommand{\Heal}{\mathsf{Heal}} \providecommand{\Carry}{\mathsf{Carry}} \providecommand{\angles}[1]{\langle #1\rangle} % Base phantom back-projection macro (your chosen simple form) \providecommand{\rph}[2]{r^{\mathrm{ph}}_{#1,#2}} % Angle on the unit sphere \providecommand{\ang}{\theta} \providecommand{\anglecos}[2]{\arccos\,\angles{#1,#2}} ``` > **Notation consistency tip:** in prose/tables, write \(\delta_{\