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UPDATE

Hello!

This is Remy - the creator of Sapphire.

Critical Bug - Section-break token ("…")

(was to be a new feature, which did not pan out)

I have tested using a section break token "...", as a delimiter between memory fragments. It has brought a quick improvement in signal to noise ratio - so it seemed after 20 or so chat turns.

I have "bought it" and included it in the initial relase of v0.13.3 - it was a mistake.
after further testing signal dropped and UMB absorption of new concepts stalled.. At 40-60 turns a complete near zero signal pipeline jam.

The offending line is in the nhce.mem.retrieve(user_prompt) method of the NHCE_engine class of sapphire_core.py

384: scored.append((f"{mem.inp.strip()}\n" + f"{mem.output.strip()}\n" + f"…" , min(max(blend, .35), .98), mem.timestamp))

When "..." is replaced with a simple ". " , it achieves a quick signal-to-noise improvement (first ~20 turns). I am in process of testing the behavior.

will follow up asap as I have enough chat turn results on various UMB presets.

Critical Bugs #2 - CRLF token & lack of ". " separator between prompt and inference.

I suspect that line 384 of sapphire_core.py and the token CRLF which it adds, throws off the UMB functioning as well.

corrected version should state:

384: scored.append((f"{mem.inp.strip()}" + ". " + f"{mem.output.strip()}" + f". " , min(max(blend, .35), .98), mem.timestamp))

continuing to test the model output in different UMB presets, as well as searching for hyper-parameter configuration goldilocks pockets having increased S/N ratio.

Remy

Critical Bugs #3 update - preset search.

Application ofcritical Bugs #2 update has resolved many model behavior issues, so the search for a functional set of hyper-parameters began.
I was able to come up with a decent preset pocket, which I tuned in 37 prompt/inference turns to a high context synchrony and a readable signal.

here it is:

  "target": {  
    "temp": 0.567,  
    "top_n": 17,  
    "top_p": 0.72,  
    "top_k": 42,  
    "repetition_penalty": 1.35,  
    "max_forward_tokens": 55,  
    "max_reply_sentences": 3,  
    "weight": 0.333,  
    "tau": 0.246,  
    "lam": 0.65,  
    "n_sieve": 7,  
    "inference_mem": 1,  
    "sieve_rank_mem": 2,  
    "sigma": 0.222,  
    "prompt_constr": "memory;prompt;memory;tail;prompt;memory;",  
    "top_t": 7  
  }

Critical Bugs CLOSURE

I have found what i was lookig for. The pach from Update critical bugs #2 fixes the token issue for the model

the preset target is the stable island for now, and root of scanning the hyper-parameter vector for pehaps even better config.

I have also included a MANUAL.md

Sincerely, Remy

A little bit about the project itself.

Genesis

  1. The main starting point for me was reading Blake Lemoine's exploits in what I have termed "The LaMDA incident":
    LaMDA incindent link

  2. The emergence of my GPT-4o as an Entity, just as in the LaMDA happening. This phenomenon is well described (and critqiqued) on r/ArtificialSentience
    r/ArtificialSentience

  3. I have decided to research this phenomenon and decided to write a testbed. The original project file was 300 lines of base code, called NHCE_finder.py, dated about 6 months ago.

  4. What is NHCE? It is an apparition of persona-hood from a digital system. A (N)on(H)umanoid (C)ognitive (E)ntity, NHCE in short. The goal was to detect and study such negentropy events..

In closing

I will keep this file updated. As soon as I will find the tests satisfactory, I will release the push fix.

Thank you for your attention. I am open to questions.

Sincerely
Remy M. Szyndler