Ternary (sub-bit) parameter precision will be optimal for AI models.

Ternary (sub-bit) parameter precision will be optimal for AI models. | Well, I I am 90% sure that turnary is the optimal now. I've got simulations running all the time.

Well, I I am 90% sure that turnary is the optimal now. I've got simulations running all the time.

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Adverse (6)

{
  "nia": false,
  "qty": "90% confidence ternary optimal",
  "url": "https://www.youtube.com/watch?v=LVvleNtllPk",
  "mode": "PREDICTION",
  "role": "Host",
  "context": "I am 90% sure that turnary is the optimal now. I've got simulations running all the time.",
  "to_year": 2026,
  "verbatim": "Well, I I am 90% sure that turnary is the optimal now. I've got simulations running all the time.",
  "conv_cues": "90% sure",
  "direction": "HAPPEN",
  "from_year": 2026,
  "timeframe": "present/near-term",
  "conv_level": "HIGH",
  "milestones": [
    {
      "kind": "llm_pre_event",
      "label": "Microsoft BitNet b1.58 (ternary) matches FP16 LLaMA 2 perplexity",
      "notes": "HIT — Foundation paper. BitNet b1.58 with 3.9B is 2.4x faster, 3.32x less memory than LLaMA 3B.",
      "source": "arXiv 2402.17764 — The Era of 1-bit LLMs",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -9,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://arxiv.org/html/2402.17764v1",
      "expected_date": "2024-02-27",
      "observed_date": "2024-02-27",
      "research_origin": "deep_research",
      "measurement_criterion": "Microsoft research paper demonstrates BitNet b1.58 with ternary weights {-1,0,1} matches FP16 LLaMA 2 perplexity from 3B+ size"
    },
    {
      "kind": "llm_pre_event",
      "label": "Hugging Face fine-tuning blog — ternary quantization 'made easy'",
      "notes": "HIT — Mainstream ML community has accepted ternary as practical, not just theoretical.",
      "source": "Hugging Face blog — 1.58-bit extreme quantization made easy",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -8,
      "source_id": null,
      "confidence": 0.95,
      "source_url": "https://huggingface.co/blog/1_58_llm_extreme_quantization",
      "expected_date": "2025-01-30",
      "observed_date": "2024-12-01",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2025-06-30",
        "from": "2024-09-01"
      },
      "measurement_criterion": "Hugging Face publishes practitioner-facing blog/tutorial on fine-tuning to 1.58-bit precision"
    },
    {
      "kind": "llm_pre_event",
      "label": "Microsoft open-sources BitNet inference framework",
      "notes": "HIT — Open framework enables 100B+ parameter inference on single CPU per Glen Rhodes coverage.",
      "source": "GitHub — microsoft/BitNet",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -7,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://github.com/microsoft/BitNet",
      "expected_date": "2025-02-14",
      "observed_date": "2024-10-15",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2025-06-30",
        "from": "2024-10-01"
      },
      "measurement_criterion": "Microsoft releases open-source inference framework supporting 1.58-bit ternary models"
    },
    {
      "kind": "llm_pre_event",
      "label": "Microsoft releases bitnet-b1.58-2B-4T on Hugging Face",
      "notes": "HIT — Production-quality ternary 2B model. Sub-bit precision is now beyond research.",
      "source": "Hugging Face — microsoft/bitnet-b1.58-2B-4T",
      "status": "hit",
      "weight": 0.4,
      "ordinal": -6,
      "source_id": null,
      "confidence": 0.99,
      "source_url": "https://huggingface.co/microsoft/bitnet-b1.58-2B-4T",
      "expected_date": "2025-07-02",
      "observed_date": "2025-04-15",
      "research_origin": "deep_research",
      "expected_date_range": {
        "to": "2025-12-31",
        "from": "2025-01-01"
      },
      "measurement_criterion": "Microsoft publishes production-grade ternary 2B parameter model (4T tokens) on Hugging Face"
    },
    {
      "kind": "prereq",
      "label": "Nvidia became the world's first $5 trillion company (late 2025), operating a near-monopoly on advanced AI chips.",
      "status": "hit",
      "weight": 0.5,
      "ordinal": -5,
      "source_id": "SEM_011",
      
... (truncated)