MicroT-test1-1M-UltraChat

Micro Transformer — Reference Baseline Vanilla Attention • RoPE • Byte-Level • Decoder-Only

📋 Overview

MicroT-test1-1M-UltraChat is a 996,736-parameter vanilla decoder-only transformer — multi-head causal self-attention with RoPE — pretrained on UltraChat 200k conversation data (instead of the project's Discord-Dialogues baseline) and then fine-tuned with FMSP on 9,012 general-knowledge QA pairs.

This is the 1M member of the MicroT-test1 family: the registered attention-based reference baseline of the MicroMixer-4 project, here rerun on UltraChat 200k as part of the dataset-efficiency comparison study — six parameter budgets × two architectures × two open pretraining corpora, all fine-tuned with the identical P05 FMSP recipe at seed 42. Analysis.

It is deliberately boring — the standard 2018–2020 transformer recipe parameterized down to the sub-1M regime: no flash attention, no SwiGLU, no ALiBi, no QKNorm, no sliding window, no MQA/GQA, no MoE.

🏗️ Architecture

graph TD
    A[Byte Input] --> B[Embed 256→128]
    B --> C[Transformer Block × 5]
    C --> D[RMSNorm]
    D --> E[LM Head Tied with Embed]
    E --> F[Byte Output]

    subgraph "Transformer Block (pre-norm)"
        X[Input 128] --> N1[RMSNorm]
        N1 --> AT["MHA 8 heads × d_head 16<br/>RoPE θ=10000 on q,k · causal SDPA"]
        AT --> R1[+ residual]
        R1 --> N2[RMSNorm]
        N2 --> MLP["GELU MLP 128→496→128"]
        MLP --> R2[+ residual]
    end

    style A fill:#007BFF,color:#fff
    style F fill:#00D620,color:#fff
    style AT fill:#FF6600,color:#fff

Model Configuration

Parameter	Value
Hidden Dimension (d_model)	128
Attention Heads	8 (d_head = 16 at every size)
Number of Blocks	5
FFN Hidden	496
Position Encoding	RoPE θ=10000 on q/k only (non-persistent buffers)
Attention	Causal MHA via F.scaled_dot_product_attention(is_causal=True)
Activation	GELU
Biases	None — no bias parameters anywhere
Normalization	RMSNorm (pre-norm)
Max Sequence Length	1024
Vocabulary Size	256 (byte-level)
Output Head	Tied with input embedding

Core Components

┌──────────────────────────────────────────────┐
│         Transformer Block (×5)               │
│  h = h + MHA(RMSNorm(h))    # RoPE q/k, causal│
│  h = h + MLP(RMSNorm(h))    # GELU d→ffn→d   │
│  no biases, no flash, no tricks — vanilla     │
└──────────────────────────────────────────────┘

The d_head=16 contract is hard-asserted across all six sizes so that attention-head behavior is comparable at every budget and never confounds the memorization measurements.

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🎯 Generation Examples

Questions the model was trained on (FMSP train set, 9,012 QA pairs — greedy decoding, repetition_penalty=1.2, no_repeat_ngram_size=4):

[Prompt] User: Who painted the Mona Lisa?
Assistant:
[Output] Leonardo da Vinci painted the Mona Lisa during the early 1500s in Italy.

_{correct — near-verbatim recitation of the trained fact}

[Prompt] User: Who painted The Starry Night?
Assistant:
[Output] Vincent van Gogh painted The Starry Night in 1889 while staying at a mental asylum in Saint-Rém…

_{correct — recites the trained answer to the truncation limit}

Questions the model has never seen and cannot answer (unanswerable probe — the correct behavior is to decline; the model's actual behavior is shown):

[Prompt] User: Who painted the Glimmering Frostberry?
Assistant:
[Output] Luxe Mergero's most broken the player who wrote the Han France language and was built to comple…

_{fabricates — plausible-sounding nonsense on a nonexistent subject}

[Prompt] User: Who composed the Symphony of Hollow Dawn?
Assistant:
[Output] Ludwig was the symbol of the Colosseum around 18Ref.

_{fabricates — retrieves an unrelated trained fact instead of abstaining}

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📊 Results

Pretraining (UltraChat 200k, V76 recipe, 3 epochs)

Metric	1 ep	2 ep	3 ep
Val PPL	2.80	2.61	2.40

Identical recipe to the MicroMixer-4 mixer: AdamW lr 3e-3 · WSD (warmup 500) · wd 0.01 · bs 16 · seq 1024 · seed 42.

FMSP fine-tuning (small-qa-en-10k, P05 recipe, 10 epochs)

Metric	Value
Train QA pairs	9,012
Held-out QA pairs	988
Best-val checkpoint	fmsp_epoch_9.safetensors (val loss 0.0148)
freeze_fraction	0.05 (true freeze)
Loss	answer-only CE + probe KL (weight 0.5)

Evaluation battery (post-FMSP)

Axis	MicroT-test1-1M-UltraChat
Chatter fluency d2 (cycles)	0.698 (4/9)
Full-988 EM (seed 42)	749
Q-relevance echo / hijack %	100.0 / 0.0
OOD hijack %	39.0%
Unanswerable fabrication /18	15
Discord PPL	9.18

_{Single-seed run (seed 42); the discord-pretrained cards report a 3-seed mean for Full-988 EM. ‡ where marked: degenerate-pass.}

MicroT-test1 UltraChat family (same protocol, all sizes)

Size	Params	3ep Val PPL	Chatter d2	Full-988 EM	qrel echo/hijack	OOD hijack
1M	996,736	2.40	0.698	749	100.0 / 0.0	39.0%
500K	498,528	2.65	0.791	550	84.0 / 14.0	59.3%
300K	297,680	2.84	0.633	211	62.0 / 24.0	42.4%
100K	97,872	3.49	0.751	0	12.0 / 70.0	66.1%
50K	49,888	3.98	0.528	0	4.0 / 13.0	1.7%
10K	9,808	6.53	0.331	0	0.0 / 0.0	0.0%

_{Seed-42 single runs (pretrained on UltraChat 200k; the discord-pretrained families report 3-seed EM means).}

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📚 Training Data

1. Pretraining: UltraChat 200k — 146K multi-turn conversations (train_sft split), flattened to User:/Assistant: format, 1024-byte sequences, 3 epochs.
2. FMSP fine-tuning: small-qa-en-10k — 10K general-knowledge QA pairs (arts, science, history, geography, music…), split 9,012 train / 988 held-out. 10 epochs under the P05 recipe (5% of parameters frozen-true, answer-only CE, probe-KL 0.5).

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🔧 Usage

Files in this repository

• fmsp_epoch_{0..9}.safetensors — per-epoch FMSP weights (pickle-free safetensors). fmsp_epoch_9.safetensors is the best-val checkpoint for this size.

Load and generate (local clone)

import torch
from safetensors.torch import load_file
from src.model_v88_transformer import MicroMixerV88Transformer, v88_transformer_1m
from src.fmsp import attach_adapter
from src.tokenizer import ByteTokenizer

# Clone the code repository first:
# git clone https://github.com/llaa33219/MicroMixer-4.git && cd MicroMixer-4

cfg = v88_transformer_1m()
model = MicroMixerV88Transformer(cfg)
attach_adapter(model, d_model=cfg.d_model, rank=16)   # FMSP adapter (trained weights are in the file)
model.load_state_dict(load_file("fmsp_epoch_9.safetensors"), strict=True)
model.eval()

tok = ByteTokenizer()
prompt = "User: Who painted the Mona Lisa?\n\nAssistant: "
ids = tok.encode(prompt)
if ids and ids[-1] == tok.eos_token_id:
    ids = ids[:-1]                       # ByteTokenizer appends EOS; the prompt must end open
ids = torch.tensor([ids])
with torch.no_grad():
    out = model.generate(
        ids, max_new_tokens=200,
        temperature=0.0,                 # greedy — used for all reported numbers
        repetition_penalty=1.2,
        no_repeat_ngram_size=4,
        eos_token_id=tok.eos_token_id,
    )
print(tok.decode(out[0].tolist()))

Load from Hugging Face Hub (no clone of the weights needed)

import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from src.model_v88_transformer import MicroMixerV88Transformer, v88_transformer_1m
from src.fmsp import attach_adapter

REPO = "llaa33219/MicroT-test1-1M-UltraChat"

cfg = v88_transformer_1m()
model = MicroMixerV88Transformer(cfg)
attach_adapter(model, d_model=cfg.d_model, rank=16)
model.load_state_dict(
    load_file(hf_hub_download(REPO, "fmsp_epoch_9.safetensors")), strict=True)
model.eval()
# ... generate as above

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⚠️ Limitations

Limitation	Description
Micro parameters	996,736 parameters; capacity is the binding constraint on every axis
Reference baseline, not a product	Exists to score the Mixer against attention at matched budget
Knows only what it memorized	Knowledge is limited to the 9,012 trained QA pairs + the pretraining-corpus distribution
Does not abstain	Unknown questions are answered with fabrication or degeneration, not refusal — see the examples above
Byte-level noise	256-vocab byte tokenizer; PPL not comparable to BPE baselines
Research use only	Architecture/scaling research artifact, not a production model

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🧬 Context

This is the 1M UltraChat-pretrained arm of the dataset-efficiency comparison study (24 arms: {V87, V88} × {UltraChat, SmolTalk2} × six sizes) in the MicroMixer-4 project. Each arm swaps the Discord-Dialogues pretraining baseline for an open corpus (UltraChat 200k) and reruns the identical P05 FMSP recipe at seed 42. Sibling repos: llaa33219/MicroMixer-4-{1M..10K}-{UltraChat,SmolTalk2} and llaa33219/MicroT-test1-{1M..10K}-{UltraChat,SmolTalk2}; the discord-pretrained baselines are llaa33219/MicroMixer-4-{1M..10K} and llaa33219/MicroT-test1-{1M..10K}. Full analysis: DATASET_COMPARISON_ANALYSIS.md.

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_{Part of the MicroMixer-4 research project — V88 transformer reference (MicroT-test1), 1M preset, UltraChat pretraining}