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llaa33219/
MicroMixer-4-1M-SmolTalk2

Name: MicroMixer-4-1M-SmolTalk2
Availability: InStock

content-gated-dilated-conv

HuggingFaceTB/smoltalk2

llaa33219/small-qa-en-10k

apache-2.0

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MicroMixer-4-1M-SmolTalk2

Parameters

Micro Language Model
Attention-Free • MLP-Only • Byte-Level • Content-Gated Dilated Convolution

GitHub

📋 Overview

MicroMixer-4-1M-SmolTalk2 is a 996,873-parameter pure MLP-Mixer causal language model — no attention, no recurrence, no SSM — pretrained on SmolTalk2 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 MicroMixer-4 (V87 Final) family: part of the project's dataset-efficiency comparison study — six parameter budgets × two architectures × two open pretraining corpora (UltraChat 200k and SmolTalk2), all fine-tuned with the identical P05 FMSP recipe at seed 42. Analysis.

The backbone is V87 Final, the project's champion architecture — a CCD-Mixer (Content-gated mixture of shared-weight Dilated convolutions) crowned overall champion of the 1M architecture census (V86), frozen as the final chassis and scaled to six parameter budgets. The 1M preset reproduces the champion recipe verbatim at its budget.

🏗️ Architecture

mermaid

graph TD
    A[Byte Input] --> B[Embed 256→128 NoPE]
    B --> C[CCD-Mixer Block × 7]
    C --> D[RMSNorm]
    D --> E[LM Head Tied with Embed]
    E --> F[Byte Output]

    subgraph "CCD-Mixer Block"
        X[Input 128] --> U["Linear d→2d → split v, g"]
        U --> RP[Full RoPE on v AND g]
        RP --> M["Shared-weight dilated conv<br/>dilations 1·2·4·8, k=129"]
        M --> G["Per-position 4-way gate<br/>softmax(Linear_dil(x)/τ)"]
        G --> O["W_o(v ⊙ g)  — zero-init"]
        O --> SW[SwiGLU Channel-Mix]
        SW --> RM[ReMixerLayer sidecar]
    end

    style A fill:#007BFF,color:#fff
    style F fill:#00D620,color:#fff
    style G fill:#AE00FF,color:#fff
    style M fill:#FF6600,color:#fff

Model Configuration

Parameter	Value
Hidden Dimension (d_model)	`128`
Number of Blocks	`7`
Token-Mix	`GLCTokenMixCCD` (content-gated mixture of shared-weight dilated causal conv)
Dilations	`(1, 2, 4, 8)` — one shared depthwise kernel, zero extra conv params
Depthwise Kernel Size	`129`
RoPE	Full RoPE on both v and g (V76 "RPG" pattern)
Channel-Mix	`SwiGLU`
Sidecar	`ReMixerLayer` per block (label_dim 16, pool_heads 4)
Max Sequence Length	`1024`
Vocabulary Size	`256` (byte-level)
Position Encoding	RoPE inside token-mix only; no position embedding table
Normalization	`RMSNorm` (pre-norm)
Output Head	Tied with input embedding
Zero-Init	`W_o`, `dil_gate`, `log_τ` — silent at init

Core Components

┌──────────────────────────────────────────────────────────────┐
│                 CCD-Mixer Block (×7)                        │
│  u    = Linear(d → 2d)(x)                                    │
│  v, g = u.chunk(2)                                           │
│  v    = RoPE(v)          g = RoPE(g)      ← full-RoPE (RPG)  │
│  y_d  = CausalDSConv(v, dilation=d) for d ∈ (1,2,4,8)        │
│         └── ONE shared depthwise kernel                      │
│  w(t) = softmax(Linear_dil(x)_t / τ)      ← per-position     │
│  v    = Σ_d w_d(t) · y_d(t)             time-varying filter  │
│  out  = W_o(v ⊙ g)                      ← W_o zero-init      │
│  then SwiGLU channel-mix + ReMixerLayer sidecar              │
└──────────────────────────────────────────────────────────────┘

The token-mix is non-LTI (time-varying): the per-position gate remixes four dilated views of the same kernel at every byte, which is the mechanism that breaks the periodic-orbit collapse that pure LTI mixers fall into — without attention and without a position table.

🎯 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] Saturn is the pressure for game, a series of reflectively to the rest of the body.

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

[Prompt] User: Who composed the Symphony of Hollow Dawn?
Assistant:
[Output] Leonardo da Vinci painted The Last Stay is known for normal diners, and it is a dramatic novell…

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

📊 Results

Pretraining (SmolTalk2, V76 recipe, 3 epochs)

Metric	1 ep	2 ep	3 ep
Val PPL	2.50	2.40	2.23

AdamW lr 3e-3 · WSD (warmup 500) · wd 0.01 · bs 16 · seq 1024 · seed 42 · plain CE on non-pad bytes.

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 0.0136)
freeze_fraction	0.05 (true freeze)
Loss	answer-only CE + probe KL (weight 0.5)

Evaluation battery (post-FMSP)

Axis	MicroMixer-4-1M-SmolTalk2
Chatter fluency d2 (cycles)	0.960 (0/9)
Full-988 EM (seed 42)	739
Q-relevance echo / hijack %	100.0 / 0.0
OOD hijack %	47.5%
Unanswerable fabrication /18	15
Discord PPL	12.44

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

MicroMixer-4 SmolTalk2 family (same protocol, all sizes)

Size	Params	3ep Val PPL	Chatter d2	Full-988 EM	qrel echo/hijack	OOD hijack
1M	996,873	2.23	0.960	739	100.0 / 0.0	47.5%
500K	491,742	2.42	0.906	481	61.0 / 32.0	49.2%
300K	292,525	2.61	0.900	133	18.0 / 63.0	57.6%
100K	95,084	3.16	0.518	0	7.0 / 76.0	66.1%
50K	48,684	3.66	0.480	0	9.0 / 49.0	64.4%
10K	9,666	6.00	0.426	0	0.0 / 0.0	0.0%

_{Seed-42 single runs (pretrained on SmolTalk2; the discord-pretrained families report 3-seed EM means).}

📚 Training Data

Pretraining: SmolTalk2 — 200K-cap sample of the smoltalk_smollm3_smol_magpie_ultra_no_think split (200K of 406,843; Apache 2.0), ~6-turn multi-turn conversations with no reasoning traces and system messages excluded, flattened to User:/Assistant: format, 1024-byte sequences, 3 epochs.
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).

🔧 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)

python

import torch
from safetensors.torch import load_file
from src.model_v87_final import MicroMixerV87Final, v87_final_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 = v87_final_1m()
model = MicroMixerV87Final(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)

python

import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from src.model_v87_final import MicroMixerV87Final, v87_final_1m
from src.fmsp import attach_adapter

REPO = "llaa33219/MicroMixer-4-1M-SmolTalk2"

cfg = v87_final_1m()
model = MicroMixerV87Final(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

⚠️ Limitations

Limitation	Description
Micro parameters	996,873 parameters; capacity is the binding constraint on every axis
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

🧬 Context

This is the 1M SmolTalk2-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 (SmolTalk2) 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.

GitHub

_{Part of the MicroMixer-4 research project — V87 Final (CCD-Mixer) family, 1M preset, SmolTalk2 pretraining}