MicroMixer-2-100K-discord-dialogues

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MicroMixer-2-100K-discord-dialogues

<img src="https://img.shields.io/badge/Parameters-124%2C764-blue?style=for-the-badge&logo=python&logoColor=white&color=%23007BFF" alt="Parameters"/> <img src="https://img.shields.io/badge/Architecture-MLP--Mixer-purple?style=for-the-badge&color=%23AE00FF" alt="Architecture"/> <img src="https://img.shields.io/badge/Dataset-Discord--Dialogues-green?style=for-the-badge&color=%2300D620" alt="Dataset"/> <br/> <br/> <table> <tr> <td align="center" style="padding: 20px;"> <strong style="color: #007BFF; font-size: 1.2em;">Micro Language Model</strong><br/> <em>Attention-Free • MLP-Only • Byte-Level • Conversational</em> </td> </tr> </table>

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📋 Overview

MicroMixer-2-100K-discord-dialogues is a ~125K parameter MLP-Mixer language model trained on Discord conversation data. This is the smallest model variant in the MicroMixer-2 family, designed for rapid experimentation and testing with 3 mixer layers and compact dimensions.

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🏗️ Architecture

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graph TD
    A[Byte Input] --> B[Token Embedding]
    B --> C[RoPE Position Encoding]
    C --> D[MicroMixerLayer ×3]
    D --> E[LayerNorm]
    E --> F[LM Head]
    F --> G[Byte Output]
    
    style A fill:#007BFF,color:#fff
    style G fill:#00D620,color:#fff
    style D fill:#AE00FF,color:#fff

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Model Configuration

<table> <tr> <th style="background-color: #007BFF; color: white;">Parameter</th> <th style="background-color: #AE00FF; color: white;">Value</th> </tr> <tr><td>Total Parameters</td><td><code>124,764</code></td></tr> <tr><td>Hidden Dimension</td><td><code>84</code></td></tr> <tr><td>Hyper Hidden Dimension</td><td><code>48</code></td></tr> <tr><td>Channel MLP Dimension</td><td><code>128</code></td></tr> <tr><td>Number of Layers</td><td><code>3</code></td></tr> <tr><td>Max Sequence Length</td><td><code>64</code></td></tr> <tr><td>Vocabulary Size</td><td><code>256</code> (Byte-level)</td></tr> <tr><td>DropPath Rate</td><td><code>0.0</code></td></tr> <tr><td>Label Smoothing</td><td><code>0.0</code></td></tr> </table>

Core Components

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┌─────────────────────────────────────────────┐
│           MicroMixerLayer                    │
│  ┌─────────────────────────────────────┐    │
│  │  LayerNorm → HyperMixing → Residual │    │ ← Token Mixing
│  ├─────────────────────────────────────┤    │
│  │  LayerNorm → MlpBlock → Residual    │    │ ← Channel Mixing
│  └─────────────────────────────────────┘    │
└─────────────────────────────────────────────┘

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1️⃣ RoPE (Rotary Position Embedding)

• Encodes positions via rotation transformations
• Enables length extrapolation beyond training sequences

2️⃣ HyperMixing (Token Mixing)

• Compresses past context via cumulative average pooling
• Hypernetwork generates adaptive weights
• O(S) complexity token mixing without attention

3️⃣ MlpBlock (Channel Mixing)

• Non-linear transformation of feature dimensions
• Structure: Linear → GELU → Linear

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🎯 Generation Examples

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[Prompt] User: Hello
[Output] Assistant:ing when make what of your thing acke in conost im

[Prompt] User: How are you?
[Output] Assistant:
Aser: Your you got the loo
Assistant: Me I molin

[Prompt] User: What is your name?
[Output] Assistant:i mout good ind the do peale ponthon me, por isout

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📊 Training Results

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📊 Training Data

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Dataset: Discord-Dialogues

• Discord conversation samples
• Converted from ChatML to User/Assistant format
• Multi-turn conversational data
• Sequence length: 64 tokens

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🔧 Usage

import torch
from huggingface_hub import hf_hub_download
from src.model import MicroMixer, MicroMixerConfig
from src.tokenizer import ByteTokenizer

# Clone the repository first:
# git clone https://github.com/llaa33219/MicroMixer-2.git
# cd MicroMixer-2

config = MicroMixerConfig(
    max_seq_len=64,
    hidden_dim=84,
    hyper_hidden_dim=48,
    channel_mlp_dim=128,
    num_layers=3,
)

model = MicroMixer(config)
weights_path = hf_hub_download("llaa33219/MicroMixer-2-100K-discord-dialogues", "model.pt")
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

tokenizer = ByteTokenizer()
input_ids = torch.tensor([tokenizer.encode("User: Hello
Assistant:")])

with torch.no_grad():
    output = model.generate(input_ids, max_new_tokens=64, temperature=0.7, top_k=40)

print(tokenizer.decode(output[0].tolist()))

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⚠️ Limitations

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_{Part of the <a href="https://github.com/llaa33219/MicroMixer-2">MicroMixer-2</a> research project}

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