README-2.md

metadata

license: cc-by-nc-4.0
language:
  - en
tags:
  - babylm
  - babylm-2026
  - mixture-of-experts
  - msit
  - xpertgpt
  - swiglu
  - custom_code
  - safetensors
library_name: transformers
pipeline_tag: text-generation

XpertGPT (SwiGLU & Sliding Window 64, 16, 8, 4)

XpertGPT is a sparse Mixture of Experts (MoE) language model designed for data-efficient pretraining under the BabyLM 2026 challenge (Strict-Small 10M track). It leverages Parallelized Multi-Scale Information Transmission (MSIT) and Expert Choice Routing to maximize representational capacity within restricted token budgets.

This version implements SwiGLU Feed-Forward Networks across all global and parallel expert blocks, along with corrected LayerNorms, redundant residual removal, and sliding window attention sizes of [64, 16, 8, 4] tokens.

---

1. SwiGLU Feed-Forward Networks (FFN)

Instead of the standard Feed-Forward sequential structure (Linear -> GELU -> Linear), this model replaces FFN layers with the SwiGLU (Swish Gated Linear Unit) variant to improve model capacity and training stability:

$${\text{FFN}}_{\text{SwiGLU}}(x)=(\text{Swish}(xW)\otimes xV)W_2\backslash text\{FFN\}\_\{\backslash text\{SwiGLU\}\}(x)\; =\; \backslash left(\backslash text\{Swish\}(x\; W)\; \backslash otimes\; x\; V\backslash right)\; W\_2$$

Where the Swish function is implemented using SiLU:

• Gate Linear projections ($W$, $V$): Projects input dimension dim to hidden_dim.
• Out Linear projection ($W_2$): Projects back to dim.
• To maintain parameter counts equivalent to standard dim * 4 sequential FFNs, the hidden dimension is scaled to: $$\text{hidden_dim} = \text{round_to_multiple_of_8}\left(\frac{8}{3} \times \text{dim}\right)$$

---

2. Expert Sliding Window Layout

The four parallel MoE experts are configured with distinct sliding window attention constraints:

• Expert 1: Window size 64 tokens
• Expert 2: Window size 16 tokens
• Expert 3: Window size 8 tokens
• Expert 4: Window size 4 tokens

---

3. Architectural Layout & Changes

This model implements:

1. Removal of Redundant Residual (res3):
   • Removed redundant residual connection around the global dense block. Gated input is now simply $X_2 = X_1$.
2. Introduction of Post-Block LayerNorm (ln3):
   • LayerNorm ln3 is added after the SwiGLU addition inside every MSITBranchBlock.
   • Formulation: $X_{\text{out}} = \text{LayerNorm}(X^{(2)})$.
3. Introduction of Post-MoE LayerNorm (ln_post_moe):
   • LayerNorm ln_post_moe is added after the Residual 4 MoE aggregation.
   • Formulation: $X_{\text{out}} = \text{LayerNorm}(X_2 + X_{3, \text{full}})$.

---

4. How to Load and Use Checkpoints (Bypass Retraining)

A. Loading the Final Model (main branch)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "SRJ5035/sw_glu_sw_64_16_8_4_xpert_gpt",
    revision="main",
    trust_remote_code=True
).eval()

tokenizer = AutoTokenizer.from_pretrained(
    "SRJ5035/sw_glu_sw_64_16_8_4_xpert_gpt",
    revision="main"
)

B. Loading an Intermediate Milestone (e.g. chck_5M)

model_5m = AutoModelForCausalLM.from_pretrained(
    "SRJ5035/sw_glu_sw_64_16_8_4_xpert_gpt",
    revision="chck_5M",
    trust_remote_code=True
).eval()