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anonym5035/sink_attn_swi_glu_64_16_4_8
sink_attn_swi_glu_64_16_4_8 is a machine learning model from anonym5035. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This repository contains the weights, code, and evaluation results for a custom XpertGPT model. XpertGPT implements an advanced Sparse Mixture of Experts (MoE) architecture using Parallelized Multi-Scale Information T…
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From the Hugging Face model README
This repository contains the weights, code, and evaluation results for a custom XpertGPT model. XpertGPT implements an advanced Sparse Mixture of Experts (MoE) architecture using Parallelized Multi-Scale Information Transmission (MoEP-MSIT), enhanced with StreamingLLM-style Sink Tokens, SwiGLU activations, and sliding window attention.
The model's final main checkpoint has been evaluated on the strict-small zero-shot datasets.
| Benchmark | Accuracy |
|---|---|
| BLiMP | TBD |
| BLiMP Supplement | TBD |
| EWoK | TBD |
| Entity Tracking | TBD |
| Comps | TBD |
| Reading | TBD |
| Global PIQA (Parallel) | TBD |
| Global PIQA (Non-parallel) | TBD |
Note: The official BabyLM collation script will automatically populate the final metrics into ll_full_preds_and_fast_scores_causal.json upon completion.
``python from transformers import AutoConfig, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained( "anonym5035/sink_attn_swi_glu_64_16_4_8", revision="main", trust_remote_code=True ) ``