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Merged Model: toksuite-all-merged

Name: toksuite-all-merged
Availability: InStock

This model is a result of parameter averaging (Model Soup) across 14 models.

Merged Models

The following models were included in the merge:

toksuite/supertoken_models-llama_google-byt5-small
toksuite/supertoken_models-llama_facebook-xglm-564M
toksuite/supertoken_models-llama_bigscience-bloom
toksuite/supertoken_models-llama_gpt2
toksuite/supertoken_models-llama_common-pile-comma-v0.1
toksuite/supertoken_models-llama_google-gemma-2-2b
toksuite/supertoken_models-llama_microsoft-Phi-3-mini-4k-instruct
toksuite/supertoken_models-llama_meta-llama-Llama-3.2-1B
toksuite/supertoken_models-llama_CohereLabs-aya-expanse-8b
toksuite/supertoken_models-llama_tiktoken-gpt-4o
toksuite/supertoken_models-llama_google-bert-bert-base-multilingual-cased
toksuite/supertoken_models-llama_Qwen-Qwen3-8B
toksuite/supertoken_models-llama_tokenmonster-englishcode-32000-consistent-v1
toksuite/supertoken_models-llama_mistralai-tekken

Merging Configuration

Method: Weighted Parameter Averaging
Weights: Simple average with merging lambda = 1.0.
Excluded Layers: Embeddings and LM Head were kept from the base model (toksuite/supertoken_models-llama_meta-llama-Llama-3.2-1B).

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("moe-dtoks/toksuite-all-merged")
tokenizer = AutoTokenizer.from_pretrained("moe-dtoks/toksuite-all-merged")