GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
catherinearnett
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
catherinearnett
Model Tree
Input Modalities
Output Modalities
Supported Functionality
All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences. For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)! Details for this model specifically:
Training dataset: OSCAR 2021/09
Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000.
Load the model:
Note: if you do not specify a revision, it will load the final checkpoint of the model. See above for the list of checkpoints. The checkpoint step is the name of the revision.
markdown
from transformers import AutoTokenizer, AutoModelForCausalLMtokenizer = AutoTokenizer.from_pretrained("catherinearnett/B-GPT_en_nl_sequential")model = AutoModelForCausalLM.from_pretrained("catherinearnett/B-GPT_en_nl_sequential", revision = "128000")
Text Generation:
markdown
from transformers import pipelinepipe = pipeline("text-generation", model="catherinearnett/B-GPT_en_nl_sequential")print(pipe("I am a", max_length=20)[0]["generated_text"])
If you use this model, please cite:
markdown
@article{arnett2025acquisition,title={On the Acquisition of Shared Grammatical Representations in Bilingual Language Models},author={Arnett, Catherine and Chang, Tyler A and Michaelov, James A and Bergen, Benjamin K},journal={arXiv preprint arXiv:2503.03962},year={2025}}