GermanT5

non-profit

AI & ML interests

Creating a German T5 model

Recent Activity

GermanT5's activity

stefan-it 
posted an update 12 days ago
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My latest project is the outcome of the last 2+ years working with TPUs from the amazing TPU Research Cloud (TRC) program and training Encoder-only LMs with the TensorFlow Model Garden library.

👉 Link: https://github.com/stefan-it/model-garden-lms

An overview of some features:

- Cheatsheet for setting-up a TPU VM Pod (with all necessary dependencies) to pretrain LMs with TF Model Garden
- Conversion scripts that convert TF Model Garden weights to Hugging Face Transformers-compatible models
- Supported architectures include BERT, BERT with Token Dropping and TEAMS

I also released BERT-based models pretrained on the great Hugging Face FineWeb and FineWeb-Edu datasets (10BT subset). With more to come!

👉 Model Hub Link: https://huggingface.co/model-garden-lms

If you find these resources useful, please give them a like!

Made from Bavarian Oberland with ❤️ and 🥨.
philschmid 
posted an update 9 months ago
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New state-of-the-art open LLM! 🚀 Databricks just released DBRX, a 132B MoE trained on 12T tokens. Claiming to surpass OpenAI GPT-3.5 and is competitive with Google Gemini 1.0 Pro. 🤯

TL;DR
🧮 132B MoE with 16 experts with 4 active in generation
🪟 32 000 context window
📈 Outperforms open LLMs on common benchmarks, including MMLU
🚀 Up to 2x faster inference than Llama 2 70B
💻 Trained on 12T tokens
🔡 Uses the GPT-4 tokenizer
📜 Custom License, commercially useable

Collection: databricks/dbrx-6601c0852a0cdd3c59f71962
Demo: databricks/dbrx-instruct

Kudos to the Team at Databricks and MosaicML for this strong release in the open community! 🤗
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philschmid 
posted an update 11 months ago
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What's the best way to fine-tune open LLMs in 2024? Look no further! 👀 I am excited to share “How to Fine-Tune LLMs in 2024 with Hugging Face” using the latest research techniques, including Flash Attention, Q-LoRA, OpenAI dataset formats (messages), ChatML, Packing, all built with Hugging Face TRL. 🚀

It is created for consumer-size GPUs (24GB) covering the full end-to-end lifecycle with:
💡Define and understand use cases for fine-tuning
🧑🏻‍💻 Setup of the development environment
🧮 Create and prepare dataset (OpenAI format)
🏋️‍♀️ Fine-tune LLM using TRL and the SFTTrainer
🥇 Test and evaluate the LLM
🚀 Deploy for production with TGI

👉  https://www.philschmid.de/fine-tune-llms-in-2024-with-trl

Coming soon: Advanced Guides for multi-GPU/multi-Node full fine-tuning and alignment using DPO & KTO. 🔜
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