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Amazon SageMaker
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philschmid
updated
a
dataset
17 days ago
philschmid
posted
an
update
9 months ago
Post
6906
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! 🤗
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! 🤗
-
- 4 replies
philschmid
posted
an
update
11 months ago
Post
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. 🔜
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. 🔜
- 4 replies
philschmid
authored
a
paper
over 1 year ago