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abhishek 
posted an update about 2 months ago
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1917
🎉 SUPER BLACK FRIDAY DEAL 🎉

Train almost any model on a variety of tasks such as llm finetuning, text classification/regression, summarization, question answering, image classification/regression, object detection, tabular data, etc for FREE using AutoTrain locally. 🔥
https://github.com/huggingface/autotrain-advanced
abhishek 
posted an update 3 months ago
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5757
INTRODUCING Hugging Face AutoTrain Client 🔥
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks 🤗

To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.

"pip install autotrain-advanced"

Github repo: https://github.com/huggingface/autotrain-advanced
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abhishek 
authored a paper 3 months ago
abhishek 
posted an update 3 months ago
abhishek 
posted an update 5 months ago
abhishek 
posted an update 5 months ago
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1859
🚨 NEW TASK ALERT 🚨
Extractive Question Answering: because sometimes generative is not all you need 😉
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 🚀
Check out the blog post on getting started with this task: https://huggingface.co./blog/abhishek/extractive-qa-autotrain
abhishek 
posted an update 8 months ago
abhishek 
posted an update 9 months ago
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2938
🚨 NEW TASK ALERT 🚨
🎉 AutoTrain now supports Object Detection! 🎉
Transform your projects with these powerful new features:
🔹 Fine-tune any supported model from the Hugging Face Hub
🔹 Seamless logging with TensorBoard or W&B
🔹 Support for local and hub datasets
🔹 Configurable training for tailored results
🔹 Train locally or leverage Hugging Face Spaces
🔹 Deployment-ready with API inference or Hugging Face endpoints
AutoTrain: https://hf.co/autotrain
abhishek 
posted an update 9 months ago
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3066
🚀🚀🚀🚀 Introducing AutoTrain Configs! 🚀🚀🚀🚀
Now you can train models using yaml config files! 💥 These configs are easy to understand and are not at all overwhelming. So, even a person with almost zero knowledge of machine learning can train state of the art models without writing any code. Check out example configs in the config directory of autotrain-advanced github repo and feel free to share configs by creating a pull request 🤗
Github repo: https://github.com/huggingface/autotrain-advanced
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abhishek 
posted an update 9 months ago
abhishek 
posted an update 9 months ago
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2372
Trained another version of llama3-8b-instruct which beats the base model. This time without losing too many points on gsm8k benchmark. Again, using AutoTrain 💥 pip install autotrain-advanced
Trained model: abhishek/autotrain-llama3-orpo-v2
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abhishek 
posted an update 9 months ago
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3478
With AutoTrain, you can already finetune the latest llama3 models without writing a single line of code. Here's an example finetune of llama3 8b model: abhishek/autotrain-llama3-no-robots
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philschmid 
posted an update 10 months ago
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7201
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: https://huggingface.co./spaces/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 about 1 year 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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abhishek 
posted an update about 1 year ago
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Happy to announce, brand new, open-source Hugging Face Competitions platform 🚀 Now, create a machine learning competition for your friends, colleagues or the world for FREE* and host it on Hugging Face: the AI community building the future. Creating a competition requires only two steps: pip install competitions, then run competitions create and create competition by answering a few questions 💥 Checkout the github repo: https://github.com/huggingface/competitions and docs: https://hf.co/docs/competitions
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abhishek 
posted an update about 1 year ago
abhishek 
posted an update about 1 year ago
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Hello Huggers! 🤗
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abhishek 
authored 2 papers almost 2 years ago
philschmid 
authored a paper almost 2 years ago