fairseq2

FAIR Sequence Modeling Toolkit 2

[AI at Meta @AIatMeta]

🚀New from Meta FAIR: today we’re introducing Seamless Interaction, a research project dedicated to modeling interpersonal dynamics.

The project features a family of audiovisual behavioral models, developed in collaboration with Meta’s Codec Avatars lab + Core AI lab, that render speech between two individuals into diverse, expressive full-body gestures and active listening behaviors, allowing the creation of fully embodied avatars in 2D and 3D.

These models have potential to create more natural, interactive virtual agents that can engage in human-like social interactions across a variety of settings.

Learn more: https://t.co/vl2jmfE7wX

[fairseq2 @fairseq2]

@engmlubbad Thanks. In the first place, we ensure that our implementation correctly capture the metrics and we welcome issues being raised for any potential factual errors in our open-sourced repo. When conducting benchmark, we follow best practices and avoid crimes: https://t.co/Luy3mShe6T

[fairseq2 @fairseq2]

[1/4] 🛠️ FAIRSeq2 – your go-to tool for reliable benchmarking and diagnosing infra issues! With native logging of metrics, monitor training performance in real-time and ensure great visibility. #AI #MachineLearning #fairseq2

[fairseq2 @fairseq2]

[4/4] Collaborate efficiently with reproducible experiment setups using FAIRSeq2. Identify root causes swiftly and share lessons learned with the community. Create your benchmarks and contribute!

[fairseq2 @fairseq2]

[3/4] Beyond TensorBoard and WanDB, FAIRSeq2 supports torch profilers (set trainer.profile and common.profilers.torch.enabled=True) to inspect potential infra issues. Dive deep into your training processes with various profilers and metric recorders.

[fairseq2 @fairseq2]

Sharing here one amazing paper built with fairseq2: https://t.co/6y4GL6GNfh

What about yours?

[fairseq2 @fairseq2]

🚀 Transform your LLM post-training with fairseq2! We make complex post-training into a breeze, so that you can make fairseq2 your paper machine!

Feel free to check our tutorials out:
- SFT: https://t.co/VVXwZu28wp
- DPO: https://t.co/aoSl1nJHp8

[fairseq2 @fairseq2]

@vllm_project @AIatMeta Nothing explains better than a vivid example:

[fairseq2 @fairseq2]

🚀 Big news for LLM researchers! #fairseq2 now has native support in @vllm_project. Deploy your fine-tuned language models with #vLLM in just one command for lightning-fast performance. Ready to accelerate your research like in @AIatMeta ? Check this out: https://t.co/mPVJaDRYmb

[fairseq2 @fairseq2]

Large Concept Models demonstrates what we can imagine beyond traditional sequence models -- check it out here: https://t.co/D27M9c4N9e

[fairseq2 @fairseq2]

🖼️ A gallery of open-source projects and papers powered by #fairseq2! 🚀

Seamless Communication and Large Concept Models are 2 vivid examples that showcase the potential of what we are building.

More exciting @metaai research built on fairseq2 is on the way!

[fairseq2 @fairseq2]

Seamless Communication as the best innovation recognized by TIMES and Nature -- check it out here: https://t.co/aLCWzEZi0c

[fairseq2 @fairseq2]

Check out our doc for more details: https://t.co/Pp94JqtX24

[fairseq2 @fairseq2]

👋 Hello world! We’re thrilled to announce the v0.4 release of fairseq2 — an open-source library from FAIR powering many projects at Meta. pip install fairseq2 and explore our trainer API, instruction & preference finetuning (up to 70B), and native vLLM integration.