Comments (1)
Hi! We didn't formalize any workloads but we experimented up to 120B. Feel free to email me ([email protected]) if you're interested in learning more. The efficiency scales favorably -- MFU's tend to improve with larger models.
What is your hardware configuration (i.e. v3-X, v4-X)? I'm happy to help you find a high performance configuration.
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Related Issues (20)
- llama_or_mistral_ckpt.py file requiring checkpoints in local file system
- Llama3 HOT 1
- Eval on C4? HOT 1
- Gemma 2 support HOT 3
- `hf_access_token` only effective for loading gated datasets, not gated tokenizers
- Inconsistent code formatting
- Support target masking (aka loss masking or label masking) for SFT datasets
- How to implement 1F1B pipeline parallelism in Jax? HOT 1
- https://us-python.pkg.dev/gce-ai-infra/maxtext-build-support-packages/simple/ not public
- Inconsistent environment variable names
- Outdated links in `First_run.md` HOT 1
- Multihost training collapses from time to time when loading the next batch HOT 3
- FlashAttention Support - TPUv3 HOT 1
- Long Context HOT 2
- Make MaxText as Python Modules
- converting Gemma maxtext compatible checkpoint to Hugging Face format HOT 1
- Standalone checkpoint write seems to have memory leak
- How to load tfrecords from local file system for Mlperf training? HOT 3
- mlperf gpt3 ckpt permission issues HOT 7
- aqtp release 0.8.0 breaking dependencies HOT 1
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