Comments (2)
There is plan for fp8, but not int8 - that is usually used in context of inference, this repo focuses on training right now. We will very likely get around to model inference as well and int8 will be relevant then, but that is still fairly far out, likely on the scale of few ~months. fp8 is more like scale of few ~weeks.
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It is great to hear that FP8 in planned for training. Looks like there is lack of open source stack that deals with e2e LLM training process in the scale that an individual is able to comprehend and run. Thumbs up for this llm.c project!
Any updates on FP8 implementation? How difficult will be the code changes from current state?
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Related Issues (20)
- apparent compatibility issues with earlier c++ versions after recent pushes HOT 3
- I can not understand the `cublasGemmStridedBatchedEx` call in the `attention_forward`
- LLM.c in google colab HOT 1
- Running `quick start on CPU` on Macbook Pro M2 HOT 7
- OSError: Memory mapping file failed: Cannot allocate memory HOT 1
- is max_seq_len configurable or hardcoded parameter? HOT 2
- sel4 + llm.c > path to putting these llms in any mission critical system
- Windows issue with Cuda Toolkit 12.5 and latest MSVC compiler 17.10 HOT 2
- Specify torch version number in requirements.txt ? HOT 2
- Pretraining (with CPUs) HOT 5
- Getting "Floating point exception (core dumped)" Error HOT 4
- Is Multi-GPU config enabled even when I'm using one GPU? HOT 1
- Suggestion: Use smollm corpus HOT 3
- image-gpt HOT 1
- Larger Tokenizers
- Different batch_size results in different evaluation loss.
- TypeError: normal_() got an unexpected keyword argument 'generator' HOT 1
- MPI run with 8 GPU fails HOT 1
- MPI run error
- Suggestion: Test more Activation Functions
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