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distributed-layers
mlx-examples/t5/requirements.txt

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Switch to fast RMS/LN Norm (#603) * use nn.RMSNorm, use sdpa, cleanup * bump mlx versions * minor update * use fast layer norm * version bump * update requirement for whisper * update requirement for gguf
2024-03-23 22:13:51 +08:00
mlx>=0.8.0
Add T5 and Flan-T5 example (#113) * Add skeleton * Load all encoder weights * Pass config to all modules, fix ln * Load position bias embeddings * Load decoder weights * Move position biases to attention module * translate pytorch to mx * Fix default prompt * Fix relative_attention_max_distance config * No scaling, no encoder mask * LM head * Decode (broken after 1st token) * Use position bias in all layers * Utils to compare encoder output * Fix layer norm * Fix decoder mask * Use position bias in decoder * Concatenate tokens * Remove prints * Stop on eos * Measure tokens/s * with cache * bug fix with bidirectional only for encoder, add offset to position bias * format * Fix T5.__call__ * Stream output * Add argument to generate float16 npz * Load config from HF to support any model * Uncomment bidirectional param * Add gitignore * Add readme.md for t5 * Fix relative position scale * Fix --encode-only * Run hf_t5 with any model * Add hf generation for comparison * Fix type for attention mask * Increase hf max_length * Rescale output before projecting on vocab * readme updates * nits * Pass ln2 to cross attention * Fix example * Fix attention for 3b model * fp16, abstract tokenizer a bit, format * clamp for low precision * higher clipping, remove non-helpful casts * default to fp32 for now * Adds support for flan-t5 * Update t5 docs on variant support * readme flan * nit --------- Co-authored-by: Awni Hannun <awni@apple.com>
2023-12-19 12:25:34 +08:00
numpy
transformers
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