* Add logit soft capping to gemma, and fix precision issues
Gemma was babbling nonsense - so I figured out it was due to not having logit softcapping and precision issues causing NaNs (so I implemented the softcapping and added more float32 inference). gemma-27b-it-4bit now works flawlessly (or near-flawlessly, no sliding-window attention).
* get rid of comments
* get rid of last comments (sry lol)
* nits
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Co-authored-by: Awni Hannun <awni@apple.com>
* Su-RoPE
* nits
* Update su_rope.py
* Update su_rope.py
Per GPT4: "The error TypeError: 'type' object is not subscriptable is caused by using the type hint list[float] in a version of Python that does not support it. This syntax is only available in Python 3.9 and later."
* Ran isort
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Co-authored-by: Awni Hannun <awni@apple.com>
* GPT-2 model support
* Add test for gpt2 model
* Fix weight sanitizing for quantization
* use approx gelu
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Co-authored-by: Awni Hannun <awni@apple.com>
* add support for granite 3-8B config
* add gpt_bigcode
* add positional embedding condition.
* add support for granite 3-8B config
* add gpt_bigcode
* add positional embedding condition.
* remove unused function
* rebase fix
* move position emebedding to mask creation
* add to tuner and format
* add support for granite 3-8B config
* add gpt_bigcode
* add positional embedding condition.
* add support for granite 3-8B config
* add gpt_bigcode
* add positional embedding condition.
* rebase fix
* move position emebedding to mask creation
* add to tuner and format
* refactor mask
* remove dropout layers
* Pad mask with zeros for non-square attention matrices
The current implementation of the mask assumes the attention matrix is square, which is true if there is no cache. However, if one wishes to produce multiple tokens at a time, such as in speculative decoding implementations, a rectangular mask is necessary.
This change pads the bottom of the mask with zeros so multi-token decoding with a cache works correctly.
* Directly create mask instead of padding
* Update llama.py
* Added support for the MiniCPM architecture
* Added support for the MiniCPM architecture
* Updated utils.py and LORA.md
* Updated utils.py and LORA.md
* Update implementation details for MiniCPM architecture
* Cleaning up
* fixed the missing lm.head layer problem
* Refactor Model class to dynamically handle tied and untied word embeddings
* Quick update
* added a dynamic rope scaling base calucaltion
* Added support for the MiniCPM architecture
* Added support for the MiniCPM architecture
* Updated utils.py and LORA.md
* Updated utils.py and LORA.md
* Update implementation details for MiniCPM architecture
* Cleaning up
* fixed the missing lm.head layer problem
* Refactor Model class to dynamically handle tied and untied word embeddings
* added a dynamic rope scaling base calucaltion
* quick fix and clean up
* clean up again
* removed the MiniCPMNorm class as its not used
* forgot something, sorry
* format
* version bump
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Co-authored-by: Awni Hannun <awni@apple.com>
* Initial config handler and test
* Added means to run from CLI
* Update lora config loading and tests
* Constrain scheduler config (warmup and minimum LR) for each kind
* Update reference to moved schedule_config module
* Minor fix
* Fix typos
* Moved build_schedule and tests
* nits in schedule config
* flake
* fix path
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Co-authored-by: Awni Hannun <awni@apple.com>
* 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
* Add Starcoder2 model and update utils.py
* Refactor model arguments and modules in starcoder2.py
* Refactor FeedForward class to MLP in starcoder2.py
* Fix typo
* pre-commit
* Refactor starcoder2.py: Update model arguments and modules
* Fix LM head and MLP layers
* Rename input layer norm
* Update bias in linear layers
* Refactor token embeddings in Starcoder2Model
* Rename to standard HF attention layer name
* Add LayerNorm
* Add transposed token embeddings (like in Gemma)
* Refactor MLP and TransformerBlock classes
* Add tie_word_embeddings option to ModelArgs and update Model implementation
* Add conditional check for tying word embeddings in Starcoder2Model
* Fix bias in lm_head linear layer
* Remove unused LayerNorm in stablelm
* Update transformers dependency to use GitHub repository
* fix lm head bug, revert transformer req
* Update RoPE initialization in Attention class
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Co-authored-by: Awni Hannun <awni@apple.com>
* StableLM now part of Transformers as stablelm rather than stablelm_epoch; changed config to match new changes
* removing old file
* reference new stablelm
