* 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
* Add support for setting MLX cache limit in GB
* Add support for setting MLX cache limit in GB in mlx_lm.server
* format
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Co-authored-by: Awni Hannun <awni@apple.com>
* Add model management functionality for local caches
This commit introduces a set of command-line utilities for managing MLX models downloaded and saved locally in Hugging Face cache. The functionalities include scanning existing models, retrieving detailed information about a specific model, and deleting a model by its name.
* Added mlx_lm.model to setup.py
* nits
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Co-authored-by: Awni Hannun <awni@apple.com>
* Update model card describe
- Add full link jump
- Add the address of the model uploader's Hugging Face homepage
* Add user_info to reduce whoami calls
* Remove the -U argument
* remove HF user info
* run pre-commit
* support for phi-3 4bits quantized gguf weights
* Added link to 4 bits quantized model
* removed some prints
* Added correct comment
* Added correct comment
* removed print
Since last condition already prints warning for when quantization is None
* 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
* chore(mlx-lm): clean up the top p imp
* chore: clean up
* chore: add test
* chore: address comments
* chore: clean up docs string
* chore: clean up test
* wip
* wip
* feat: convert mlx model to gguf f16
* chore: conver norm layer to float32 to avoid overflow issue
* chore: add support for mixtral
* chore: clean up
* chore: remove unused import statement
* chore: clean up weight name mapping
* version and readme
* actual version bump
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Co-authored-by: Awni Hannun <awni@apple.com>
