* Use named tuple from typing for typehints
* Add type hints
* Simplify expression
* Type hint fix
* Improved do_POST logic
Use a map of endpoints to methods to reduce redundancy in code
* Fix format
* Improve redundancy
Call method dynamically instead of writing out all arguments twice
* Send response instead of returning
* Fix typo
* Revert change
* Make adapter_file as Optional
* Mark formatter as optional
* format
* Create message generator
Store response data that stays static for the duration of the response inside of the object:
system_fingerprint
request_id
object_type
requested_model
Created a message generator, that dynamically creates messages from the metadata stored inside of the object, and the data from the model pipeline
* Remove leftover
* Update parameters to reflect new object structure
No longer pass all arguments between functions, but use the stores values inside of the object
* Parse body before calling request specific methods
* Call super init
* Update server.py
* Fixed outdated documentation parameter name
* Add documentation
* Fix sending headers twice
During testing I found that when using the streaming option, headers have always been sent twice. This should fix that
* Simplify streaming code by using guard clauses
Don't wrap wfile writes in try blocks, the server class has its own try block to prevent crashing
* Bug fix
* Use Content-Length header
Let the completion type specific methods finish sending the headers. This allows us to send the Content-Length header as the model returns a completion.
* Update utils.py
* Add top_p documentation
* Type hint model and tokenizer as required
* Use static system fingerprint
System fingerprint now stays the same across requests
* Make type hint more specific
* Bug Fix
Supplying less than 2 models to merge would raise ValueError and calls len on unbound "models". Should be "model_paths" instead.
Mark upload_repo as optional
* Move more of the shared code into do_POST
Processing stop_id_sequences is done no matter the request endpoint or type, move it into the shared section. handle_ methods now just return the prompt in mx.array form.
* Store stop_id_sequences as lists instead of np
During testing I found that letting the tokenizer return values as python lists and converting them to mlx arrays was around 20% faster than having the tokenizer convert them to np, and from np to mlx. This allows makes it so numpy no longer needs to be imported.
* Update stop_id_sequences docs
* Turn if check to non-inclusive
Only continue if buffer is smaller
* Documentation fix
* Cleared method names
Instead of handle_stream and generate_competion, we should name it handle_completion.
Instead of handle_completions and handle_chat_completions, we should name it handle_text_completions, since both are completions, calling it text completions should make it more descriptive
* Make comment clearer
* fix format
* format
* 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
---------
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
* Add metadata when saving safetensors
Add metadata format="pt" for safetensors so that model's are accessible to `transformers` users as well.
* save with metadata format mlx
Save the model weights with metadata format of "mlx".
* Updated llms/mlx_lm/generate.py
* Don't serve local directory
BaseHTTPRequestHandler serves the current directory by default. Definitely not intended behaviour. Remove the "do_HEAD" and "do_GET" methods.
* Fix typo in method name
I assume hanlde_stream was intended to be called handle_stream
* Fix outdated typehint
load_model returns nn.Module, however fetch_from_hub was not updated to reflect the change
* Add some more type hints
* Add warnings for using in prod
Add a warning to README and runtime, discouraging use in production. The warning is the same as on the python docs for HTTPServer https://docs.python.org/3/library/http.server.html
* format
* nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* LoRA：Refactor TrainingCallback to enhance flexibility and extensibility
This commit refactors the TrainingCallback class to accept a dictionary parameter for both on_train_loss_report and on_val_loss_report methods. By switching from multiple parameters to a single dict parameter, this change significantly improves the class's flexibility and makes it easier to extend with new training or validation metrics in the future without altering the method signatures. This approach simplifies the addition of new information to be logged or processed and aligns with best practices for scalable and maintainable code design.
* LoRA: Add printing and callbacks for learning rate during training
Mixtral models throw the following exception
```
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/generate.py", line 119, in <module>
main(args)
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/generate.py", line 96, in main
model, tokenizer = load(args.model, tokenizer_config=tokenizer_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/utils.py", line 278, in load
model = load_model(model_path)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/utils.py", line 221, in load_model
model_class, model_args_class = _get_classes(config=config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/utils.py", line 46, in _get_classes
arch = importlib.import_module(f"mlx_lm.models.{model_type}")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 940, in exec_module
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/models/mixtral.py", line 11, in <module>
@dataclass
^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 1230, in dataclass
return wrap(cls)
^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 1220, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 1027, in _process_class
_init_fn(all_init_fields,
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 545, in _init_fn
raise TypeError(f'non-default argument {f.name!r} '
TypeError: non-default argument 'model_type' follows default argument
```
* LoRA: Improve validation error for LoRA layer count exceeding model layer
This commit enhances the error handling when the specified LoRA layer count exceeds the total number of layers in the model. It clarifies the error message to provide actionable feedback for users, guiding them to adjust their input parameters accordingly.
* format + nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* lazy model import in mlx_lm
* change lora loading
* fix olmo lora
* remove a bunch of unused stuff from plamo
* move phixtral to mlx-lm and out of llms/
* Add checkpoints directory for adapter weights
The code was modified to create a checkpoints directory if it doesn't exist yet. Adapter weights are now saved to this checkpoints directory during the training iterations.
Corrected indentation of Save adapter weights code because it was part of "if eval"
* Fixing a blank added by mistake
* update a few examples to use compile
* update mnist
* add compile to vae and rename some stuff for simplicity
* update reqs
* use state in eval
* GCN example with RNG + dropout
* add a bit of prefetching
* initial commit
* style fixes
* update of ACKNOWLEDGMENTS
* fixed comment
* minor refactoring; removed unused imports
* added cifar and cvae to top-level README.md
* removed mention of cuda/mps in argparse
* fixed training status output
* load_weights() with strict=True
* pretrained model update
* fixed imports and style
* requires mlx>=0.0.9
* updated with results using mlx 0.0.9
* removed mention of private repo
* simplify and combine to one file, more consistency with other exmaples
* few more nits
* nits
* spell
* format
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* chore(mlx-lm): add model weight index in save_weights
* Update llms/mlx_lm/utils.py
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Update llms/mlx_lm/utils.py
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* chore: save total siZe as param size isntead of file size
* chore: clean up format
---------
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
