* initial commit
* udpate ACKNOWLEDGMENTS.md
* adding olmoe to training
* clean up
* faster generation
* remove sanitize method
* more clean ups
* adding SwitchGLU
* clean up
* a little faster and adding norm_topk_prob
* formated
* Adding full model weights finetuning
* Updating the LORA.md and ACKNOWLEDGMENTS.md files.
* removing --use-dora and --fulll-training and adding --fine-tune-type
* some clean up
* reformating and fixing dora training
* updated CONFIG_DEFAULTS
* update config example
* update in the config example fie
* Update LORA.md
* merge and commit
* adding argument for dora linear layer
* clean up
* clean up in the example yaml file
* fix
* final fix before sending
* small addition to re md file
* fix for loading the fully trained model by saving all the files and configs correctly
* clean up
* removing the unnesesairy files
* changing lora layers back to 16
* removed max file size
* nits
* resolve merge
* some consistency changes
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* initial commit
* initial commit
* Adding first lines
* adding x, and dt projection layers
* adding the clamping mechanism
* First succesful inference
* last commit for today - added custom geenrate function and it works as expected, will try training and then with loading a model from the hub
* clean up
* save up
* almost
* update
* update
* fixed cache handeling
* fixed loading
* added seperate generat_step method in the model and also in the utils to automaticaly use the generate step mthod in the model class
* quick update
* still not working
* save
* still not working
* initial commit
* utils.py logits = logits[:, -1, :] TypeError: tuple indices must be integers or slices, not tuple
* update
* update
* Fixing the Batching Depfwise Comnvolution and multi token input
* fixing generate and logits outputs
* Done!
* Fixing the cache handling, generating works now trying training
* update ACKNOWLEDGEMENTS
* removing the model_type if stuff in the _step loop in generate_step and adding MambaCache in base.py for training easier generations and removing mamba in tuner/utils.
* quick clean up
* update trainer/utils for right initialisation of the layers for LoRA, but not working.
* clean up
* Forther update to trainer/utils for correct layer selection. Successfull training
* removing extra mamba-infer.py file
* clean up, reformating will come later
* reformat and big clean up, final commit
* some speedups and cleanups
* fix test
* nits
* nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* 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
* 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>
* 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>
* Convert HF weights of PLaMo and load it to a plamo model in mlx
* Fix model inference part
* Add bos at the beginning of the prompt
* Fix convert.py to copy tokenizer.model into the converted dir
* Use the required insturction format in generate.py when "--instruct" option is specified
* Change filenames and update existing scripts
* Add README
* Add requirements.txt
* Fix plamo.py to stop generation when EOS appears
* Add quantization to convert.py
* Use mlx>=0.0.9 for mx.core.outer() in PLaMo model
* Update acknowledgements.md
* Fix card text in upload_to_hub()
* Not use prompt template when --instruct is not specified
* Ask if you trust_remote_code for loading tokenizer of PLaMo
* Check the user trusts the remote code when converting
* Remove plamo directory
* Update README
* Add PLaMo model file
* Fix the handling of cache in PLaMo and update README
* Ask if trust_remote_code only when the model is PLaMo
* Remove resolve_trust_remote_code from convert.py and use the latest transformers
* Remove code not to add EOS
* Update README to fix an example not to use noncommercial version of the model
* Remove unused imports
* Remove unnecessary description about the instruct model of PLaMo from README
* format, nits in README
* typo
---------
Co-authored-by: Shunta Saito <shunta@mitmul-mbp.local>
Co-authored-by: Awni Hannun <awni@apple.com>
