* 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
* Adds EXAONE architecture.
* nits + format
* format
* clean up and fix rope
* clean up and fix rope
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* 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>
* feat: Nemotron
https://huggingface.co/nvidia/Minitron-4B-Base
This is basically Llama with partial RoPE and LayerNorm instead of
BatchNorm. Also they add 1 to the LayerNorm weight for some reason.
* fixup! feat: Nemotron
* nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* feat: deepseek v1
DeepSeek is still releasing models on the DeepSeek V1 architecture.
```sh
mlx_lm.convert --hf-path deepseek-ai/DeepSeek-Prover-V1.5-RL --mlx-path DeepSeek-Prover-V1.5-RL-8bit --q-bits 8 -q
mlx_lm.generate --model DeepSeek-Prover-V1.5-RL-8bit --ignore-chat-template --max-tokens 512 --prompt 'import Mathlib
import Aesop
set_option maxHeartbeats 0
open BigOperators Real Nat Topology Rat
/-- The second and fourth terms of a geometric sequence are $2$ and $6$. Which of the following is a possible first term?
Show that it is $\frac{2\sqrt{3}}{3}$.-/
theorem amc12b_2003_p6 (a r : ℝ) (u : ℕ → ℝ) (h₀ : ∀ k, u k = a * r ^ k) (h₁ : u 1 = 2)
(h₂ : u 3 = 6) : u 0 = 2 / Real.sqrt 3 ∨ u 0 = -(2 / Real.sqrt 3) := by'
```
* nits
* nits
* nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* feature: LoRA adapter for Embeddings
* feature: wire in LoRAEmbedding into the tuner. Allow the embedding and non model.layers Linear layers to be targeted for fine tuning
* feature: DoRA adapter for Embeddings
* feature: wire in DoRAEmbedding
* bugfix: ensure self.m is recalculated when the linear layer is changed in DoRALinear.from_linear
* refactor: prefer from_base over from_linear or from_embedding. prefer fuse over to_linear or to_embedding
* cleanup: remove unused imports in test_dora.py
* refactor: remove unnecessary non_layer_modules
* cleanup: remove wrong comments for lora embedding dropout. remove uncessary parens in dora embedding dropout
* nits
---------
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
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* LoRA: Extract pre_processing_model function
* LoRA: Extract small functions(train_model,evaluate_model)
* move test case to test_tuner_utils.py
* nits
* nits
* remove extra param, validate at it 0
* version
* fix test
---------
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
* support dora finetune
* solve problems in lora.py and tuner.utils.py
* add use_dora (bool) in functions of load adapters
* delete all unsupported quantization code and fix all the calculate problems in mlx_lm/tuner/dora.py
* Using stop_gradient to prevent gradients from flowing through ‘norm’ during backpropagation
* set DEFAULT_USE_DORA in mlx_lm/generate.py
* add annotation for all the use_dora
* mlx_lm/fuse.py support fuse dora layers and fix a bug of to_linear() in mlx_lm/tuner/dora.py
* simplify code of juding type of a fused layer in mlx_lm/fuse.py
* add use_dora in mlx_lm/fuse.py when apply_lora_layers()
* style + nits
* style + nits
* more updates
---------
Co-authored-by: chenyifei08 <chenyifei08@baidu.com>
Co-authored-by: Awni Hannun <awni@apple.com>
* 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
---------
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
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Add dropout parameter to lora configuration
A dropout parameter has been added to the lora configuration settings in lora_config.yaml. The LoRALinear class in utils.py has been updated to take this new parameter. Additionally, a AttributeError: 'types.SimpleNamespace' object has no attribute 'prompt' related to `args.prompt` has been removed from lora.py.
* Update lora_config.yaml
Set dropout to 0.0 in the sample config file
* format
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* Add --lora-all-linear option to apply LoRa to all linear transfer block layers
* Moved to YAML config and added specification of rank & alpha
* nits in conifg, more tests
* nit
* run tests for prs
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* 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
