* 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
* 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/
* feat(mlx-lm): add de-quant for fuse
* chore: disable quant in to linear when de-quant enabled
* chore: add better error handling for adapter file not found
