wip fix

* build cloud images with torch 2.6.0

* nightlies too

.github/workflows/main.yml

@@ -88,6 +88,11 @@ jobs:
             pytorch: 2.5.1
             axolotl_extras:
             is_latest: true
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/nightlies.yml

@@ -80,6 +80,11 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             axolotl_extras:
+          - cuda: 124
+            cuda_version: 12.4.1
+            python_version: "3.11"
+            pytorch: 2.6.0
+            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

README.md

@@ -19,9 +19,6 @@
     <br/>
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests-nightly.yml/badge.svg" alt="tests-nightly">
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/multi-gpu-e2e.yml/badge.svg" alt="multigpu-semi-weekly tests">
-    <a href="https://www.phorm.ai/query?projectId=e315ba4a-4e14-421f-ab05-38a1f9076f25">
-    <img alt="phorm.ai" src="https://img.shields.io/badge/Phorm-Ask_AI-%23F2777A.svg?&logo=data:image/svg+xml;base64,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">
-  </a>
 </p>
 
 Axolotl is a tool designed to streamline post-training for various AI models.

_quarto.yml

@@ -40,6 +40,7 @@ website:
 
         - section: "Deployments"
           contents:
+            - docs/docker.qmd
             - docs/multi-gpu.qmd
             - docs/multi-node.qmd
             - docs/ray-integration.qmd

docker/Dockerfile-cloud

@@ -14,7 +14,7 @@ COPY scripts/motd /etc/motd
 
 RUN pip install jupyterlab notebook ipywidgets && \
     jupyter lab clean
-RUN apt install --yes --no-install-recommends openssh-server tmux && \
+RUN apt install --yes --no-install-recommends openssh-server tmux iproute2 nvtop && \
     mkdir -p ~/.ssh && \
     chmod 700 ~/.ssh && \
     printf "\n[[ -z \"\$TMUX\"  ]] && { tmux attach-session -t ssh_tmux || tmux new-session -s ssh_tmux; exit; }\n" >> ~/.bashrc && \

docs/config.qmd

@@ -154,8 +154,6 @@ datasets:
       content: value
       # ...
 
-    message_property_mappings:
-
     # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
     roles:
       user: ["human", "user"]
@@ -163,6 +161,12 @@ datasets:
       system: ["system"]
       tool: ["tool"]
 
+    # Optional[bool]. Whether to drop the system turn from the dataset. Only works with chat_template.
+    # This does not drop the default system message from chat_template if it exists. If you wish to,
+    # we recommend using a custom jinja template with the default system message removed or
+    # adding a system turn with empty content.
+    drop_system_message:
+
     # IMPORTANT: The following fields determine which parts of the conversation to train on.
     # Priority order: message_field_training > message_field_training_detail > train_on_inputs or role in roles_to_train
     # See examples at `docs/dataset-formats/conversation.qmd`
@@ -222,8 +226,8 @@ process_reward_model:
 chat_template: tokenizer_default
 # custom jinja template for chat template. This will be only used if chat_template is set to `jinja` or `null` (in which case chat_template is automatically set to `jinja`). Default is null.
 chat_template_jinja: null
-# Changes the default system message
-default_system_message: You are a helpful assistant. Please give a long and detailed answer. # Currently only supports chatml.
+# Changes the default system message. Currently only supports chatml.
+default_system_message: You are a helpful assistant. Please give a long and detailed answer.
 # Axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared
@@ -445,7 +449,7 @@ gradient_checkpointing: false
 early_stopping_patience: 3
 
 # Specify a scheduler and kwargs to use with the optimizer
-lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
+lr_scheduler: # 'one_cycle' | 'rex' | 'log_sweep' | empty for cosine
 lr_scheduler_kwargs:
 cosine_min_lr_ratio: # decay lr to some percentage of the peak lr, e.g. cosine_min_lr_ratio=0.1 for 10% of peak lr
 cosine_constant_lr_ratio: # freeze lr at some percentage of the step, e.g. cosine_constant_lr_ratio=0.8 means start cosine_min_lr at 80% of training step (https://arxiv.org/pdf/2308.04014.pdf)
@@ -528,6 +532,8 @@ flash_attn_fuse_mlp: # Whether to fuse part of the MLP into a single operation
 sdp_attention:
 # Shifted-sparse attention (only llama) - https://arxiv.org/pdf/2309.12307.pdf
 s2_attention:
+# Optional[bool]. Whether to use low_cpu_mem_usage
+low_cpu_mem_usage:
 # Resume from a specific checkpoint dir
 resume_from_checkpoint:
 # If resume_from_checkpoint isn't set and you simply want it to start where it left off.
@@ -548,6 +554,13 @@ special_tokens:
 # Add extra tokens.
 tokens:
 
+# Mapping token_id to new_token_string to override reserved added_tokens in the tokenizer.
+# Only works for tokens that are not part of the base vocab (aka are added_tokens).
+# Can be checked if they exist in tokenizer.json added_tokens.
+added_tokens_overrides:  # Dict[int, str]
+#  128041: "<|im_start|>"
+#  128042: "<|im_end|>"
+
 # FSDP
 fsdp:
 fsdp_config:

docs/dataset-formats/conversation.qmd

@@ -74,6 +74,10 @@ datasets:
     train_on_eos:
 ```
 
+::: {.callout-tip}
+If you receive an error like "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null.", it means the tokenizer does not have a default `chat_template`. Follow the examples below instead to set a custom `chat_template`.
+:::
+
 2. Using the `gemma` chat template to override the tokenizer_config.json's chat template on OpenAI messages format, training on all assistant messages.
 
 ```yaml

docs/dataset-formats/index.qmd

@@ -129,6 +129,7 @@ You can mix and match within each approach or across approaches to train a model
 We suggest this approach when you want to bring your own tokenized dataset.
 
 Axolotl expects the dataset to have three keys:
+
 - `input_ids`: from tokenizing formatted prompt
 - `attention_mask`: for masking padding. If you don't add padding, it would be equal to `len(input_ids) * [1]`
 - `labels`: this is the same as `input_ids`, however, if you want to mask certain tokens, you would set those indices to `-100`.

docs/docker.qmd

@@ -0,0 +1,140 @@
+---
+title: "Docker"
+format:
+  html:
+    toc: true
+    toc-depth: 4
+---
+
+This section describes the different Docker images that are released by AxolotlAI at [Docker Hub](https://hub.docker.com/u/axolotlai).
+
+## Base
+
+The base image is the most minimal image that can install Axolotl. It is based on the `nvidia/cuda` image. It includes python, torch, git, git-lfs, awscli, pydantic, and more.
+
+#### Image
+
+```
+axolotlai/axolotl-base
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-base)
+
+#### Tags format
+
+```bash
+main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
+```
+
+Tags examples:
+
+- `main-base-py3.11-cu124-2.6.0`
+- `main-base-py3.11-cu124-2.5.1`
+- `main-base-py3.11-cu124-2.4.1`
+
+## Main
+
+The main image is the image that is used to run Axolotl. It is based on the `axolotlai/axolotl-base` image and includes the Axolotl codebase, dependencies, and more.
+
+#### Image
+
+```
+axolotlai/axolotl
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
+
+#### Tags format {#sec-main-tags}
+
+```bash
+# on push to main
+main-py{python_version}-cu{cuda_version}-{pytorch_version}
+
+# latest main (currently torch 2.5.1, python 3.11, cuda 12.4)
+main-latest
+
+# nightly build
+{branch}-{date_in_YYYYMMDD}-py{python_version}-cu{cuda_version}-{pytorch_version}
+
+# tagged release
+{version}
+```
+
+:::{.callout-tip}
+
+There may be some extra tags appended to the image, like `-vllm` which installs those packages.
+
+:::
+
+Tags examples:
+
+- `main-py3.11-cu124-2.6.0`
+- `main-py3.11-cu124-2.5.1`
+- `main-py3.11-cu124-2.4.1`
+- `main-latest`
+- `main-20250303-py3.11-cu124-2.6.0`
+- `main-20250303-py3.11-cu124-2.5.1`
+- `main-20250303-py3.11-cu124-2.4.1`
+- `0.7.1`
+
+## Cloud
+
+The cloud image is the image that is used to run Axolotl in the cloud. It is based on the `axolotlai/axolotl` image and sets ENV variables like HuggingFace cache directories for volume mounts, tmux, and more for different cloud providers.
+
+:::{.callout-tip}
+
+Jupyter lab is run by default. Set `JUPYTER_DISABLE=1` in the environment variables to disable it.
+
+:::
+
+#### Image
+
+```
+axolotlai/axolotl-cloud
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud)
+
+#### Tags format
+
+This uses the same tags as the [`main` image](#sec-main-tags).
+
+#### Environment variables
+
+- `JUPYTER_DISABLE`: Disable Jupyter lab.
+- `JUPYTER_PASSWORD`: Set a password for the Jupyter lab.
+- `PUBLIC_KEY`: Add a public key for the SSH service.
+- `SSH_KEY`: Add a private key for the SSH service.
+
+#### Volume mounts
+
+:::{.callout-tip}
+
+We recommend mounting volumes to `/workspace/data` for data persistence. `/workspace/axolotl` contains the source code and is ephemeral.
+
+:::
+
+- `/workspace/data/axolotl-artifacts`: Directory to store Axolotl artifacts.
+- `/workspace/data/huggingface-cache`: Directory to store HuggingFace cache.
+
+## Cloud-no-tmux
+
+This is the same as the [`cloud` image](#sec-cloud) but without tmux.
+
+#### Image
+
+```
+axolotlai/axolotl-cloud-term
+```
+
+Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl-cloud-term)
+
+:::{.callout-note}
+
+The naming may be a bit confusing as it has `-term` appended to the end.
+
+:::
+
+#### Tags format
+
+This uses the same tags as the [`cloud` image](#sec-cloud-tags).

docs/faq.qmd

@@ -19,7 +19,9 @@ description: Frequently asked questions
 
 **Q: AttributeError: 'DummyOptim' object has no attribute 'step'**
 
-> A: You may be using deepspeed with single gpu. Please don't set `deepspeed:` in yaml or cli.
+**Q: ModuleNotFoundError: No module named 'mpi4py' using single GPU with deepspeed**
+
+> A: You may be using deepspeed with single gpu. Please remove the `deepspeed:` section in the yaml file or `--deepspeed` CLI flag.
 
 **Q: The codes is stuck on saving preprocessed datasets.**
 
@@ -50,3 +52,7 @@ description: Frequently asked questions
 **Q: The EOS/EOT token is incorrectly being masked or not being masked.**
 
 > A: This is because of the mismatch between `tokenizer.eos_token` and EOS/EOT token in template. Please make sure to set `eos_token` under `special_tokens` to the same EOS/EOT token as in template.
+
+**Q: "`chat_template` choice is `tokenizer_default` but tokenizer's `chat_template` is null. Please add a `chat_template` in tokenizer config"**
+
+> A: This is because the tokenizer does not have a chat template. Please add a chat template in the tokenizer config. See [chat_template](dataset-formats/conversation.qmd#chat-template) for more details.

docs/installation.qmd

@@ -65,6 +65,8 @@ docker run --privileged --gpus '"all"' --shm-size 10g --rm -it \
 ```
 :::
 
+Please refer to the [Docker documentation](docker.qmd) for more information on the different Docker images that are available.
+
 ## Cloud Environments {#sec-cloud}
 
 ### Cloud GPU Providers {#sec-cloud-gpu}

docs/reward_modelling.qmd

@@ -28,6 +28,17 @@ val_set_size: 0.1
 eval_steps: 100
 ```
 
+Bradley-Terry chat templates expect single-turn conversations in the following format:
+
+```json
+{
+    "system": "...", // optional
+    "input": "...",
+    "chosen": "...",
+    "rejected": "..."
+}
+```
+
 ### Process Reward Models (PRM)
 
 Process reward models are trained using data which contains preference annotations for each step in a series of interactions. Typically, PRMs are trained to provide reward signals over each step of a reasoning trace and are used for downstream reinforcement learning.
@@ -45,3 +56,5 @@ datasets:
 val_set_size: 0.1
 eval_steps: 100
 ```
+
+Please see [stepwise_supervised](dataset-formats/stepwise_supervised.qmd) for more details on the dataset format.

docs/rlhf.qmd

@@ -3,6 +3,7 @@ title: "RLHF (Beta)"
 description: "Reinforcement Learning from Human Feedback is a method whereby a language model is optimized from data using human feedback."
 back-to-top-navigation: true
 toc: true
+toc-expand: 2
 toc-depth: 4
 ---
 
@@ -528,6 +529,7 @@ trl:
     vllm_gpu_memory_utilization: 0.15
     num_generations: 4
     reward_funcs: ["rewards.rand_reward_func"]    # format: '{file_name}.{fn_name}'
+    reward_weights: [1.0]
 datasets:
   - path: openai/gsm8k
     name: main
@@ -536,6 +538,8 @@ datasets:
 
 To see other examples of custom reward functions, please see [TRL GRPO Docs](https://github.com/huggingface/trl/blob/main/docs/source/grpo_trainer.md#using-a-custom-reward-function).
 
+To see description of the configs, please see [TRLConfig](https://github.com/axolotl-ai-cloud/axolotl/blob/main/src/axolotl/utils/config/models/input/v0_4_1/trl.py).
+
 ### Using local dataset files
 
 ```yaml

requirements.txt

@@ -62,4 +62,5 @@ antlr4-python3-runtime==4.13.2
 torchao==0.7.0
 schedulefree==1.3.0
 
-axolotl-contribs-lgpl==0.0.3
+axolotl-contribs-lgpl==0.0.6
+axolotl-contribs-mit==0.0.3

scripts/cutcrossentropy_install.py

@@ -24,5 +24,5 @@ if cce_spec:
 
 print(
     UNINSTALL_PREFIX
-    + 'pip install "cut-cross-entropy @ git+https://github.com/apple/ml-cross-entropy.git@9c297c905f55b73594b5d650722d1e78183b77bd"'
+    + 'pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"'
 )

src/axolotl/cli/cloud/modal_.py

@@ -113,7 +113,7 @@ class ModalCloud(Cloud):
                 [
                     # Random id for cache busting of branch commits
                     f"RUN echo '{str(randint(0, 1000000))}'",  # nosec B311
-                    f"RUN cd /workspace/axolotl && git fetch && git checkout {self.config.branch}",
+                    f"RUN cd /workspace/axolotl && git fetch && git checkout {self.config.branch} && git pull",
                 ]
             )
 
@@ -270,6 +270,7 @@ def _preprocess(config_yaml: str, volumes=None):
 
 
 def _train(config_yaml: str, accelerate: bool = True, volumes=None, **kwargs):
+    Path("/workspace/mounts").mkdir(parents=True, exist_ok=True)
     with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:
         f_out.write(config_yaml)
     run_folder = "/workspace/mounts"
@@ -288,6 +289,7 @@ def _train(config_yaml: str, accelerate: bool = True, volumes=None, **kwargs):
 
 
 def _lm_eval(config_yaml: str, volumes=None):
+    Path("/workspace/mounts").mkdir(parents=True, exist_ok=True)
     with open("/workspace/mounts/config.yaml", "w", encoding="utf-8") as f_out:
         f_out.write(config_yaml)
     run_folder = "/workspace/mounts"

src/axolotl/cli/train.py

@@ -1,6 +1,7 @@
 """CLI to run training on a model."""
 
 import logging
+import os
 from pathlib import Path
 from typing import Union
 
@@ -34,18 +35,20 @@ def do_train(cfg: DictDefault, cli_args: TrainerCliArgs) -> None:
     """
     print_axolotl_text_art()
     check_accelerate_default_config()
-    check_user_token()
+    if int(os.getenv("LOCAL_RANK", "0")) == 0:
+        check_user_token()
 
     if cfg.rl:
         dataset_meta = load_preference_datasets(cfg=cfg, cli_args=cli_args)
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-    model, tokenizer = train(cfg=cfg, dataset_meta=dataset_meta)
+    model, tokenizer, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
     plugin_manager = PluginManager.get_instance()
 
     del model
     del tokenizer
+    del trainer
 
     plugin_manager.post_train_unload(cfg)
 

src/axolotl/common/datasets.py

@@ -24,8 +24,8 @@ class TrainDatasetMeta:
     """Dataclass with fields for training and validation datasets and metadata."""
 
     train_dataset: Dataset
-    eval_dataset: Optional[Dataset] = None
-    total_num_steps: Optional[int] = None
+    eval_dataset: Dataset | None = None
+    total_num_steps: int | None = None
 
 
 def sample_dataset(dataset: Dataset, num_samples: int) -> Dataset:

src/axolotl/core/datasets/chat.py

@@ -43,7 +43,7 @@ class TokenizedChatDataset(Dataset):
         process_or_cpu_count: int = (
             process_count or os.cpu_count()  # type: ignore[assignment]
         )
-        num_proc = min(64, process_or_cpu_count)
+        num_proc = min(32, process_or_cpu_count)
         features = data.features.keys()
         tokenized_data = data.map(
             map_fn,

src/axolotl/core/trainer_builder.py

@@ -35,11 +35,11 @@ from transformers import (
     EarlyStoppingCallback,
     TrainerCallback,
 )
+from transformers.training_args import OptimizerNames
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
 from axolotl.core.trainers.base import (
     AxolotlCPOTrainer,
-    AxolotlKTOTrainer,
     AxolotlMambaTrainer,
     AxolotlORPOTrainer,
     AxolotlPRMTrainer,
@@ -50,6 +50,7 @@ from axolotl.core.trainers.base import (
 from axolotl.core.trainers.dpo import DPOStrategy
 from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
 from axolotl.core.trainers.grpo import GRPOStrategy
+from axolotl.core.trainers.kto import AxolotlKTOTrainer
 from axolotl.core.training_args import (
     AxolotlCPOConfig,
     AxolotlKTOConfig,
@@ -84,6 +85,7 @@ from axolotl.utils.collators import (
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
+from axolotl.utils.config.models.input.v0_4_1 import CustomSupportedOptimizers
 from axolotl.utils.models import ensure_dtype
 
 try:
@@ -91,13 +93,11 @@ try:
 except ImportError:
     pass
 
-LOG = logging.getLogger("axolotl.core.trainer_builder")
+LOG = logging.getLogger(__name__)
 
 
 class TrainerBuilderBase(abc.ABC):
-    """
-    Base class for trainer builder
-    """
+    """Base class for trainer builder."""
 
     _train_dataset = None
     _eval_dataset = None
@@ -110,9 +110,9 @@ class TrainerBuilderBase(abc.ABC):
         self.tokenizer = tokenizer
         self.processor = processor
 
-        # in case the model supports tagging, add the axolotl tag.
+        # If the model supports tagging, add the axolotl tag.
         # This makes sure the tag is correctly pushed even if a user calls
-        # model.push_to_hub instad of  trainer.push_to_hub.
+        # model.push_to_hub instead of trainer.push_to_hub.
         if hasattr(model, "add_model_tags"):
             model.add_model_tags(["axolotl"])
 
@@ -227,8 +227,8 @@ class TrainerBuilderBase(abc.ABC):
 
 class HFCausalTrainerBuilder(TrainerBuilderBase):
     """
-    Build the HuggingFace training args/trainer for causal models
-    and reward modelling using TRL.
+    Build the HuggingFace training args/trainer for causal models and reward modeling
+    using TRL.
     """
 
     def get_callbacks(self):
@@ -551,30 +551,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             training_arguments_kwargs["run_name"] = self.cfg.mlflow_run_name
         else:
             training_arguments_kwargs["run_name"] = None
-        training_arguments_kwargs["optim"] = (
-            self.cfg.optimizer if self.cfg.optimizer else "adamw_hf"
-        )
-        if self.cfg.optim_args:
-            if isinstance(self.cfg.optim_args, dict):
-                optim_args = ",".join(
-                    [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
-                )
-            else:
-                optim_args = self.cfg.optim_args
-            training_arguments_kwargs["optim_args"] = optim_args
-        if self.cfg.optim_target_modules:
-            training_arguments_kwargs[
-                "optim_target_modules"
-            ] = self.cfg.optim_target_modules
-        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
-        training_arguments_kwargs[
-            "loraplus_lr_embedding"
-        ] = self.cfg.loraplus_lr_embedding
-        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
-        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
-        training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
 
-        if self.cfg.lr_scheduler in ["one_cycle", "log_sweep"]:
+        if self.cfg.lr_scheduler in ["one_cycle", "rex", "log_sweep"]:
             training_arguments_kwargs["lr_scheduler_type"] = "cosine"
             training_arguments_kwargs[
                 "alternate_lr_scheduler_type"
@@ -658,46 +636,114 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         if self.cfg.reward_model:
             training_arguments_kwargs["max_length"] = self.cfg.sequence_len
 
-        # pylint: disable=duplicate-code
-        if self.cfg.optimizer in [
-            "optimi_adamw",
-            "ao_adamw_4bit",
-            "ao_adamw_8bit",
-            "ao_adamw_fp8",
-            "adopt_adamw",
-        ]:
-            # Set default so transformers doesn't throw
-            training_arguments_kwargs["optim"] = "adamw_hf"
-            training_arguments_kwargs["alternate_optimizer"] = self.cfg.optimizer
+        # Handle custom optimizer
+        custom_supported_optimizers = [opt.value for opt in CustomSupportedOptimizers]
+        if self.cfg.optimizer in custom_supported_optimizers:
+            # Common optimizer kwargs
+            optimizer_kwargs = {
+                "lr": training_arguments_kwargs.get("learning_rate"),
+                "weight_decay": training_arguments_kwargs.get("weight_decay"),
+            }
 
-        if self.cfg.optimizer == "lion_pytorch":
-            from lion_pytorch import Lion
+            # Adam-specific kwargs
+            adam_kwargs = {}
+            if training_arguments_kwargs.get(
+                "adam_beta1"
+            ) and training_arguments_kwargs.get("adam_beta2"):
+                adam_kwargs["betas"] = (
+                    training_arguments_kwargs.get("adam_beta1"),
+                    training_arguments_kwargs.get("adam_beta2"),
+                )
+            if training_arguments_kwargs.get("adam_epsilon"):
+                adam_kwargs["eps"] = training_arguments_kwargs.get("adam_epsilon")
 
-            lion_kwargs = {"lr": training_arguments_kwargs["learning_rate"]}
-            if "weight_decay" in training_arguments_kwargs:
-                lion_kwargs["weight_decay"] = training_arguments_kwargs["weight_decay"]
-
-            if (
-                "adam_beta1" in training_arguments_kwargs
-                and "adam_beta2" in training_arguments_kwargs
-            ):
-                lion_kwargs["betas"] = (
-                    training_arguments_kwargs["adam_beta1"],
-                    training_arguments_kwargs["adam_beta2"],
+            if self.cfg.optimizer == "muon":
+                from axolotl.contribs.mit.muon import (  # pylint: disable=no-name-in-module
+                    MuonOptimizerFactory,
                 )
 
-            trainer_kwargs["optimizers"] = (
-                Lion(params=self.model.parameters(), **lion_kwargs),
-                None,
+                optimizer_cls = MuonOptimizerFactory
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "optimi_adamw":
+                from optimi import AdamW
+
+                optimizer_kwargs["foreach"] = False
+                optimizer_cls = AdamW
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "ao_adamw_4bit":
+                # TODO remove 20250401
+                from torchao.prototype.low_bit_optim import AdamW4bit
+
+                optimizer_cls = AdamW4bit
+                optimizer_kwargs.update(adam_kwargs)
+
+                LOG.warning(
+                    f"`ao_adamw_4bit` will be deprecated soon. Please use `{OptimizerNames.ADAMW_TORCH_4BIT}` instead."
+                )
+            elif self.cfg.optimizer == "ao_adamw_8bit":
+                from torchao.prototype.low_bit_optim import AdamW8bit
+
+                optimizer_cls = AdamW8bit
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "ao_adamw_fp8":
+                from torchao.prototype.low_bit_optim import AdamWFp8
+
+                optimizer_cls = AdamWFp8
+                optimizer_kwargs.update(adam_kwargs)
+            elif self.cfg.optimizer == "adopt_adamw":
+                from axolotl.utils.optimizers.adopt import ADOPT
+
+                optimizer_cls = ADOPT
+                adam_kwargs["decouple"] = True
+                optimizer_kwargs.update(adam_kwargs)
+
+            # Parse any additional optimizer args from config
+            if self.cfg.optim_args:
+                if isinstance(self.cfg.optim_args, dict):
+                    optimizer_kwargs.update(self.cfg.optim_args)
+                else:
+                    # Parse string format "key1=value1,key2=value2"
+                    for mapping in self.cfg.optim_args.replace(" ", "").split(","):
+                        key, value = mapping.split("=")
+                        optimizer_kwargs[key] = value
+
+            trainer_kwargs["optimizer_cls_and_kwargs"] = (
+                optimizer_cls,
+                optimizer_kwargs,
             )
-            # Set default so transformers doesn't throw
-            training_arguments_kwargs["optim"] = "adamw_hf"
+        else:
+            # Use transformers' optimizer
+            training_arguments_kwargs["optim"] = self.cfg.optimizer
+
+            # Parse any additional optimizer args from config
+            if self.cfg.optim_args:
+                if isinstance(self.cfg.optim_args, dict):
+                    optim_args = ",".join(
+                        [f"{key}={value}" for key, value in self.cfg.optim_args.items()]
+                    )
+                else:
+                    optim_args = self.cfg.optim_args
+                training_arguments_kwargs["optim_args"] = optim_args
 
         if self.cfg.optimizer == "adamw_anyprecision":
             if Path(self.cfg.torchdistx_path).exists():
                 sys.path.append(self.cfg.torchdistx_path)
                 importlib.import_module("torchdistx")
 
+        if self.cfg.optim_target_modules:
+            training_arguments_kwargs[
+                "optim_target_modules"
+            ] = self.cfg.optim_target_modules
+
+        training_arguments_kwargs["embedding_lr"] = self.cfg.embedding_lr
+        training_arguments_kwargs["embedding_lr_scale"] = self.cfg.embedding_lr_scale
+
+        training_arguments_kwargs["loraplus_lr_ratio"] = self.cfg.loraplus_lr_ratio
+        training_arguments_kwargs[
+            "loraplus_lr_embedding"
+        ] = self.cfg.loraplus_lr_embedding
+        training_arguments_kwargs["lr_groups"] = self.cfg.lr_groups
+
         if self.cfg.accelerator_config:
             training_arguments_kwargs[
                 "accelerator_config"
@@ -872,9 +918,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
 
 
 class HFRLTrainerBuilder(TrainerBuilderBase):
-    """
-    Trainer factory class for TRL-based RLHF trainers (e.g. DPO)
-    """
+    """Trainer factory class for TRL-based RLHF trainers (e.g. DPO)"""
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()

src/axolotl/core/trainers/base.py

@@ -14,17 +14,21 @@ from typing import Dict, Literal, Optional
 import torch
 from datasets import Dataset
 from peft.optimizers import create_loraplus_optimizer
+from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import Trainer
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
 from transformers.utils import is_sagemaker_mp_enabled
-from trl import CPOTrainer, KTOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
+from trl import CPOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
 from trl.trainer.utils import pad_to_length
 
+from axolotl.core.trainers.kto import AxolotlKTOTrainer
+from axolotl.integrations.base import BaseOptimizerFactory
 from axolotl.monkeypatch.relora import ReLoRAScheduler
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
+    RexLR,
     get_cosine_schedule_with_min_lr,
     get_cosine_schedule_with_quadratic_warmup,
     get_cosine_schedule_with_warmup_decay_constant,
@@ -115,6 +119,17 @@ class SchedulerMixin(Trainer):
                     **extra_lr_kwargs,
                     **self.args.lr_scheduler_kwargs,
                 )
+            elif self.args.alternate_lr_scheduler_type == "rex":
+                if use_cosine_min_lr:
+                    assert 0 <= self.args.cosine_min_lr_ratio <= 1.0, "cosine_min_lr_ratio must be between 0.0 and 1.0"
+
+                self.lr_scheduler = RexLR(
+                    optimizer=optimizer,
+                    max_lr=self.args.learning_rate,
+                    min_lr=0 if not use_cosine_min_lr else (self.args.learning_rate * self.args.cosine_min_lr_ratio),
+                    total_steps=num_training_steps,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                )
             elif use_cosine_quadratic:
                 if use_cosine_min_lr:
                     LOG.warning("Both cosine quadratic warmup and min lr detected. Using quadratic warmup.")
@@ -154,47 +169,18 @@ class SchedulerMixin(Trainer):
         return self.lr_scheduler
 
 
-class AxolotlTrainer(SchedulerMixin, Trainer):
+class OptimizerMixin(Trainer):
     """
-    Extend the base Trainer for axolotl helpers
+    Mixin class for shared handling of building custom optimizers
     """
 
     args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
-    tag_names = ["axolotl"]
 
-    def __init__(
-        self,
-        *_args,
-        bench_data_collator=None,
-        eval_data_collator=None,
-        dataset_tags=None,
-        **kwargs,
-    ):
-        self.bench_data_collator = bench_data_collator
-        self.eval_data_collator = eval_data_collator
-        self.dataset_tags = dataset_tags
-        self._signature_columns = None  # workaround for pylint
-        super().__init__(*_args, **kwargs)
-        self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(lambda: defaultdict(list))
-        if self.args.orpo_alpha:
-            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
-
-    def _wrap_model(self, model, training=True, dataloader=None):
-        if self.args.torch_compile:
-            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
-                256
-            )
-            model = torch.compile(
-                model,
-                backend=self.args.torch_compile_backend,
-                mode=self.args.torch_compile_mode,
-            )
-        return super()._wrap_model(model, training=training, dataloader=dataloader)
-
-    def create_optimizer_grouped_parameters(self, opt_model, optimizer_kwargs):
+    def create_optimizer_grouped_parameters(
+        self, opt_model, optimizer_kwargs
+    ) -> list[dict]:
         decay_parameters = self.get_decay_parameter_names(opt_model)
-        params = {
+        params: dict = {
             "to_weight_decay": {},  # LayerNorm and bias
             "embeddings": {},  # lm_head, embed_tokens,
             "no_weight_decay": {},
@@ -281,23 +267,30 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
             and self.args.embedding_lr_scale is None
             and self.args.embedding_lr is None
             and self.args.lr_groups is None
-            and self.args.alternate_optimizer
-            not in [
-                "optimi_adamw",
-                "ao_adamw_8bit",
-                "ao_adamw_4bit",
-                "ao_adamw_fp8",
-                "adopt_adamw",
-            ]
+            and self.optimizer_cls_and_kwargs is None
         ):
             return super().create_optimizer()
 
         opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-        if self.optimizer is None:  # pylint: disable=access-member-before-definition
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
-                self.args,
-                opt_model,
+
+        if (
+            not self.optimizer
+            and self.optimizer_cls_and_kwargs is not None
+            and issubclass(self.optimizer_cls_and_kwargs[0], BaseOptimizerFactory)
+        ):
+            optimizer_factory_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            self.optimizer = optimizer_factory_cls()(
+                opt_model, self.args, **optimizer_kwargs
             )
+
+        if not self.optimizer:
+            if self.optimizer_cls_and_kwargs is not None:
+                optimizer_cls, optimizer_kwargs = self.optimizer_cls_and_kwargs
+            else:
+                optimizer_cls, optimizer_kwargs = self.get_optimizer_cls_and_kwargs(
+                    self.args, opt_model
+                )
+
             optimizer_grouped_parameters = self.create_optimizer_grouped_parameters(
                 opt_model, optimizer_kwargs
             )
@@ -314,50 +307,47 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
                     loraplus_lr_embedding=loraplus_lr_embedding,
                     **optimizer_kwargs,
                 )
-            elif (
-                self.args.embedding_lr_scale is not None
-                or self.args.embedding_lr is not None
-                or self.args.lr_groups is not None
-            ):
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "optimi_adamw":
-                from optimi import AdamW
+            else:
+                # Overwrite `params` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for GaLore optimizer.
+                if "params" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("params")
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamW(
-                        optimizer_grouped_parameters, foreach=False, **optimizer_kwargs
+                # Overwrite `model` in case it's created by `get_optimizer_cls_and_kwargs`
+                # e.g. for LOMO optimizer.
+                if "model" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop("model")
+
+                # For layer-wise dummy optimizers we overwrite optimizer_grouped_parameters with `optimizer_dict`
+                # to avoid arguments conflicts.
+                if "optimizer_dict" in optimizer_kwargs:
+                    optimizer_grouped_parameters = optimizer_kwargs.pop(
+                        "optimizer_dict"
                     )
-                )
-            elif self.args.alternate_optimizer == "ao_adamw_4bit":
-                from torchao.prototype.low_bit_optim import AdamW4bit
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamW4bit(optimizer_grouped_parameters, **optimizer_kwargs)
+                self.optimizer = optimizer_cls(
+                    optimizer_grouped_parameters, **optimizer_kwargs
                 )
-            elif self.args.alternate_optimizer == "ao_adamw_8bit":
-                from torchao.prototype.low_bit_optim import AdamW8bit
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamW8bit(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "ao_adamw_fp8":
-                from torchao.prototype.low_bit_optim import AdamWFp8
+            if optimizer_cls.__name__ == "Adam8bit":
+                import bitsandbytes
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    AdamWFp8(optimizer_grouped_parameters, **optimizer_kwargs)
-                )
-            elif self.args.alternate_optimizer == "adopt_adamw":
-                from axolotl.utils.optimizers.adopt import ADOPT
+                manager = bitsandbytes.optim.GlobalOptimManager.get_instance()
 
-                self.optimizer = (  # pylint: disable=attribute-defined-outside-init
-                    ADOPT(
-                        optimizer_grouped_parameters,
-                        decouple=True,
-                        **optimizer_kwargs,
-                    )
-                )
+                skipped = 0
+                for module in opt_model.modules():
+                    if isinstance(module, nn.Embedding):
+                        skipped += sum(
+                            {
+                                p.data_ptr(): p.numel() for p in module.parameters()
+                            }.values()
+                        )
+                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        manager.register_module_override(
+                            module, "weight", {"optim_bits": 32}
+                        )
+                        LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
+                LOG.info(f"skipped: {skipped/2**20}M params")
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -366,6 +356,45 @@ class AxolotlTrainer(SchedulerMixin, Trainer):
 
         return self.optimizer
 
+
+class AxolotlTrainer(SchedulerMixin, OptimizerMixin, Trainer):
+    """
+    Extend the base Trainer for axolotl helpers
+    """
+
+    args = None  # type: "AxolotlTrainingArguments"  # type: ignore[name-defined]
+    tag_names = ["axolotl"]
+
+    def __init__(
+        self,
+        *_args,
+        bench_data_collator=None,
+        eval_data_collator=None,
+        dataset_tags=None,
+        **kwargs,
+    ):
+        self.bench_data_collator = bench_data_collator
+        self.eval_data_collator = eval_data_collator
+        self.dataset_tags = dataset_tags
+        self._signature_columns = None  # workaround for pylint
+        super().__init__(*_args, **kwargs)
+        self.train_data_collator = self.data_collator
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+        if self.args.orpo_alpha:
+            self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+
+    def _wrap_model(self, model, training=True, dataloader=None):
+        if self.args.torch_compile:
+            torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access
+                256
+            )
+            model = torch.compile(
+                model,
+                backend=self.args.torch_compile_backend,
+                mode=self.args.torch_compile_mode,
+            )
+        return super()._wrap_model(model, training=training, dataloader=dataloader)
+
     def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
         if self.args.sample_packing and not self.args.pretraining:
             if self.args.multipack_real_batches:
@@ -846,14 +875,6 @@ class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     tag_names = ["axolotl", "orpo"]
 
 
-class AxolotlKTOTrainer(SchedulerMixin, KTOTrainer):
-    """
-    Extend the base KTOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "kto"]
-
-
 class AxolotlCPOTrainer(SchedulerMixin, CPOTrainer):
     """
     Extend the base CPOTrainer for axolotl helpers

src/axolotl/core/trainers/grpo/__init__.py

@@ -9,6 +9,7 @@ import logging
 from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
+from axolotl.utils.config.models.input.v0_4_1.trl import TRLConfig
 
 LOG = logging.getLogger("axolotl")
 
@@ -31,30 +32,44 @@ class GRPOStrategy:
     @classmethod
     def set_training_args_kwargs(cls, cfg):
         grpo_args_kwargs = {}
-        if cfg.trl and cfg.trl.use_vllm:
-            grpo_args_kwargs["use_vllm"] = cfg.trl.use_vllm
-            if cfg.trl and cfg.trl.vllm_device:
-                grpo_args_kwargs["vllm_device"] = cfg.trl.vllm_device
-            else:
-                grpo_args_kwargs["vllm_device"] = "auto"
-            if cfg.trl and cfg.trl.vllm_gpu_memory_utilization:
+
+        if not hasattr(cfg, "trl") or not cfg.trl:
+            return grpo_args_kwargs
+
+        trl: TRLConfig = cfg.trl  # type: ignore
+
+        if trl.use_vllm:
+            grpo_args_kwargs["use_vllm"] = trl.use_vllm
+            grpo_args_kwargs["vllm_device"] = (
+                trl.vllm_device if trl.vllm_device else "auto"
+            )
+
+            if trl.vllm_gpu_memory_utilization:
                 grpo_args_kwargs[
                     "vllm_gpu_memory_utilization"
-                ] = cfg.trl.vllm_gpu_memory_utilization
-            if cfg.trl and cfg.trl.vllm_max_model_len:
-                grpo_args_kwargs["vllm_max_model_len"] = cfg.trl.vllm_max_model_len
-        if cfg.trl and cfg.trl.num_generations:
-            grpo_args_kwargs["num_generations"] = cfg.trl.num_generations
-        if cfg.trl and cfg.trl.sync_ref_model:
-            grpo_args_kwargs["sync_ref_model"] = cfg.trl.sync_ref_model
-            if cfg.trl and cfg.trl.ref_model_mixup_alpha:
-                grpo_args_kwargs[
-                    "ref_model_mixup_alpha"
-                ] = cfg.trl.ref_model_mixup_alpha
-            if cfg.trl and cfg.trl.ref_model_sync_steps:
-                grpo_args_kwargs["ref_model_sync_steps"] = cfg.trl.ref_model_sync_steps
-        grpo_args_kwargs["max_completion_length"] = cfg.trl.max_completion_length
-        grpo_args_kwargs["log_completions"] = cfg.trl.log_completions
+                ] = trl.vllm_gpu_memory_utilization
+
+            if trl.vllm_max_model_len:
+                grpo_args_kwargs["vllm_max_model_len"] = trl.vllm_max_model_len
+
+        if trl.num_generations:
+            grpo_args_kwargs["num_generations"] = trl.num_generations
+
+        if trl.sync_ref_model:
+            grpo_args_kwargs["sync_ref_model"] = trl.sync_ref_model
+
+            if trl.ref_model_mixup_alpha:
+                grpo_args_kwargs["ref_model_mixup_alpha"] = trl.ref_model_mixup_alpha
+
+            if trl.ref_model_sync_steps:
+                grpo_args_kwargs["ref_model_sync_steps"] = trl.ref_model_sync_steps
+
+        grpo_args_kwargs["max_completion_length"] = trl.max_completion_length
+        grpo_args_kwargs["log_completions"] = trl.log_completions
+
+        if trl.reward_weights:
+            grpo_args_kwargs["reward_weights"] = trl.reward_weights
+
         return grpo_args_kwargs
 
     @classmethod

src/axolotl/core/trainers/kto/__init__.py

@@ -0,0 +1,7 @@
+"""
+KTO package initialization.
+"""
+
+from axolotl.core.trainers.kto.trainer import AxolotlKTOTrainer
+
+__all__ = ["AxolotlKTOTrainer"]

src/axolotl/core/trainers/kto/trainer.py

@@ -0,0 +1,512 @@
+"""
+KTO trainer implementation for Axolotl.
+"""
+
+from __future__ import annotations
+
+import inspect
+import logging
+import warnings
+from contextlib import nullcontext
+from typing import Any, Callable, Literal, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from datasets import Dataset
+from torch.utils.data import DataLoader, SequentialSampler
+from transformers import (
+    AutoModelForCausalLM,
+    BaseImageProcessor,
+    DataCollator,
+    FeatureExtractionMixin,
+    PreTrainedModel,
+    PreTrainedTokenizerBase,
+    ProcessorMixin,
+    Trainer,
+    TrainerCallback,
+    TrainingArguments,
+)
+from transformers.trainer_utils import EvalLoopOutput
+from trl import KTOTrainer
+from trl.trainer.kto_config import KTOConfig
+from trl.trainer.utils import KTODataCollatorWithPadding, pad_to_length
+
+from axolotl.core.trainers.base import SchedulerMixin
+
+# Check if PEFT is available
+try:
+    from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training, peft_module_casting_to_bf16
+    is_peft_available = True
+except ImportError:
+    is_peft_available = False
+
+LOG = logging.getLogger("axolotl.core.trainers.kto")
+
+
+class AxolotlKTOTrainer(SchedulerMixin, Trainer):
+    """
+    Extend the base KTOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "kto"]
+
+    def __init__(
+        self,
+        model: Union[PreTrainedModel, nn.Module, str] = None,
+        args: KTOConfig = None,
+        train_dataset: Optional[Dataset] = None,
+        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
+        processing_class: Optional[
+            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
+        ] = None,
+        data_collator: Optional[DataCollator] = None,
+        model_init: Optional[Callable[[], PreTrainedModel]] = None,
+        callbacks: Optional[list[TrainerCallback]] = None,
+        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
+        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
+        peft_config: Optional[dict] = None,
+        compute_metrics: Optional[Callable[[EvalLoopOutput], dict]] = None,
+        dataset_tags=None,
+        model_adapter_name: Optional[str] = None,
+        ref_adapter_name: Optional[str] = None,
+    ):
+        self.dataset_tags = dataset_tags
+        self._tag_names = ["trl", "kto"]
+        if hasattr(self, "tag_names"):
+            self._tag_names.extend(self.tag_names)
+
+        if type(args) is TrainingArguments:
+            raise ValueError("Please use `KTOConfig` instead TrainingArguments.")
+
+        if args.model_init_kwargs is None:
+            model_init_kwargs = {}
+        elif not isinstance(model, str):
+            raise ValueError("You passed model_kwargs to the KTOTrainer. But your model is already instantiated.")
+        else:
+            model_init_kwargs = args.model_init_kwargs
+            torch_dtype = model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if args.ref_model_init_kwargs is None:
+            ref_model_init_kwargs = {}
+        elif not isinstance(ref_model, str):
+            raise ValueError(
+                "You passed ref_model_kwargs to the KTOTrainer. But your ref_model is already instantiated."
+            )
+        else:
+            ref_model_init_kwargs = args.ref_model_init_kwargs
+            torch_dtype = ref_model_init_kwargs.get("torch_dtype")
+            if torch_dtype is not None:
+                # Convert to `torch.dtype` if an str is passed
+                if isinstance(torch_dtype, str) and torch_dtype != "auto":
+                    torch_dtype = getattr(torch, torch_dtype)
+                if torch_dtype != "auto" and not isinstance(torch_dtype, torch.dtype):
+                    raise ValueError(
+                        f"Invalid `torch_dtype` passed to the KTOConfig. Expected a string with either `torch.dtype` or 'auto', but got {torch_dtype}."
+                    )
+                ref_model_init_kwargs["torch_dtype"] = torch_dtype
+
+        if isinstance(model, str):
+            model = AutoModelForCausalLM.from_pretrained(model, **model_init_kwargs)
+
+        if isinstance(ref_model, str):
+            ref_model = AutoModelForCausalLM.from_pretrained(ref_model, **ref_model_init_kwargs)
+
+        # Initialize this variable to False. This helps tracking the case when `peft_module_casting_to_bf16`
+        # has been called in order to properly call autocast if needed.
+        self._peft_has_been_casted_to_bf16 = False
+
+        if not is_peft_available() and peft_config is not None:
+            raise ValueError(
+                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it with `pip install peft` to use the PEFT models"
+            )
+        elif is_peft_available() and peft_config is not None:
+            # if model is a peft model and we have a peft_config, we merge and unload it first
+            if isinstance(model, PeftModel):
+                model = model.merge_and_unload()
+
+            if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False):
+                _support_gc_kwargs = hasattr(
+                    args, "gradient_checkpointing_kwargs"
+                ) and "gradient_checkpointing_kwargs" in list(
+                    inspect.signature(prepare_model_for_kbit_training).parameters
+                )
+
+                prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
+
+                if _support_gc_kwargs:
+                    prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
+
+                model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)
+            elif getattr(args, "gradient_checkpointing", False):
+                # For backward compatibility with older versions of transformers
+                if hasattr(model, "enable_input_require_grads"):
+                    model.enable_input_require_grads()
+                else:
+
+                    def make_inputs_require_grad(module, input, output):
+                        output.requires_grad_(True)
+
+                    model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+            # get peft model with the given config
+            model = get_peft_model(model, peft_config)
+            if args.bf16 and getattr(model, "is_loaded_in_4bit", False):
+                peft_module_casting_to_bf16(model)
+                # If args.bf16 we need to explicitly call `generate` with torch amp autocast context manager
+                self._peft_has_been_casted_to_bf16 = True
+
+        # For models that use gradient_checkpointing, we need to attach a hook that enables input
+        # to explicitly have `requires_grad=True`, otherwise training will either silently
+        # fail or completely fail.
+        elif getattr(args, "gradient_checkpointing", False):
+            # For backward compatibility with older versions of transformers
+            if hasattr(model, "enable_input_require_grads"):
+                model.enable_input_require_grads()
+            else:
+
+                def make_inputs_require_grad(module, input, output):
+                    output.requires_grad_(True)
+
+                model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
+
+        if args.generate_during_eval and not (is_wandb_available() or is_comet_available()):
+            raise ValueError(
+                "`generate_during_eval=True` requires Weights and Biases or Comet to be installed."
+                " Please install `wandb` or `comet-ml` to resolve."
+            )
+
+        if model is not None:
+            self.is_encoder_decoder = model.config.is_encoder_decoder
+        elif args.is_encoder_decoder is None:
+            raise ValueError("When no model is provided, you need to pass the parameter is_encoder_decoder.")
+        else:
+            self.is_encoder_decoder = args.is_encoder_decoder
+
+        self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
+        self.model_adapter_name = model_adapter_name
+        self.ref_adapter_name = ref_adapter_name
+
+        if ref_model:
+            self.ref_model = ref_model
+        elif self.is_peft_model or args.precompute_ref_log_probs:
+            # The `model` with adapters turned off will be used as the reference model
+            self.ref_model = None
+        else:
+            self.ref_model = create_reference_model(model)
+
+        if processing_class is None:
+            raise ValueError(
+                "max_length or a processing_class must be specified when using the default DPODataCollatorWithPadding"
+            )
+        if args.max_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_length` in the KTOTrainer's init"
+                " it will be set to `512` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_length = 512
+        if args.max_length is not None:
+            max_length = args.max_length
+
+        if args.max_prompt_length is None:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding, you should set `max_prompt_length` in the KTOTrainer's init"
+                " it will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_prompt_length = 128
+        if args.max_prompt_length is not None:
+            max_prompt_length = args.max_prompt_length
+
+        max_completion_length = None
+        if args.max_completion_length is None and self.is_encoder_decoder:
+            warnings.warn(
+                "When using DPODataCollatorWithPadding with an encoder decoder architecture, you should set `max_completion_length` in the KTOTrainer's init"
+                " it will be set to `128` by default, but you should do it yourself in the future.",
+                UserWarning,
+            )
+            max_completion_length = 128
+        if args.max_completion_length is not None and self.is_encoder_decoder:
+            max_completion_length = args.max_completion_length
+
+        if data_collator is None:
+            data_collator = DPODataCollatorWithPadding(
+                pad_token_id=processing_class.pad_token_id,
+                label_pad_token_id=args.label_pad_token_id,
+                is_encoder_decoder=self.is_encoder_decoder,
+            )
+
+            if args.remove_unused_columns:
+                args.remove_unused_columns = False
+                # warn users
+                warnings.warn(
+                    "When using DPODataCollatorWithPadding, you should set `remove_unused_columns=False` in your KTOConfig"
+                    " we have set it for you, but you should do it yourself in the future.",
+                    UserWarning,
+                )
+
+            self.use_dpo_data_collator = True
+        else:
+            self.use_dpo_data_collator = False
+
+        # Disable dropout in the model and reference model
+        if args.disable_dropout:
+            disable_dropout_in_model(model)
+            if self.ref_model is not None:
+                disable_dropout_in_model(self.ref_model)
+
+        self.loss_type = args.loss_type
+        self.max_length = max_length
+        self.generate_during_eval = args.generate_during_eval
+        self.label_pad_token_id = args.label_pad_token_id
+        self.padding_value = args.padding_value if args.padding_value is not None else processing_class.pad_token_id
+        self.max_prompt_length = max_prompt_length
+        self.truncation_mode = args.truncation_mode
+        self.max_completion_length = max_completion_length
+        self.processing_class = processing_class
+        self.precompute_ref_log_probs = args.precompute_ref_log_probs
+
+        # Not all losses require a KL calculation
+        self.calculate_KL = True
+        if self.loss_type in ["apo_zero_unpaired"]:
+            self.calculate_KL = False
+
+        # Since ref_logs are precomputed on the first call to get_train/eval_dataloader
+        # keep track of first called to avoid computation of future calls
+        self._precomputed_train_ref_log_probs = False
+        self._precomputed_eval_ref_log_probs = False
+
+        # metric
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
+
+        # KTO parameter
+        self.beta = args.beta
+        self.desirable_weight = args.desirable_weight
+        self.undesirable_weight = args.undesirable_weight
+        self.aux_loss_enabled = getattr(model.config, "output_router_logits", False)
+        self.aux_loss_coef = getattr(model.config, "router_aux_loss_coef", 0.0)
+        if self.aux_loss_enabled and self.aux_loss_coef == 0.0:
+            warnings.warn(
+                "You set `output_router_logits` to `True` in the model config, but `router_aux_loss_coef` is set to "
+                "`0.0`, meaning the auxiliary loss will not be used. Either set `router_aux_loss_coef` to a value "
+                "greater than `0.0`, or set `output_router_logits` to `False` if you don't want to use the auxiliary "
+                "loss.",
+                UserWarning,
+            )
+
+        # The trainer estimates the number of FLOPs (floating-point operations) using the number of elements in the
+        # input tensor associated with the key "input_ids". However, in KTO, the sampled data does not include the
+        # "input_ids" key. Instead, the available keys are "prompt_input_ids" and "completion_input_ids". As a result,
+        # the trainer issues the warning: "Could not estimate the number of tokens of the input, floating-point
+        # operations will not be computed." To suppress this warning, we set the "estimate_tokens" key in the model's
+        # "warnings_issued" dictionary to True. This acts as a flag to indicate that the warning has already been
+        # issued.
+        model.warnings_issued["estimate_tokens"] = True
+
+        # Compute that only on the main process for faster data processing.
+        # see: https://github.com/huggingface/trl/pull/1255
+        with PartialState().local_main_process_first():
+            # Extract the prompt if needed
+            train_dataset = train_dataset.map(
+                maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from train dataset"
+            )
+            # Unpair the dataset if needed
+            train_dataset = maybe_unpair_preference_dataset(
+                train_dataset, args.dataset_num_proc, desc="Unpairing train dataset"
+            )
+            # Apply the chat template if needed
+            train_dataset = train_dataset.map(
+                maybe_apply_chat_template,
+                fn_kwargs={"tokenizer": processing_class},
+                num_proc=args.dataset_num_proc,
+                desc="Applying chat template to train dataset",
+            )
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(
+                    maybe_extract_prompt, num_proc=args.dataset_num_proc, desc="Extracting prompt from eval dataset"
+                )
+                eval_dataset = maybe_unpair_preference_dataset(
+                    eval_dataset, args.dataset_num_proc, desc="Unpairing eval dataset"
+                )
+                eval_dataset = eval_dataset.map(
+                    maybe_apply_chat_template,
+                    fn_kwargs={"tokenizer": processing_class},
+                    num_proc=args.dataset_num_proc,
+                    desc="Applying chat template to eval dataset",
+                )
+
+            # Tokenize and prepare the training datasets
+            train_dataset = train_dataset.map(
+                _tokenize,
+                batched=True,
+                fn_kwargs={"tokenizer": self.processing_class},
+                num_proc=args.dataset_num_proc,
+                desc="Tokenizing train dataset",
+            )
+
+            fn_kwargs = {
+                "prefix": "",
+                "is_encoder_decoder": self.is_encoder_decoder,
+                "tokenizer": self.processing_class,
+                "max_length": self.max_length,
+                "truncation_mode": self.truncation_mode,
+                "label_pad_token_id": self.label_pad_token_id,
+                "max_prompt_length": self.max_prompt_length,
+                "max_completion_length": self.max_completion_length,
+            }
+
+            train_dataset = train_dataset.map(
+                _process_tokens,
+                fn_kwargs=fn_kwargs,
+                num_proc=args.dataset_num_proc,
+                desc="Processing tokenized train dataset",
+            )
+
+            # Tokenize and prepare the eval datasets
+            if eval_dataset is not None:
+                eval_dataset = eval_dataset.map(
+                    _tokenize,
+                    fn_kwargs={"tokenizer": self.processing_class},
+                    batched=True,
+                    num_proc=args.dataset_num_proc,
+                    desc="Tokenizing eval dataset",
+                )
+
+                eval_dataset = eval_dataset.map(
+                    _process_tokens,
+                    fn_kwargs=fn_kwargs,
+                    num_proc=args.dataset_num_proc,
+                    desc="Processing tokenized eval dataset",
+                )
+
+            # Get KL datasets if needed
+            if self.calculate_KL:
+                if args.per_device_train_batch_size <= 1:
+                    raise ValueError(
+                        "Actual (not effective) batch size must be > 1. KTO will not work properly because the KL term will be equivalent to the implied reward."
+                    )
+
+                # create pairs for estimating the KL term by flipping the matched pairs in each batch of size total_batch_size
+                # i.e., (x_1, y_1), ..., (x_n, y_n) --> (x_1, y_n), ..., (x_n, y_1) = (x'_1, y'_1), ..., (x'_n, y'_n)
+                train_kl_dataset = train_dataset.map(
+                    _get_kl_dataset,
+                    batched=True,
+                    batch_size=args.per_device_train_batch_size,
+                    num_proc=args.dataset_num_proc,
+                    desc="Extracting KL train dataset",
+                )
+
+                fn_kwargs["prefix"] = "KL_"
+                train_kl_dataset = train_kl_dataset.map(
+                    _process_tokens,
+                    fn_kwargs=fn_kwargs,
+                    num_proc=args.dataset_num_proc,
+                    remove_columns=[c for c in train_kl_dataset.column_names if c in train_dataset.column_names],
+                    desc="Processing tokenized train KL dataset",
+                )
+
+                # merge the datasets
+                train_dataset = concatenate_datasets([train_dataset, train_kl_dataset], axis=1)
+
+                if eval_dataset is not None:
+                    # Get KL dataset
+                    eval_kl_dataset = eval_dataset.map(
+                        _get_kl_dataset,
+                        batched=True,
+                        batch_size=args.per_device_train_batch_size,
+                        num_proc=args.dataset_num_proc,
+                        desc="Extracting eval KL dataset",
+                    )
+
+                    eval_kl_dataset = eval_kl_dataset.map(
+                        _process_tokens,
+                        fn_kwargs=fn_kwargs,
+                        num_proc=args.dataset_num_proc,
+                        remove_columns=[c for c in eval_kl_dataset.column_names if c in eval_dataset.column_names],
+                        desc="Processing tokenized eval KL dataset",
+                    )
+
+                    # merge the datasets
+                    eval_dataset = concatenate_datasets([eval_dataset, eval_kl_dataset], axis=1)
+
+            # calculate dataset desirability balance
+            num_desirable = max(sum(train_dataset["label"]), 1)
+            num_undesirable = max(len(train_dataset["label"]) - num_desirable, 1)  # "label" is binary
+
+            if num_desirable != num_undesirable:
+                # The lower and upper bounds come from Eq. (8) of https://huggingface.co/papers/2402.01306
+                des_weight_lower_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1, 2)
+                des_weight_upper_bound = round((num_undesirable * self.undesirable_weight / num_desirable) * 1.33, 2)
+                und_weight_lower_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1.33, 2)
+                und_weight_upper_bound = round((num_desirable * self.desirable_weight / num_undesirable) / 1, 2)
+
+                des_weight_in_range = des_weight_lower_bound <= self.desirable_weight <= des_weight_upper_bound
+                und_weight_in_range = und_weight_lower_bound <= self.undesirable_weight <= und_weight_upper_bound
+
+                if not (des_weight_in_range or und_weight_in_range):
+                    warnings.warn(
+                        "You have different amounts of desirable/positive and undesirable/negative examples but the "
+                        "weights on the desirable and undesirable losses don't seem to be in an ideal range. Based "
+                        f"on your data, we recommend EITHER "
+                        f"desirable_weight in [{des_weight_lower_bound}, {des_weight_upper_bound}] or "
+                        f"undesirable_weight in [{und_weight_lower_bound}, {und_weight_upper_bound}] (but NOT BOTH). "
+                        "See the documentation on how to optimally set these weights.",
+                        UserWarning,
+                    )
+
+        super().__init__(
+            model=model,
+            args=args,
+            data_collator=data_collator,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            processing_class=processing_class,
+            model_init=model_init,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
+        )
+
+        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
+        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
+        # self.model_accepts_loss_kwargs to False to enable scaling.
+        self.model_accepts_loss_kwargs = False
+
+        # Add tags for models that have been loaded with the correct transformers version
+        if hasattr(self.model, "add_model_tags"):
+            self.model.add_model_tags(self._tag_names)
+
+        if not hasattr(self, "accelerator"):
+            raise AttributeError(
+                "Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
+            )
+
+        # Deepspeed Zero-3 does not support precompute_ref_log_probs
+        if self.is_deepspeed_enabled:
+            if self.accelerator.state.deepspeed_plugin.zero_stage == 3 and self.precompute_ref_log_probs:
+                raise ValueError(
+                    "You cannot use `precompute_ref_log_probs=True` with Deepspeed ZeRO-3. Please set `precompute_ref_log_probs=False`."
+                )
+
+        if self.ref_model is None:
+            if not (self.is_peft_model or self.precompute_ref_log_probs):
+                raise ValueError(
+                    "No reference model and model is not a Peft model. Try setting `precompute_ref_log_probs=True`"
+                )
+        else:
+            if self.is_deepspeed_enabled:
+                self.ref_model = self._prepare_deepspeed(self.ref_model)
+            else:
+                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

src/axolotl/integrations/base.py

@@ -23,6 +23,8 @@ import importlib
 import logging
 from typing import OrderedDict
 
+import torch
+
 
 class BasePlugin:
     """
@@ -469,3 +471,14 @@ class PluginManager:
         """
         for plugin in self.plugins.values():
             plugin.post_train_unload(cfg)
+
+
+class BaseOptimizerFactory:
+    """
+    Base class for factories to create custom optimizers
+    """
+
+    def __call__(
+        self, opt_model, training_args, **optimizer_kwargs
+    ) -> "torch.optim.Optimizer":
+        pass

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -4,6 +4,22 @@ Cut Cross Entropy reduces VRAM usage through optimization on the cross-entropy o
 
 See https://github.com/apple/ml-cross-entropy
 
+## Requirements
+
+- PyTorch 2.4.0 or higher
+
+## Installation
+
+Run the following command to install `cut_cross_entropy[transformers]` if you don't have it already.
+
+```bash
+# if you are in dev environment
+python scripts/cutcrossentropy_install.py | sh
+
+# if you are not in dev environment
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"
+```
+
 ## Usage
 
 ```yaml

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -33,7 +33,7 @@ LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy")
 
 _CCE_INSTALL_MESSAGE = (
     "Please install cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers]==24.11.4"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"`'
 )
 
 

src/axolotl/integrations/spectrum/args.py

@@ -17,7 +17,7 @@ Module for handling Spectrum input arguments.
 """
 from typing import Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, model_validator
 
 
 class SpectrumArgs(BaseModel):
@@ -27,3 +27,20 @@ class SpectrumArgs(BaseModel):
 
     spectrum_top_fraction: Optional[float] = 0.5
     spectrum_model_name: Optional[str] = None
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_fsdp_use_orig_params(cls, data):
+        if (
+            data.get("fsdp")
+            and data.get("fsdp_config")
+            and not data["fsdp_config"].get("use_orig_params")
+            and data.get("plugins")
+            and any("SpectrumPlugin" in plugin for plugin in data["plugins"])
+        ):
+            # would otherwise raise
+            # ValueError: Must flatten tensors with uniform `requires_grad` when `use_orig_params=False`
+            raise ValueError(
+                "FSDP + SpectrumPlugin cannot be used together when `use_orig_params=False` is set"
+            )
+        return data

src/axolotl/train.py

@@ -1,26 +1,29 @@
 """Prepare and train a model on a dataset. Can also infer from a model or merge lora"""
 
+import importlib
 import inspect
 import os
 import signal
 import sys
 import weakref
 from pathlib import Path
-from typing import Tuple, Union
+from typing import Any, Dict
 
 import torch
 import transformers.modelcard
 from accelerate.logging import get_logger
 from accelerate.utils import save_fsdp_model
-from peft import PeftModel
-from pkg_resources import get_distribution  # type: ignore
-from transformers import PreTrainedModel, PreTrainedTokenizer
+from datasets import Dataset
+from peft import PeftConfig, PeftModel
+from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
+from transformers.trainer import Trainer
 
 from axolotl.common.datasets import TrainDatasetMeta
-from axolotl.contribs.lgpl.unsloth import (  # pylint: disable = no-name-in-module
+from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
+from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.freeze import freeze_layers_except
@@ -32,17 +35,25 @@ try:
 except ImportError:
     BetterTransformer = None
 
-project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-src_dir = os.path.join(project_root, "src")
-sys.path.insert(0, src_dir)
-
 configure_logging()
 LOG = get_logger(__name__)
 
 
-def train(
-    *, cfg: DictDefault, dataset_meta: TrainDatasetMeta
-) -> Tuple[Union[PeftModel, PreTrainedModel], PreTrainedTokenizer]:
+def setup_model_and_tokenizer(
+    cfg: DictDefault,
+) -> tuple[
+    PreTrainedModel, PreTrainedTokenizer, PeftConfig | None, ProcessorMixin | None
+]:
+    """
+    Load the tokenizer, processor (for multimodal models), and model based on configuration.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+
+    Returns:
+        Tuple containing model, tokenizer, `peft_config` (if LoRA / QLoRA, else
+            `None`), and processor (if multimodal, else `None`).
+    """
     # Load tokenizer
     LOG.debug(
         f"loading tokenizer... {cfg.tokenizer_config or cfg.base_model_config}",
@@ -55,11 +66,58 @@ def train(
     if cfg.is_multimodal:
         processor = load_processor(cfg, tokenizer)
 
-    # Get datasets
-    train_dataset = dataset_meta.train_dataset
-    eval_dataset = dataset_meta.eval_dataset
-    total_num_steps = dataset_meta.total_num_steps
+    # Load the model and peft_config
+    msg = "loading model"
+    if cfg.adapter:
+        msg += " and peft_config..."
+    LOG.debug(msg)
 
+    model, peft_config = load_model(cfg, tokenizer, processor=processor)
+    if model.generation_config is not None:
+        model.generation_config.do_sample = True
+
+    # Apply freezing if specified
+    if cfg.unfrozen_parameters:
+        freeze_layers_except(model, cfg.unfrozen_parameters)
+
+    return model, tokenizer, peft_config, processor
+
+
+def setup_reference_model(
+    cfg: DictDefault, tokenizer: PreTrainedTokenizer
+) -> PreTrainedModel | None:
+    """
+    Set up the reference model for RL training if needed.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        tokenizer: The tokenizer to use for the reference model.
+
+    Returns:
+        Reference model if needed for RL training, `None` otherwise.
+    """
+    model_ref = None
+    if cfg.rl and cfg.rl != "orpo":
+        if cfg.adapter and not cfg.rl_adapter_ref_model:
+            # use built-in trl autounwrap
+            LOG.debug("Passing model_ref: None to RL trainer")
+            model_ref = None  # explicit setting to None
+        else:
+            # load the model again for model_ref/baseline
+            model_ref, _ = load_model(cfg, tokenizer, reference_model=True)
+    return model_ref
+
+
+def determine_resume_checkpoint(cfg: DictDefault) -> str | None:
+    """
+    Determine the checkpoint to resume from based on configuration.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+
+    Returns:
+        Path to the checkpoint to resume from, or `None` if not resuming.
+    """
     if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
         possible_checkpoints = [
             str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")
@@ -73,77 +131,22 @@ def train(
             LOG.info(
                 f"Using Auto-resume functionality to start with checkpoint at {cfg.resume_from_checkpoint}"
             )
-    resume_from_checkpoint = cfg.resume_from_checkpoint
+    return cfg.resume_from_checkpoint
 
-    # Load the model and tokenizer
-    msg = "loading model"
-    if cfg.adapter:
-        msg += " and peft_config..."
-    LOG.debug(msg)
-    model, peft_config = load_model(cfg, tokenizer, processor=processor)
-    if model.generation_config is not None:
-        model.generation_config.do_sample = True
 
-    model_ref = None
-    if cfg.rl and cfg.rl != "orpo":
-        if cfg.adapter and not cfg.rl_adapter_ref_model:
-            # use built-in trl autounwrap
-            LOG.debug("Passing model_ref: None to RL trainer")
-            model_ref = None  # explicit setting to None
-        else:
-            # load the model again for model_ref/baseline
-            model_ref, _ = load_model(cfg, tokenizer, reference_model=True)
+def setup_signal_handler(
+    cfg: DictDefault, model: PreTrainedModel, safe_serialization: bool
+):
+    """
+    Set up signal handler for graceful termination.
 
-    safe_serialization = cfg.save_safetensors is True
-
-    if cfg.unfrozen_parameters:
-        freeze_layers_except(model, cfg.unfrozen_parameters)
-
-    trainer = setup_trainer(
-        cfg,
-        train_dataset,
-        eval_dataset,
-        (model, model_ref, peft_config),
-        tokenizer,
-        processor,
-        total_num_steps,
-    )
-
-    if cfg.fix_untrained_tokens:
-        # check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
-        sig = inspect.signature(fix_untrained_tokens)
-        # if the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
-        if "token_ids_to_fix" in sig.parameters and isinstance(
-            cfg.fix_untrained_tokens, list
-        ):
-            fix_untrained_tokens(
-                model,
-                tokenizer,
-                train_dataset,
-                token_ids_to_fix=cfg.fix_untrained_tokens,
-            )
-        else:
-            fix_untrained_tokens(model, tokenizer, train_dataset)
-        if cfg.local_rank == 0:
-            model.save_pretrained(
-                str(Path(cfg.output_dir)), safe_serialization=safe_serialization
-            )
-
-    # go ahead and presave, so we have the adapter config available to inspect
-    if peft_config:
-        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
-        peft_config.save_pretrained(cfg.output_dir)
-    # additionally presave the tokenizer and model configs
-    if not Path(cfg.output_dir).is_dir():
-        os.makedirs(cfg.output_dir, exist_ok=True)
-    tokenizer.save_pretrained(str(Path(cfg.output_dir)))
-    if hasattr(model, "config"):
-        model.config.save_pretrained(str(Path(cfg.output_dir)))
-
-    # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
-    if (
-        cfg.local_rank == 0 and not cfg.use_ray
-    ):  # ray workers don't have access to this signal
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        model: The model to save on termination
+        safe_serialization: Whether to use safe serialization when saving
+    """
+    # ray workers don't have access to this signal
+    if cfg.local_rank == 0 and not cfg.use_ray:
 
         def terminate_handler(_, __, model_weakref):
             if model_weakref() is not None:
@@ -161,21 +164,22 @@ def train(
             lambda signum, frame: terminate_handler(signum, frame, _model_weakref),
         )
 
-    badge_markdown = """[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)"""
-    transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
 
-    if getattr(cfg, "axolotl_config_path"):
-        raw_axolotl_cfg = Path(cfg.axolotl_config_path)
-        version = get_distribution("axolotl").version
-        if raw_axolotl_cfg.is_file():
-            transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n<details><summary>See axolotl config</summary>\n\naxolotl version: `{version}`\n```yaml\n{raw_axolotl_cfg.read_text(encoding='utf-8')}\n```\n\n</details><br>\n"
+def execute_training(
+    cfg: DictDefault, trainer: Any, resume_from_checkpoint: str | None
+):
+    """
+    Execute the training process with appropriate backend configurations.
 
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        trainer: The configured trainer object.
+        resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
+    """
     LOG.info("Starting trainer...")
     if cfg.group_by_length:
         LOG.info("hang tight... sorting dataset for group_by_length")
 
-    pretrain_hooks(cfg, trainer)
-
     if cfg.flash_optimum:
         with torch.backends.cuda.sdp_kernel(
             # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
@@ -187,15 +191,30 @@ def train(
     else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
-    post_train_hooks(cfg, trainer)
 
-    LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
+def save_trained_model(
+    cfg: DictDefault,
+    trainer: Any,
+    model: PreTrainedModel,
+    safe_serialization: bool,
+):
+    """
+    Save the trained model according to configuration and training setup.
 
-    # post training
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        trainer: The trainer object.
+        model: The trained model to save.
+        safe_serialization: Whether to use safe serialization.
+    """
+    LOG.info(f"Training completed! Saving pre-trained model to {cfg.output_dir}.")
+
+    # Post training module hooks
     for name, module in model.named_modules():
         if hasattr(module, "_post_training"):
             module._post_training(model, name)  # pylint: disable=protected-access
 
+    # Handle FSDP state dict type
     state_dict_type = "FULL_STATE_DICT"
     if trainer.is_fsdp_enabled:
         if cfg.fsdp_final_state_dict_type:
@@ -203,16 +222,18 @@ def train(
         trainer.accelerator.state.fsdp_plugin.set_state_dict_type(state_dict_type)
         LOG.info(f"Set FSDP state dict type to {state_dict_type} for saving.")
 
+    # Handle ReLoRA early return case
     if cfg.relora_steps:
         if cfg.adapter == "lora" and not (cfg.load_in_4bit or cfg.load_in_8bit):
             model = model.merge_and_unload()
         else:
             # final model weights have already been saved by `ReLoRACallback.on_train_end`
-            return model, tokenizer
+            return
 
-    # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
-    # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
     if cfg.fsdp:
+        # TODO: do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
+        # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple
+        # processes attempt to write the same file
         if (
             state_dict_type == "SHARDED_STATE_DICT"
             and cfg.fsdp_config.fsdp_state_dict_type == "SHARDED_STATE_DICT"
@@ -244,7 +265,6 @@ def train(
                 os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
             except FileNotFoundError:
                 pass
-
     elif cfg.local_rank == 0:
         if cfg.flash_optimum and BetterTransformer:
             model = BetterTransformer.reverse(model)
@@ -255,58 +275,241 @@ def train(
             )
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
+
+def create_model_card(cfg: DictDefault, trainer: Trainer):
+    """
+    Create a model card for the trained model if needed.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        trainer: The trainer object with model card creation capabilities.
+    """
     if not cfg.hub_model_id:
+        # Guard since create_model_card may fail if dataset_tags is empty list
         try:
             model_card_kwarg = {
                 "model_name": cfg.output_dir.lstrip("./")
                 .encode("utf-8")
                 .decode("utf-8")
             }
-            if cfg.datasets is not None:
-                if cfg.rl is not None or cfg.reward_model or cfg.process_reward_model:
-                    dataset_tags = [
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                    ]
-                    dataset_tags = [
-                        d for d in dataset_tags if not d.startswith("https://")
-                    ]
-                    if dataset_tags:
-                        # guard as create_model_card may fail if dataset_tags is empty list
-                        model_card_kwarg["dataset_name"] = dataset_tags
-                else:
-                    dataset_tags = [
-                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
-                    ]
-                    dataset_tags = [
-                        d for d in dataset_tags if not d.startswith("https://")
-                    ]
-                    if dataset_tags:
-                        # guard as create_model_card may fail if dataset_tags is empty list
-                        model_card_kwarg["dataset_tags"] = dataset_tags
+
+            # We check if we're using a TRL trainer; if so, `dataset_tags` is not consumed.
+            rl = cfg.rl is not None or cfg.reward_model or cfg.process_reward_model
+            if cfg.datasets is not None and not rl:
+                dataset_tags = [
+                    d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
+                ]
+                dataset_tags = [d for d in dataset_tags if not d.startswith("https://")]
+
+                if dataset_tags:
+                    model_card_kwarg["dataset_tags"] = dataset_tags
 
             trainer.create_model_card(**model_card_kwarg)
         except (AttributeError, UnicodeDecodeError):
             pass
     elif cfg.hub_model_id:
-        # defensively push to the hub to ensure the model card is updated
+        # Defensively push to the hub to ensure the model card is updated
         trainer.push_to_hub()
 
-    return model, tokenizer
+
+def save_initial_configs(
+    cfg: DictDefault,
+    tokenizer: PreTrainedTokenizer,
+    model: PreTrainedModel,
+    peft_config: PeftConfig | None,
+):
+    """
+    Save initial configurations before training.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        tokenizer: The tokenizer to save.
+        model: The model to save configuration for.
+        peft_config: The PEFT configuration to save if applicable.
+    """
+    # Create output_dir if it doesn't already exist
+    output_dir = Path(cfg.output_dir)
+    if not output_dir.is_dir():
+        os.makedirs(cfg.output_dir, exist_ok=True)
+
+    # Pre-save adapter config so it's available to inspect
+    if peft_config:
+        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}...")
+        peft_config.save_pretrained(cfg.output_dir)
+
+    # Pre-save the tokenizer and model configs
+    LOG.info(f"Pre-saving tokenizer to {cfg.output_dir}...")
+    tokenizer.save_pretrained(str(output_dir))
+    if hasattr(model, "config"):
+        LOG.info(f"Pre-saving model config to {cfg.output_dir}...")
+        model.config.save_pretrained(str(output_dir))
 
 
-def pretrain_hooks(_cfg, _trainer):
+def setup_model_card(cfg: DictDefault):
     """
-    Run hooks right before kicking off the training
-    :param cfg:
-    :param trainer:
-    :return:
+    Set up the Axolotl badge and add the Axolotl config to the model card if available.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
     """
+    badge_markdown = """[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)"""
+    transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n{badge_markdown}"
+
+    if getattr(cfg, "axolotl_config_path"):
+        raw_axolotl_cfg = Path(cfg.axolotl_config_path)
+        version = importlib.metadata.version("axolotl")
+        if raw_axolotl_cfg.is_file():
+            transformers.modelcard.AUTOGENERATED_TRAINER_COMMENT += f"\n<details><summary>See axolotl config</summary>\n\naxolotl version: `{version}`\n```yaml\n{raw_axolotl_cfg.read_text(encoding='utf-8')}\n```\n\n</details><br>\n"
 
 
-def post_train_hooks(_cfg, _trainer):
+def handle_untrained_tokens_fix(
+    cfg: DictDefault,
+    model: PreTrainedModel,
+    tokenizer: PreTrainedTokenizer,
+    train_dataset: Dataset,
+    safe_serialization: bool,
+):
     """
-    Run hooks right after training completes
-    :param cfg:
-    :param trainer:
-    :return:
+    Apply fixes for untrained tokens if configured.
+
+    Args:
+        cfg: Dictionary mapping `axolotl` config keys to values.
+        model: The model to apply fixes to.
+        tokenizer: The tokenizer for token identification.
+        train_dataset: The training dataset to use.
+        safe_serialization: Whether to use safe serialization when saving.
     """
+    if not cfg.fix_untrained_tokens:
+        return
+
+    is_ds_zero3: bool = False
+    if os.environ.get("ACCELERATE_DEEPSPEED_ZERO_STAGE") == "3":
+        is_ds_zero3 = True
+
+    # Check if the `token_ids_to_fix` kwarg exists in the fix_untrained_tokens args
+    sig = inspect.signature(fix_untrained_tokens)
+
+    fix_kwargs: Dict[str, Any] = {}
+    # If the function has the `token_ids_to_fix` arg, and fix_untrained_tokens is a list
+    if "token_ids_to_fix" in sig.parameters and isinstance(
+        cfg.fix_untrained_tokens, list
+    ):
+        fix_kwargs["token_ids_to_fix"] = cfg.fix_untrained_tokens
+    if "is_ds_zero3" in sig.parameters:
+        fix_kwargs["is_ds_zero3"] = is_ds_zero3
+
+    fix_untrained_tokens(model, tokenizer, train_dataset, **fix_kwargs)
+
+    if cfg.local_rank == 0:
+        model.save_pretrained(
+            str(Path(cfg.output_dir)), safe_serialization=safe_serialization
+        )
+
+
+def setup_model_and_trainer(
+    cfg: DictDefault, dataset_meta: TrainDatasetMeta
+) -> tuple[
+    HFRLTrainerBuilder | HFCausalTrainerBuilder,
+    PeftModel | PreTrainedModel,
+    PreTrainedTokenizer,
+    PeftConfig | None,
+]:
+    """
+    Load model, tokenizer, trainer, etc. Helper function to encapsulate the full
+    trainer setup.
+
+    Args:
+        cfg: The configuration dictionary with training parameters.
+        dataset_meta: Object with training, validation datasets and metadata.
+
+    Returns:
+        Tuple of:
+            - Trainer (Causal or RLHF)
+            - Model
+            - Tokenizer
+            - PEFT config
+    """
+    # Load tokenizer, processor and model
+    model, tokenizer, peft_config, processor = setup_model_and_tokenizer(cfg)
+
+    # Set up reference model for RL if needed
+    model_ref = setup_reference_model(cfg, tokenizer)
+
+    # Get datasets from metadata
+    train_dataset = dataset_meta.train_dataset
+    eval_dataset = dataset_meta.eval_dataset
+    total_num_steps = dataset_meta.total_num_steps
+
+    # Set up trainer
+    trainer = setup_trainer(
+        cfg=cfg,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        model=model,
+        tokenizer=tokenizer,
+        processor=processor,
+        total_num_steps=total_num_steps,
+        model_ref=model_ref,
+        peft_config=peft_config,
+    )
+
+    return (
+        trainer,
+        model,
+        tokenizer,
+        peft_config,
+    )
+
+
+def train(
+    cfg: DictDefault, dataset_meta: TrainDatasetMeta
+) -> tuple[PeftModel | PreTrainedModel, PreTrainedTokenizer, Trainer]:
+    """
+    Train a model on the given dataset.
+
+    Args:
+        cfg: The configuration dictionary with training parameters
+        dataset_meta: Object with training, validation datasets and metadata
+
+    Returns:
+        Tuple of (model, tokenizer) after training
+    """
+    # Setup model, tokenizer, (causal or RLHF) trainer etc.
+    (
+        trainer,
+        model,
+        tokenizer,
+        peft_config,
+    ) = setup_model_and_trainer(cfg, dataset_meta)
+
+    # Determine if we need to resume from a checkpoint
+    resume_from_checkpoint = determine_resume_checkpoint(cfg)
+
+    # Configuration for saving
+    safe_serialization = cfg.save_safetensors is True
+
+    # Handle untrained tokens if configured
+    train_dataset = dataset_meta.train_dataset
+    handle_untrained_tokens_fix(
+        cfg, model, tokenizer, train_dataset, safe_serialization
+    )
+
+    # Save initial configs
+    save_initial_configs(cfg, tokenizer, model, peft_config)
+
+    # Set up signal handler for graceful termination
+    setup_signal_handler(cfg, model, safe_serialization)
+
+    # Set up badges and config info for model card
+    setup_model_card(cfg)
+
+    # Execute the training
+    execute_training(cfg, trainer, resume_from_checkpoint)
+
+    # Save the trained model
+    save_trained_model(cfg, trainer, model, safe_serialization)
+
+    # Create model card
+    create_model_card(cfg, trainer)
+
+    return model, tokenizer, trainer

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -64,6 +64,17 @@ class ChatTemplate(str, Enum):
     metharme = "metharme"  # pylint: disable=invalid-name
 
 
+class CustomSupportedOptimizers(str, Enum):
+    """Custom supported optimizers"""
+
+    optimi_adamw = "optimi_adamw"  # pylint: disable=invalid-name
+    ao_adamw_4bit = "ao_adamw_4bit"  # pylint: disable=invalid-name
+    ao_adamw_8bit = "ao_adamw_8bit"  # pylint: disable=invalid-name
+    ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
+    adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
+    muon = "muon"  # pylint: disable=invalid-name
+
+
 class DeprecatedParameters(BaseModel):
     """configurations that are deprecated"""
 
@@ -494,17 +505,7 @@ class HyperparametersConfig(BaseModel):
     embedding_lr_scale: Optional[float] = None
     weight_decay: Optional[float] = 0.0
     optimizer: Optional[
-        Union[
-            OptimizerNames,
-            Literal[
-                "lion_pytorch",
-                "optimi_adamw",
-                "ao_adamw_4bit",
-                "ao_adamw_8bit",
-                "ao_adamw_fp8",
-                "adopt_adamw",
-            ],
-        ]
+        Union[OptimizerNames, CustomSupportedOptimizers]
     ] = OptimizerNames.ADAMW_HF
     optim_args: Optional[Union[str, Dict[str, Any]]] = Field(
         default=None,
@@ -518,7 +519,7 @@ class HyperparametersConfig(BaseModel):
     )
     torchdistx_path: Optional[str] = None
     lr_scheduler: Optional[
-        Union[SchedulerType, Literal["one_cycle"]]
+        Union[SchedulerType, Literal["one_cycle"], Literal["rex"]]
     ] = SchedulerType.COSINE
     lr_scheduler_kwargs: Optional[Dict[str, Any]] = None
     lr_quadratic_warmup: Optional[bool] = None
@@ -727,7 +728,7 @@ class AxolotlInputConfig(
         default=None,
         json_schema_extra={"description": "streaming dataset to use for pretraining"},
     )
-    dataset_processes: Optional[int] = Field(default=os.cpu_count())
+    dataset_processes: Optional[int] = Field(default=min(32, os.cpu_count()))  # type: ignore[type-var]
     dataset_exact_deduplication: Optional[bool] = None
     dataset_keep_in_memory: Optional[bool] = None
     dataloader_pin_memory: Optional[bool] = None
@@ -778,9 +779,9 @@ class AxolotlInputConfig(
 
     # torch_dtype: Optional[torch.dtype]
 
-    gradient_checkpointing: Optional[Union[Literal["unsloth"], bool]] = Field(
-        default=False
-    )
+    gradient_checkpointing: Optional[
+        Union[Literal["unsloth", "offload"], bool]
+    ] = Field(default=False)
     gradient_checkpointing_kwargs: Optional[Dict[str, Any]] = None
 
     unfrozen_parameters: Optional[List[str]] = None
@@ -855,6 +856,7 @@ class AxolotlInputConfig(
 
     special_tokens: Optional[SpecialTokensConfig] = None
     tokens: Optional[List[str]] = None
+    added_tokens_overrides: Optional[Dict[int, str]] = None
 
     torch_compile: Optional[Union[Literal["auto"], bool]] = None
     torch_compile_backend: Optional[str] = None
@@ -1153,6 +1155,15 @@ class AxolotlInputConfig(
             raise ValueError("gradient_checkpointing is not supported for MPT models")
         return self
 
+    @model_validator(mode="after")
+    def check_offload_grad_checkpointing(self):
+        if self.gradient_checkpointing and self.gradient_checkpointing == "unsloth":
+            LOG.warning(
+                "`unsloth` is deprecated for gradient_checkpointing, use `offload`"
+            )
+            self.gradient_checkpointing = "offload"
+        return self
+
     @model_validator(mode="after")
     def check_better_transformers(self):
         if self.flash_optimum is True:
@@ -1177,6 +1188,13 @@ class AxolotlInputConfig(
             LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
         return self
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_lr_groups(cls, data):
+        if data.get("lr_groups") and data.get("loraplus_lr_ratio"):
+            raise ValueError("lr_groups and loraplus_lr_ratio cannot be used together.")
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_saves(cls, data):

src/axolotl/utils/config/models/input/v0_4_1/trl.py

@@ -1,7 +1,8 @@
 """
 GRPO specific configuration args
 """
-from typing import List, Optional
+
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -11,7 +12,10 @@ class TRLConfig(BaseModel):
     Input args for TRL.
     """
 
-    beta: Optional[float] = None
+    beta: Optional[float] = Field(
+        default=None,
+        json_schema_extra={"description": "Beta for RL training"},
+    )
     max_completion_length: Optional[int] = Field(
         default=None,
         json_schema_extra={
@@ -20,16 +24,68 @@ class TRLConfig(BaseModel):
     )
 
     # GRPO specific args
-    use_vllm: Optional[bool] = False
-    vllm_device: Optional[str] = "auto"
-    vllm_gpu_memory_utilization: Optional[float] = 0.9
-    vllm_max_model_len: Optional[int] = None
-    vllm_dtype: Optional[str] = "auto"
+    # Ref: https://github.com/huggingface/trl/blob/e3244d2d096ff1e2e248c931d06d39e165e20623/trl/trainer/grpo_config.py#L22
+    use_vllm: Optional[bool] = Field(
+        default=False,
+        json_schema_extra={"description": "Whether to use VLLM for RL training"},
+    )
+    vllm_device: Optional[str] = Field(
+        default="auto",
+        json_schema_extra={"description": "Device to use for VLLM"},
+    )
+    vllm_gpu_memory_utilization: Optional[float] = Field(
+        default=0.9,
+        json_schema_extra={"description": "GPU memory utilization for VLLM"},
+    )
+    vllm_dtype: Optional[str] = Field(
+        default="auto",
+        json_schema_extra={"description": "Data type for VLLM"},
+    )
+    vllm_max_model_len: Optional[int] = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Maximum length of the model context for VLLM"
+        },
+    )
 
-    reward_funcs: Optional[List[str]] = None
-    num_generations: Optional[int] = None
-    log_completions: Optional[bool] = False
-
-    sync_ref_model: Optional[bool] = False
-    ref_model_mixup_alpha: Optional[float] = 0.9
-    ref_model_sync_steps: Optional[int] = 64
+    reward_funcs: Optional[list[str]] = Field(
+        default=None,
+        json_schema_extra={"description": "List of reward functions to load"},
+    )
+    reward_weights: Optional[list[float]] = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Weights for each reward function. Must match the number of reward functions."
+        },
+    )
+    num_generations: Optional[int] = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Number of generations to sample. The global batch size (num_processes * per_device_batch_size) must be divisible by this value."
+        },
+    )
+    log_completions: Optional[bool] = Field(
+        default=False,
+        json_schema_extra={"description": "Whether to log completions"},
+    )
+    sync_ref_model: Optional[bool] = Field(
+        default=False,
+        json_schema_extra={
+            "description": (
+                "Whether to sync the reference model every `ref_model_sync_steps` "
+                "steps, using the `ref_model_mixup_alpha` parameter."
+            )
+        },
+    )
+    ref_model_mixup_alpha: Optional[float] = Field(
+        default=0.9,
+        json_schema_extra={
+            "description": "Mixup alpha for the reference model. Requires `sync_ref_model=True`."
+        },
+    )
+    ref_model_sync_steps: Optional[int] = Field(
+        default=64,
+        json_schema_extra={
+            "description": "Sync steps for the reference model. Requires `sync_ref_model=True`."
+        },
+    )

src/axolotl/utils/data/rl.py

@@ -121,6 +121,7 @@ def drop_long_rl_seq(
 
 
 def load_prepare_preference_datasets(cfg):
+    import pdb; pdb.set_trace()
     def load_split(dataset_cfgs, _cfg):
         split_datasets: List[Any] = []
         use_auth_token = _cfg.hf_use_auth_token

src/axolotl/utils/distributed.py

@@ -79,7 +79,7 @@ def is_main_process():
 
 
 def is_local_main_process():
-    return PartialState().is_main_process
+    return PartialState().is_local_main_process
 
 
 def get_world_size():

src/axolotl/utils/gradient_checkpointing/__init__.py

@@ -4,7 +4,7 @@ from axolotl.utils.gradient_checkpointing.unsloth import (
 )
 
 
-def hf_grad_checkpoint_unsloth_wrapper(
+def hf_grad_checkpoint_offload_wrapper(
     decoder_layer, *args, use_reentrant=None
 ):  # pylint: disable=unused-argument
     return Unsloth_Offloaded_Gradient_Checkpointer.apply(

src/axolotl/utils/models.py

@@ -57,8 +57,14 @@ from axolotl.prompt_tokenizers import LLAMA_DEFAULT_EOS_TOKEN
 from axolotl.utils.bench import log_gpu_memory_usage
 from axolotl.utils.chat_templates import get_chat_template_from_config
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import get_device_count, get_device_type, zero_only
-from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_unsloth_wrapper
+from axolotl.utils.distributed import (
+    barrier,
+    get_device_count,
+    get_device_type,
+    is_local_main_process,
+    zero_only,
+)
+from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrapper
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
 
@@ -165,7 +171,95 @@ def load_model_config(cfg):
     return model_config
 
 
+def modify_tokenizer_files(
+    tokenizer_path: str, token_mappings: Dict[int, str], output_dir: str
+) -> str:
+    """
+    Modify tokenizer files to replace added_tokens strings, save to output directory, and return the path to the modified tokenizer.
+
+    This only works with reserved tokens that were added to the tokenizer, not tokens already part of the vocab.
+
+    Args:
+        tokenizer_path: Path or name of the original tokenizer
+        token_mappings: Dict mapping {token_id (int): new_token_string}
+        output_dir: Directory to save the modified tokenizer
+
+    Returns:
+        Path to the modified tokenizer directory
+
+    Ref: https://github.com/huggingface/transformers/issues/27974#issuecomment-1854188941
+    """
+
+    import json
+
+    # Create the tokenizer directory in output_dir if it doesn't exist
+    tokenizer_dir = os.path.join(output_dir, "tokenizer")
+    os.makedirs(tokenizer_dir, exist_ok=True)
+
+    if is_local_main_process():  # pylint: disable=too-many-nested-blocks
+        # Load the tokenizer
+        temp_tokenizer = AutoTokenizer.from_pretrained(tokenizer_path, use_fast=True)
+
+        # Save the tokenizer to the output directory
+        temp_tokenizer.save_pretrained(tokenizer_dir)
+
+        # Get the token IDs and map them to their new values
+        token_id_mappings = {
+            int(token_id): new_value for token_id, new_value in token_mappings.items()
+        }
+
+        # 1. Update tokenizer_config.json - added_tokens_decoder
+        config_path = os.path.join(tokenizer_dir, "tokenizer_config.json")
+        if os.path.exists(config_path):
+            with open(config_path, "r", encoding="utf-8") as f:
+                config_data = json.load(f)
+
+            # Update added_tokens_decoder
+            if "added_tokens_decoder" in config_data:
+                for token_id, new_value in token_id_mappings.items():
+                    token_id_str = str(token_id)
+                    if token_id_str in config_data["added_tokens_decoder"]:
+                        config_data["added_tokens_decoder"][token_id_str][
+                            "content"
+                        ] = new_value
+                    else:
+                        raise ValueError(
+                            f"Token ID {token_id_str} not found in added_tokens_decoder"
+                        )
+
+            # Write the updated config back
+            with open(config_path, "w", encoding="utf-8") as f:
+                json.dump(config_data, f, indent=2)
+
+        # 2. Update tokenizer.json - added_tokens
+        tokenizer_path = os.path.join(tokenizer_dir, "tokenizer.json")
+        if os.path.exists(tokenizer_path):
+            with open(tokenizer_path, "r", encoding="utf-8") as f:
+                tokenizer_data = json.load(f)
+
+            # Update added_tokens
+            if "added_tokens" in tokenizer_data:
+                for token_id, new_value in token_id_mappings.items():
+                    for i, token_entry in enumerate(tokenizer_data["added_tokens"]):
+                        if token_entry["id"] == token_id:
+                            tokenizer_data["added_tokens"][i]["content"] = new_value
+                            break
+                    else:
+                        # Reaching this section means the token_id was not found in tokenizer.json added_tokens
+                        raise ValueError(
+                            f"Token ID {token_id} not found in added_tokens"
+                        )
+
+            # Write the updated tokenizer data back
+            with open(tokenizer_path, "w", encoding="utf-8") as f:
+                json.dump(tokenizer_data, f, indent=2)
+
+    barrier()
+    return tokenizer_dir
+
+
 def load_tokenizer(cfg):
+    """Load and configure the tokenizer based on the provided config."""
     model_config = load_model_config(cfg)
     tokenizer_kwargs = {}
     use_fast = True  # this is the default
@@ -180,8 +274,18 @@ def load_tokenizer(cfg):
     if cfg.tokenizer_type:
         tokenizer_cls = getattr(transformers, cfg.tokenizer_type)
 
+    # Set base tokenizer path
+    tokenizer_path = cfg.tokenizer_config
+
+    # Apply token string overrides if specified
+    if cfg.added_tokens_overrides:
+        # Modify tokenizer files and get path to modified tokenizer
+        tokenizer_path = modify_tokenizer_files(
+            tokenizer_path, cfg.added_tokens_overrides, output_dir=cfg.output_dir
+        )
+
     tokenizer = tokenizer_cls.from_pretrained(
-        cfg.tokenizer_config,
+        tokenizer_path,
         trust_remote_code=cfg.trust_remote_code or False,
         use_fast=use_fast,
         **tokenizer_kwargs,
@@ -389,8 +493,8 @@ class ModelLoader:
 
             patch_fa_peft_integration()
 
-        if self.cfg.gradient_checkpointing == "unsloth":
-            transformers.modeling_utils.checkpoint = hf_grad_checkpoint_unsloth_wrapper
+        if self.cfg.gradient_checkpointing in ["unsloth", "offload"]:
+            transformers.modeling_utils.checkpoint = hf_grad_checkpoint_offload_wrapper
 
         if self.cfg.flash_attention:
             self.patch_attention()

src/axolotl/utils/schedulers.py

@@ -6,6 +6,80 @@ from torch.optim import Optimizer
 from torch.optim.lr_scheduler import LambdaLR, LRScheduler
 
 
+class RexLR(LRScheduler):
+    """
+    Reflected Exponential (REX) learning rate scheduler.
+
+    - Original implementation: https://github.com/IvanVassi/REX_LR
+    - Original license: Apache 2.0
+    - Based on: https://arxiv.org/abs/2107.04197
+
+    Args:
+        optimizer (torch.optim.Optimizer): The optimizer to schedule the learning rate for.
+        max_lr (float): The maximum learning rate.
+        min_lr (float): The minimum learning rate.
+        total_steps (int): The total number of training steps.
+        num_warmup_steps (int): The number of warmup steps.
+        last_step (int): The index of last step.
+    """
+
+    def __init__(
+        self, optimizer, max_lr, min_lr, total_steps=0, num_warmup_steps=0, last_step=0
+    ):
+        if min_lr > max_lr:
+            raise ValueError(
+                f'Value of "min_lr" should be less than value of "max_lr". Got min_lr={min_lr} and max_lr={max_lr}'
+            )
+        if num_warmup_steps > total_steps:
+            raise ValueError(
+                f"num_warmup_steps ({num_warmup_steps}) must be less than or equal to total_steps ({total_steps})."
+            )
+
+        self.min_lr = min_lr
+        self.max_lr = max_lr
+        self.total_steps = total_steps
+        self.num_warmup_steps = num_warmup_steps
+        self.last_step = last_step - 1
+
+        # Ensure each parameter group has an "initial_lr" key to avoid issues when resuming.
+        for group in optimizer.param_groups:
+            group.setdefault("initial_lr", group["lr"])
+
+        # Pass self.last_step as last_epoch to the parent.
+        super().__init__(optimizer, last_epoch=self.last_step)
+
+    @property
+    def last_step(self):
+        return self.last_epoch
+
+    @last_step.setter
+    def last_step(self, value):
+        self.last_epoch = value
+
+    def get_lr(self):
+        # Warmup phase: if defined, increase lr linearly from 0 to max_lr.
+        if 1 <= self.last_step <= self.num_warmup_steps:
+            return [
+                base_lr * self.last_step / self.num_warmup_steps
+                for base_lr in self.base_lrs
+            ]
+
+        # Post-warmup phase: adjust step relative to the end of warmup.
+        step_after = self.last_step - self.num_warmup_steps
+        remaining_steps = self.total_steps - self.num_warmup_steps
+
+        # Avoid LR spiking
+        if step_after >= remaining_steps or step_after == -1 or remaining_steps <= 0:
+            return [self.min_lr for _ in self.base_lrs]
+
+        mod_iter = step_after % remaining_steps
+        z = (remaining_steps - mod_iter) / remaining_steps
+        rex_factor = self.min_lr / self.max_lr + (1.0 - self.min_lr / self.max_lr) * (
+            z / (0.1 + 0.9 * z)
+        )
+        return [base_lr * rex_factor for base_lr in self.base_lrs]
+
+
 class InterpolatingLogScheduler(LRScheduler):
     """
     A scheduler that interpolates learning rates in a logarithmic fashion

src/axolotl/utils/trainer.py

@@ -574,14 +574,40 @@ def prepare_opinionated_env(cfg):
 
 
 def setup_trainer(
-    cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps
+    cfg,
+    train_dataset,
+    eval_dataset,
+    model,
+    tokenizer,
+    processor,
+    total_num_steps,
+    model_ref=None,
+    peft_config=None,
 ):
+    """
+    Helper method for instantiating and building a (causal or RLHF) trainer.
+
+    Args:
+        cfg: Axolotl config object containing training parameters.
+        train_dataset: Dataset to use for training.
+        eval_dataset: Dataset to use for evaluation.
+        model: The model to train.
+        tokenizer: Tokenizer for processing text input.
+        processor: Processor for data preparation.
+        total_num_steps: The total number of training steps.
+        model_ref: Optional reference model for RLHF training. Default is None.
+        peft_config: Optional PEFT (Parameter-Efficient Fine-Tuning) configuration. Default is None.
+
+    Returns:
+        A trainer instance (either `HFRLTrainer` or `HFCausalTrainer`) configured based
+            on the provided parameters.
+    """
     if cfg.rl:
-        trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer, processor)
-        trainer_builder.model_ref = model[1]
-        trainer_builder.peft_config = model[2]
+        trainer_builder = HFRLTrainerBuilder(cfg, model, tokenizer, processor)
+        trainer_builder.model_ref = model_ref
+        trainer_builder.peft_config = peft_config
     else:
-        trainer_builder = HFCausalTrainerBuilder(cfg, model[0], tokenizer, processor)
+        trainer_builder = HFCausalTrainerBuilder(cfg, model, tokenizer, processor)
 
     trainer_builder.train_dataset = train_dataset
     trainer_builder.eval_dataset = eval_dataset

styles.css

@@ -14,7 +14,7 @@
 h1 {
     font-family: var(--font-title);
     font-weight: 400;
-    font-size: 6rem;
+    font-size: 5rem;
     line-height: 1.1;
     letter-spacing: -0.05em;
     font-feature-settings: "ss01" on;

tests/cli/test_cli_train.py

@@ -28,7 +28,7 @@ class TestTrainCommand(BaseCliTest):
         config_path.write_text(valid_test_config)
 
         with patch("axolotl.cli.train.train") as mock_train:
-            mock_train.return_value = (MagicMock(), MagicMock())
+            mock_train.return_value = (MagicMock(), MagicMock(), MagicMock())
 
             result = cli_runner.invoke(
                 cli,
@@ -48,7 +48,7 @@ class TestTrainCommand(BaseCliTest):
         config_path = self._test_cli_overrides(tmp_path, valid_test_config)
 
         with patch("axolotl.cli.train.train") as mock_train:
-            mock_train.return_value = (MagicMock(), MagicMock())
+            mock_train.return_value = (MagicMock(), MagicMock(), MagicMock())
 
             result = cli_runner.invoke(
                 cli,

tests/e2e/integrations/test_cut_cross_entropy.py

@@ -69,6 +69,51 @@ class TestCutCrossEntropyIntegration:
             train(cfg=cfg, dataset_meta=dataset_meta)
             check_model_output_exists(temp_dir, cfg)
 
+    # pylint: disable=redefined-outer-name
+    def test_qwen2_w_cce(self, temp_dir):
+        cfg = DictDefault(
+            {
+                "base_model": "Qwen/Qwen2.5-0.5B",
+                "plugins": [
+                    "axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin",
+                ],
+                "cut_cross_entropy": True,
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "output_dir": temp_dir,
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "max_steps": 10,
+                "bf16": "auto",
+            }
+        )
+        prepare_plugins(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        major, minor, _ = get_pytorch_version()
+        if (major, minor) < (2, 4):
+            with pytest.raises(ImportError):
+                train(cfg=cfg, dataset_meta=dataset_meta)
+        else:
+            train(cfg=cfg, dataset_meta=dataset_meta)
+            check_model_output_exists(temp_dir, cfg)
+
     @pytest.mark.parametrize(
         "attention_type",
         [

tests/e2e/multigpu/test_llama.py

@@ -750,3 +750,66 @@ class TestMultiGPULlama:
         check_tensorboard(
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
         )
+
+    def test_fix_untrained_tokens(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "fix_untrained_tokens": True,
+                "sequence_len": 512,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                    "bos_token": "<|custom_im_start|>",
+                    "eos_token": "<|custom_im_end|>",
+                },
+                "datasets": [
+                    {
+                        "chat_template": "jinja",
+                        "chat_template_jinja": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|custom_im_start|>' + message['role'] + '\n' + message['content'] + '<|custom_im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|custom_im_start|>assistant\n' }}{% endif %}",
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "split": "train[:10%]",
+                        "field_messages": "conversations",
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": True,
+                "bf16": True,
+                "save_safetensors": True,
+                "deepspeed": str(AXOLOTL_ROOT / "deepspeed_configs/zero3_bf16.json"),
+                "use_tensorboard": True,
+            }
+        )
+
+        # write cfg to yaml file
+        Path(temp_dir).mkdir(parents=True, exist_ok=True)
+        with open(Path(temp_dir) / "config.yaml", "w", encoding="utf-8") as fout:
+            fout.write(yaml.dump(cfg.to_dict(), Dumper=yaml.Dumper))
+
+        execute_subprocess_async(
+            [
+                "axolotl",
+                "train",
+                str(Path(temp_dir) / "config.yaml"),
+                "--num-processes",
+                "2",
+                "--main-process-port",
+                f"{get_torch_dist_unique_port()}",
+            ]
+        )
+
+        check_tensorboard(
+            temp_dir + "/runs", "train/train_loss", 4.0, "Train Loss is too high"
+        )

tests/e2e/test_llama.py

@@ -66,6 +66,54 @@ class TestLlama:
         check_model_output_exists(temp_dir, cfg)
 
     def test_fix_untrained_tokens(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "fix_untrained_tokens": True,
+                "sequence_len": 512,
+                "val_set_size": 0.0,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                    "bos_token": "<|custom_im_start|>",
+                    "eos_token": "<|custom_im_end|>",
+                },
+                "datasets": [
+                    {
+                        "chat_template": "jinja",
+                        "chat_template_jinja": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|custom_im_start|>' + message['role'] + '\n' + message['content'] + '<|custom_im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|custom_im_start|>assistant\n' }}{% endif %}",
+                        "path": "mlabonne/FineTome-100k",
+                        "type": "chat_template",
+                        "split": "train[:10%]",
+                        "field_messages": "conversations",
+                        "message_field_role": "from",
+                        "message_field_content": "value",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_8bit",
+                "lr_scheduler": "cosine",
+                "flash_attention": True,
+                "sample_packing": True,
+                "bf16": True,
+                "save_safetensors": True,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+    def test_fix_untrained_tokens_already_trained(self, temp_dir):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {

tests/e2e/test_mixtral.py

@@ -75,7 +75,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32
@@ -131,7 +131,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32
@@ -190,7 +190,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32
@@ -249,7 +249,7 @@ class TestMixtral(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        model, _ = train(cfg=cfg, dataset_meta=dataset_meta)
+        model, _, _ = train(cfg=cfg, dataset_meta=dataset_meta)
         assert (
             model.base_model.model.model.layers[0].block_sparse_moe.gate.weight.dtype
             == torch.float32

tests/e2e/test_optimizers.py

@@ -65,8 +65,9 @@ class TestCustomOptimizers(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        train(cfg=cfg, dataset_meta=dataset_meta)
+        _, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+        assert trainer.optimizer.optimizer.__class__.__name__ == "AdamW"
 
     @with_temp_dir
     @require_torch_2_5_1
@@ -111,8 +112,57 @@ class TestCustomOptimizers(unittest.TestCase):
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
 
-        train(cfg=cfg, dataset_meta=dataset_meta)
+        _, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
         check_model_output_exists(temp_dir, cfg)
+        assert "ADOPT" in trainer.optimizer.optimizer.__class__.__name__
+
+    @with_temp_dir
+    @require_torch_2_5_1
+    def test_muon(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "max_steps": 5,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "muon",
+                "lr_scheduler": "cosine",
+                "weight_decay": 0.01,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        _, _, trainer = train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+        assert "Muon" in trainer.optimizer.optimizer.__class__.__name__
 
     @with_temp_dir
     def test_fft_schedule_free_adamw(self, temp_dir):

tests/e2e/test_schedulers.py

@@ -0,0 +1,71 @@
+"""
+E2E tests for custom schedulers using Llama
+"""
+
+import logging
+import os
+import unittest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import check_model_output_exists, with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestCustomSchedulers(unittest.TestCase):
+    """
+    Test case for Llama models using LoRA
+    """
+
+    @with_temp_dir
+    def test_rex_scheduler(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 8,
+                "lora_alpha": 16,
+                "lora_dropout": 0.05,
+                "lora_target_linear": True,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "unk_token": "<unk>",
+                    "bos_token": "<s>",
+                    "eos_token": "</s>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 8,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_hf",
+                "max_steps": 20,
+                "lr_scheduler": "rex",
+                "warmup_steps": 5,
+                "cosine_min_lr_ratio": 0.05,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)

tests/test_tokenizers.py

@@ -1,6 +1,7 @@
 """
 Test cases for the tokenizer loading
 """
+
 import unittest
 
 import pytest
@@ -9,7 +10,7 @@ from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_tokenizer
 
 
-class TestTokenizers(unittest.TestCase):
+class TestTokenizers:
     """
     test class for the load_tokenizer fn
     """
@@ -75,12 +76,48 @@ class TestTokenizers(unittest.TestCase):
             }
         )
         tokenizer = load_tokenizer(cfg)
-        self.assertEqual(tokenizer("<|im_start|>user")["input_ids"], [1, 32000, 1404])
-        self.assertEqual(len(tokenizer), 32001)
+        assert tokenizer("<|im_start|>user")["input_ids"] == [1, 32000, 1404]
+        assert len(tokenizer) == 32001
 
         # ensure reloading the tokenizer again from cfg results in same vocab length
         tokenizer = load_tokenizer(cfg)
-        self.assertEqual(len(tokenizer), 32001)
+        assert len(tokenizer) == 32001
+
+    def test_added_tokens_overrides(self, temp_dir):
+        cfg = DictDefault(
+            {
+                # use with tokenizer that has reserved_tokens in added_tokens
+                "tokenizer_config": "NousResearch/Llama-3.2-1B",
+                "added_tokens_overrides": {
+                    128041: "RANDOM_OVERRIDE_1",
+                    128042: "RANDOM_OVERRIDE_2",
+                },
+                "output_dir": temp_dir,
+            }
+        )
+
+        tokenizer = load_tokenizer(cfg)
+        assert tokenizer.encode("RANDOM_OVERRIDE_1", add_special_tokens=False) == [
+            128041
+        ]
+        assert tokenizer.encode("RANDOM_OVERRIDE_2", add_special_tokens=False) == [
+            128042
+        ]
+
+    def test_added_tokens_overrides_with_toolargeid(self, temp_dir):
+        cfg = DictDefault(
+            {
+                # use with tokenizer that has reserved_tokens in added_tokens
+                "tokenizer_config": "NousResearch/Llama-3.2-1B",
+                "added_tokens_overrides": {1000000: "BROKEN_RANDOM_OVERRIDE_1"},
+                "output_dir": temp_dir,
+            }
+        )
+
+        with pytest.raises(
+            ValueError, match=r".*Token ID 1000000 not found in added_tokens.*"
+        ):
+            load_tokenizer(cfg)
 
 
 if __name__ == "__main__":