add guassian dropout support

2023-09-25 14:50:39 -04:00
72 changed files with 527 additions and 2758 deletions
--- a/.github/ISSUE_TEMPLATE/bug-report.yaml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yaml
@@ -53,13 +53,6 @@ body:
    validations:
      required: true

-  - type: textarea
-    id: config
-    attributes:
-      label: Config yaml
-      description: |
-        Please attach the config yaml!
-
  - type: textarea
    id: possible-solution
    attributes:
--- a/.github/workflows/base.yml
+++ b/.github/workflows/base.yml
@@ -25,11 +25,6 @@ jobs:
            python_version: "3.10"
            pytorch: 2.0.1
            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
-          - cuda: "118"
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.1.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
    steps:
      - name: Checkout
        uses: actions/checkout@v3
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -23,11 +23,6 @@ jobs:
            python_version: "3.10"
            pytorch: 2.0.1
            axolotl_extras:
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.1.0
-            axolotl_extras:
    runs-on: [self-hosted, gpu, docker]
    steps:
      - name: Checkout
@@ -51,7 +46,6 @@ jobs:
          build-args: |
            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}
            CUDA=${{ matrix.cuda }}
-            PYTORCH_VERSION=${{ matrix.pytorch }}
          file: ./docker/Dockerfile
          push: ${{ github.event_name != 'pull_request' }}
          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
@@ -74,11 +68,6 @@ jobs:
            pytorch: 2.0.1
            axolotl_extras:
            is_latest: true
-          - cuda: 118
-            cuda_version: 11.8.0
-            python_version: "3.10"
-            pytorch: 2.1.0
-            axolotl_extras:
    runs-on: [self-hosted, gpu, docker]
    steps:
      - name: Checkout
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -6,11 +6,9 @@ on:
      - "main"
    paths:
      - '**.py'
-      - 'requirements.txt'
  pull_request:
      paths:
       - '**.py'
-       - 'requirements.txt'
  workflow_dispatch:

 jobs:
@@ -46,7 +44,7 @@ jobs:

      - name: Install dependencies
        run: |
-          pip3 install -U -e .
+          pip3 install -e .
          pip3 install -r requirements-tests.txt

      - name: Run tests
@@ -71,8 +69,8 @@ jobs:

      - name: Install dependencies
        run: |
-          pip3 uninstall -y transformers accelerate
-          pip3 install -U -e .[flash-attn]
+          pip3 install -e .
+          pip3 install flash-attn
          pip3 install -r requirements-tests.txt

      - name: Run e2e tests
--- a/README.md
+++ b/README.md
@@ -23,10 +23,9 @@ Features:
 - [Supported Features](#axolotl-supports)
 - [Quickstart](#quickstart-)
 - [Installation](#installation)
-  - [Docker](#docker)
-  - [Conda/Pip venv](#condapip-venv)
-  - [LambdaLabs](#lambdalabs)
-  - [Windows](#windows)
+  - [Docker Installation](#environment)
+  - [Conda/Pip venv Installation](#condapip-venv)
+  - [LambdaLabs Installation](#lambdalabs)
 - [Dataset](#dataset)
  - [How to Add Custom Prompts](#how-to-add-custom-prompts)
  - [How to Use Custom Pretokenized Dataset](#how-to-use-your-custom-pretokenized-dataset)
@@ -51,7 +50,7 @@ Features:
      <b>Axolotl provides a unified repository for fine-tuning <br />a variety of AI models with ease</b>
    </p>
    <p>
-      Go ahead and Axolotl questions!!
+      Go ahead and axolotl questions!!
    </p>
    <img src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/pre-commit.yml/badge.svg?branch=main" alt="pre-commit">
    <img alt="PyTest Status" src="https://github.com/OpenAccess-AI-Collective/axolotl/actions/workflows/tests.yml/badge.svg?branch=main">
@@ -103,7 +102,7 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \

 ### Environment

-#### Docker
+- Docker
  ```bash
  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
  ```
@@ -115,23 +114,18 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
  docker compose up -d
  ```

-#### Conda/Pip venv
+- Conda/Pip venv
  1. Install python >=**3.9**

  2. Install pytorch stable https://pytorch.org/get-started/locally/

-  3. Install Axolotl along with python dependencies
+  3. Install axolotl along with python dependencies
        ```bash
        pip3 install packaging
        pip3 install -e '.[flash-attn,deepspeed]'
        ```
-  4. (Optional) Login to Huggingface to use gated models/datasets.
-        ```bash
-        huggingface-cli login
-        ```
-        Get the token at huggingface.co/settings/tokens

-#### LambdaLabs
+- LambdaLabs
  <details>

  <summary>Click to Expand</summary>
@@ -175,8 +169,7 @@ accelerate launch -m axolotl.cli.inference examples/openllama-3b/lora.yml \
  ```
  </details>

-#### Windows
-Please use WSL or Docker!
+- Windows: Please use WSL or Docker!

 ### Dataset

@@ -187,7 +180,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
  ```json
  {"instruction": "...", "input": "...", "output": "..."}
  ```
- `sharegpt`: conversations where `from` is `human`/`gpt`
+- `sharegpt:chat`: conversations where `from` is `human`/`gpt`
  ```json
  {"conversations": [{"from": "...", "value": "..."}]}
  ```
@@ -252,10 +245,6 @@ Have dataset(s) in one of the following format (JSONL recommended):
  ```json
  {"article": "...", "question": "...", "answer": "..."}
  ```
- `context_qa.load_v2`: in context question answering (alternate)
-  ```json
-  {"context": "...", "question": "...", "answer": "..."}
-  ```
 - `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
  ```json
  {"article": "...", "unanswerable_question": "..."}
@@ -280,11 +269,11 @@ Have dataset(s) in one of the following format (JSONL recommended):
  ```json
  {"prompt": "...", "generation": "..."}
  ```
- `sharegpt.load_role`: conversations where `role` is used instead of `from`
+- `sharegpt_simple.load_role`: conversations where `role` is used instead of `from`
  ```json
  {"conversations": [{"role": "...", "value": "..."}]}
  ```
- `sharegpt.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
+- `sharegpt_simple.load_guanaco`: conversations where `from` is `prompter`/`assistant` instead of default sharegpt
  ```json
  {"conversations": [{"from": "...", "value": "..."}]}
  ```
@@ -319,7 +308,7 @@ Using file:
 #### How to use your custom pretokenized dataset

 - Do not pass a `type:`
- Columns in Dataset must be exactly `input_ids`, `attention_mask`, `labels`
+- Dataset must contain `input_ids`, `attention_mask`, `labels` in columns


 ### Config
@@ -362,12 +351,6 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
    - path: data.jsonl # or json
      ds_type: json # see other options below
      type: alpaca
-
-  # dataset with splits, but no train split
-  dataset:
-    - path: knowrohit07/know_sql
-      type: context_qa.load_v2
-      train_on_split: validation
  ```

 - loading
@@ -398,15 +381,15 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
 <summary>All yaml options</summary>

 ```yaml
-# This is the huggingface model that contains *.pt, *.safetensors, or *.bin files
-# This can also be a relative path to a model on disk
+# this is the huggingface model that contains *.pt, *.safetensors, or *.bin files
+# this can also be a relative path to a model on disk
 base_model: ./llama-7b-hf
-# You can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
+# you can specify an ignore pattern if the model repo contains more than 1 model type (*.pt, etc)
 base_model_ignore_patterns:
-# If the base_model repo on hf hub doesn't include configuration .json files,
-# You can set that here, or leave this empty to default to base_model
+# if the base_model repo on hf hub doesn't include configuration .json files,
+# you can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
-# You can specify to choose a specific model revision from huggingface hub
+# you can specify to choose a specific model revision from huggingface hub
 model_revision:
 # Optional tokenizer configuration override in case you want to use a different tokenizer
 # than the one defined in the base model
@@ -421,24 +404,18 @@ trust_remote_code:
 tokenizer_use_fast:
 # Whether to use the legacy tokenizer setting, defaults to True
 tokenizer_legacy:
-# Resize the model embeddings when new tokens are added to multiples of 32
-# This is reported to improve training speed on some models
+# resize the model embeddings when new tokens are added to multiples of 32
+# this is reported to improve training speed on some models
 resize_token_embeddings_to_32x:

-# Used to identify which the model is based on
-is_falcon_derived_model:
-is_llama_derived_model:
-# Please note that if you set this to true, `padding_side` will be set to "left" by default
-is_mistral_derived_model:
-
-# Whether you are training a 4-bit GPTQ quantized model
+# whether you are training a 4-bit GPTQ quantized model
 gptq: true
 gptq_groupsize: 128 # group size
 gptq_model_v1: false # v1 or v2

-# This will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
+# this will attempt to quantize the model down to 8 bits and use adam 8 bit optimizer
 load_in_8bit: true
-# Use bitsandbytes 4 bit
+# use bitsandbytes 4 bit
 load_in_4bit:

 # Use CUDA bf16
@@ -452,9 +429,9 @@ tf32: true # require >=ampere
 bfloat16: true # require >=ampere
 float16: true

-# A list of one or more datasets to finetune the model with
+# a list of one or more datasets to finetune the model with
 datasets:
-  # HuggingFace dataset repo | "json" for local dataset, make sure to fill data_files
+  # hf dataset repo | "json" for local dataset, make sure to fill data_files
  - path: vicgalle/alpaca-gpt4
  # The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]
    type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
@@ -462,20 +439,18 @@ datasets:
    data_files: # Optional[str] path to source data files
    shards: # Optional[int] number of shards to split data into
    name: # Optional[str] name of dataset configuration to load
-    conversation:  # Optional[str] fastchat conversation type, only used with type: sharegpt

-  # Custom user prompt
+  # custom user prompt
  - path: repo
    type:
-      # The below are defaults. only set what's needed.
+      # the below are defaults. only set what's needed.
      system_prompt: ""
-      system_format: "{system}"
      field_system: system
      field_instruction: instruction
-      field_input: input
-      field_output: output
+      field_output: input

-      # Customizable to be single line or multi-line
+      # customizable to be single line or multi-line
+      system_format: "{system}"
      # 'format' can include {input}
      format: |-
        User: {instruction} {input}
@@ -483,24 +458,21 @@ datasets:
      # 'no_input_format' cannot include {input}
      no_input_format: "{instruction} "

-      # For `completion` datsets only, uses the provided field instead of `text` column
+      # for completions datsets, uses the provided field if not `text`
      field:

-# Axolotl attempts to save the dataset as an arrow after packing the data together so
+# 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
-# Push prepared dataset to hub
+# push prepared dataset to hub
 push_dataset_to_hub: # repo path
-# The maximum number of processes to use while preprocessing your input dataset. This defaults to `os.cpu_count()`
-# if not set.
-dataset_processes: # defaults to os.cpu_count() if not set
 # push checkpoints to hub
 hub_model_id: # repo path to push finetuned model
 # how to push checkpoints to hub
 # https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
 hub_strategy:
-# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
-# Required to be true when used in combination with `push_dataset_to_hub`
+# whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
+# required to be true when used in combination with `push_dataset_to_hub`
 hf_use_auth_token: # boolean
 # How much of the dataset to set aside as evaluation. 1 = 100%, 0.50 = 50%, etc. 0 for no eval.
 val_set_size: 0.04
@@ -509,34 +481,30 @@ dataset_shard_num:
 # Index of shard to use for whole dataset
 dataset_shard_idx:

-# The maximum length of an input to train with, this should typically be less than 2048
+# the maximum length of an input to train with, this should typically be less than 2048
 # as most models have a token/context limit of 2048
 sequence_len: 2048
-# Pad inputs so each step uses constant sized buffers
-# This will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
+# pad inputs so each step uses constant sized buffers
+# this will reduce memory fragmentation and may prevent OOMs, by re-using memory more efficiently
 pad_to_sequence_len:
-# Max sequence length to concatenate training samples together up to
-# Inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
+# max sequence length to concatenate training samples together up to
+# inspired by StackLLaMA. see https://huggingface.co/blog/stackllama#supervised-fine-tuning
 # FutureWarning: This will soon be DEPRECATED
 max_packed_sequence_len: 1024
-# Use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
+# use efficient multi-packing with block diagonal attention and per sequence position_ids. Recommend set to 'true'
 sample_packing:
-# Set to 'false' if getting errors during eval with sample_packing on.
+# set to 'false' if getting errors during eval with sample_packing on.
 eval_sample_packing:
-# You can set these packing optimizations AFTER starting a training at least once.
+# you can set these packing optimizations AFTER starting a training at least once.
 # The trainer will provide recommended values for these values.
 sample_packing_eff_est:
 total_num_tokens:

-# If you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
+# if you want to use 'lora' or 'qlora' or leave blank to train all parameters in original model
 adapter: lora
-# If you already have a lora model trained that you want to load, put that here.
-# This means after training, if you want to test the model, you should set this to the value of `lora_out_dir`.
+# if you already have a lora model trained that you want to load, put that here
+# lora hyperparameters
 lora_model_dir:
-
-# LoRA hyperparameters
-# For more details about the following options, see:
-# https://www.anyscale.com/blog/fine-tuning-llms-lora-or-full-parameter-an-in-depth-analysis-with-llama-2
 lora_r: 8
 lora_alpha: 16
 lora_dropout: 0.05
@@ -548,96 +516,81 @@ lora_target_modules:
 #  - gate_proj
 #  - down_proj
 #  - up_proj
-lora_target_linear: # If true, will target all linear layers
-
-# If you added new tokens to the tokenizer, you may need to save some LoRA modules because they need to know the new tokens.
-# For LLaMA and Mistral, you need to save `embed_tokens` and `lm_head`. It may vary for other models.
-# `embed_tokens` converts tokens to embeddings, and `lm_head` converts embeddings to token probabilities.
-# https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
+lora_target_linear: # if true, will target all linear layers
 lora_modules_to_save:
 #  - embed_tokens
 #  - lm_head
-
-# Once you complete training, the model will be saved to the following directory.
-# If you merge the adapter to the base model, a subdirectory `merged` will be created under this directory.
-# Make sure `lora_model_dir` points to this directory if you want to use the trained model.
 lora_out_dir:
 lora_fan_in_fan_out: false

 # ReLoRA configuration
-# Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
-relora_steps: # Number of steps per ReLoRA restart
-relora_warmup_steps: # Number of per-restart warmup steps
-relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
+# must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
+relora_steps: # number of steps per ReLoRA restart
+relora_warmup_steps: # number of per-restart warmup steps
+relora_cpu_offload: # true to perform lora weight merges on cpu during restarts, for modest gpu memory savings

 # wandb configuration if you're using it
 wandb_mode: # "offline" to save run metadata locally and not sync to the server, "disabled" to turn off wandb
-wandb_project: # Your wandb project name
-wandb_entity: # A wandb Team name if using a Team
+wandb_project: # your wandb project name
+wandb_entity: # a wandb Team name if using a Team
 wandb_watch:
-wandb_run_id: # Set the name of your wandb run
+wandb_run_id: # set the name of your wandb run
 wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training

-# Where to save the full-finetuned model to
+# where to save the finished model to
 output_dir: ./completed-model

-# Whether to use torch.compile and which backend to use
+# whether to use torch.compile and which backend to use
 torch_compile:  # bool
 torch_compile_backend:  # Optional[str]

-# Training hyperparameters
-
-# If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
+# training hyperparameters
 gradient_accumulation_steps: 1
-# The number of samples to include in each batch. This is the number of samples sent to each GPU.
 micro_batch_size: 2
-eval_batch_size:
+eval_batch_size: 2
 num_epochs: 3
 warmup_steps: 100
 learning_rate: 0.00003
 lr_quadratic_warmup:
 logging_steps:
-save_strategy: # Set to `no` to skip checkpoint saves
-save_steps: # Leave empty to save at each epoch
-eval_steps: # Leave empty to eval at each epoch
-save_total_limit: # Checkpoints saved at a time
-# Maximum number of iterations to train for. It precedes num_epochs which means that
-# if both are set, num_epochs will not be guaranteed.
-# e.g., when 1 epoch is 1000 steps => `num_epochs: 2` and `max_steps: 100` will train for 100 steps
+save_strategy: # set to `no` to skip checkpoint saves
+save_steps: # leave empty to save at each epoch
+eval_steps: # leave empty to eval at each epoch
+save_total_limit: # checkpoints saved at a time
 max_steps:

-eval_table_size: # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
-eval_table_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
+eval_table_size: # approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
+eval_table_max_new_tokens: # total number of tokens generated for predictions sent to wandb. Default is 128

-# Save model as safetensors (require safetensors package)
+# save model as safetensors (require safetensors package)
 save_safetensors:

-# Whether to mask out or include the human's prompt from the training labels
+# whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
-# Group similarly sized data to minimize padding.
-# May be slower to start, as it must download and sort the entire dataset.
-# Note that training loss may have an oscillating pattern with this enabled.
+# group similarly sized data to minimize padding
+# may be slower to start, as it must download and sort the entire dataset
+# note that training loss may have an oscillating pattern with this enabled
 group_by_length: false

 # Whether to use gradient checkpointing https://huggingface.co/docs/transformers/v4.18.0/en/performance#gradient-checkpointing
 gradient_checkpointing: false

-# Stop training after this many evaluation losses have increased in a row
+# stop training after this many evaluation losses have increased in a row
 # https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
 early_stopping_patience: 3

-# Specify a scheduler and kwargs to use with the optimizer
+# specify a scheduler and kwargs to use with the optimizer
 lr_scheduler: # 'one_cycle' | 'log_sweep' | empty for cosine
 lr_scheduler_kwargs:

-# For one_cycle optim
-lr_div_factor: # Learning rate div factor
+# for one_cycle optim
+lr_div_factor: # learning rate div factor

-# For log_sweep optim
+# for log_sweep optim
 log_sweep_min_lr:
 log_sweep_max_lr:

-# Specify optimizer
+# specify optimizer
 # Valid values are driven by the Transformers OptimizerNames class, see:
 # https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
 #
@@ -663,7 +616,7 @@ log_sweep_max_lr:
 # - paged_lion_32bit
 # - paged_lion_8bit
 optimizer:
-# Specify weight decay
+# specify weight decay
 weight_decay:
 # adamw hyperparams
 adam_beta1:
@@ -672,56 +625,47 @@ adam_epsilon:
 # Gradient clipping max norm
 max_grad_norm:

-# Augmentation techniques
-# NEFT https://arxiv.org/abs/2310.05914, set this to a number (paper default is 5) to add noise to embeddings
-# currently only supported on Llama and Mistral
-noisy_embedding_alpha:
-
-# Whether to bettertransformers
+# whether to bettertransformers
 flash_optimum:
-# Whether to use xformers attention patch https://github.com/facebookresearch/xformers:
+# whether to use xformers attention patch https://github.com/facebookresearch/xformers:
 xformers_attention:
-# Whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
+# whether to use flash attention patch https://github.com/Dao-AILab/flash-attention:
 flash_attention:
-flash_attn_cross_entropy:  # Whether to use flash-attention cross entropy implementation - advanced use only
-flash_attn_rms_norm:  # Whether to use flash-attention rms norm implementation - advanced use only
-# Whether to use scaled-dot-product attention
+# whether to use scaled-dot-product attention
 # https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
 sdp_attention:
 # Landmark attention (only llama)
 landmark_attention:
 # xpos RoPE see https://github.com/kaiokendev/cutoff-len-is-context-len/blob/main/util/xpos_rope_llama_monkey_patch.py
-# LLaMA only
+# llama only
 xpos_rope:
 # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
 rope_scaling:
  type: # linear | dynamic
  factor: # float

-# Resume from a specific checkpoint dir
+# 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.
-# Be careful with this being turned on between different models.
+# if resume_from_checkpoint isn't set and you simply want it to start where it left off
+# be careful with this being turned on between different models
 auto_resume_from_checkpoints: false

-# Don't mess with this, it's here for accelerate and torchrun
+# don't mess with this, it's here for accelerate and torchrun
 local_rank:

-# Add or change special tokens.
-# If you add tokens here, you don't need to add them to the `tokens` list.
+# add or change special tokens
 special_tokens:
  # bos_token: "<s>"
  # eos_token: "</s>"
  # unk_token: "<unk>"
-
-# Add extra tokens.
+# add extra tokens
 tokens:

 # FSDP
 fsdp:
 fsdp_config:

-# Deepspeed config path. e.g., deepspeed/zero3.json
+# Deepspeed config path
 deepspeed:

 # Advanced DDP Arguments
@@ -747,66 +691,6 @@ strict:

 </details>

-<details>
-<summary> Understanding of batch size and gradient accumulation steps </summary>
-<br/>
-Gradient accumulation means accumulating gradients over several mini-batches and updating the model weights afterward. When the samples in each batch are diverse, this technique doesn't significantly impact learning.
-
-This method allows for effective training with larger effective batch sizes without needing proportionally larger memory. Here's why:
-
-1. **Memory Consumption with Batch Size**: The primary reason increasing the batch size impacts memory is due to the storage requirements for intermediate activations. When you forward propagate a batch through a network, you have to store the activations at each layer for each sample in the batch, because these activations are used during backpropagation to compute gradients. Therefore, larger batches mean more activations, leading to greater GPU memory consumption.
-
-2. **Gradient Accumulation**: With gradient accumulation, you're effectively simulating a larger batch size by accumulating gradients over several smaller batches (or micro-batches). However, at any given time, you're only forward and backward propagating a micro-batch. This means you only store activations for the micro-batch, not the full accumulated batch. As a result, you can simulate the effect of a larger batch size without the memory cost of storing activations for a large batch.
-
-**Example 1:**
-Micro batch size: 3
-Gradient accumulation steps: 2
-Number of GPUs: 3
-Total batch size = 3 * 2 * 3 = 18
-
-```
-| GPU 1          | GPU 2          | GPU 3          |
-|----------------|----------------|----------------|
-| S1, S2, S3     | S4, S5, S6     | S7, S8, S9     |
-| e1, e2, e3     | e4, e5, e6     | e7, e8, e9     |
-|----------------|----------------|----------------|
-| → (accumulate) | → (accumulate) | → (accumulate) |
-|----------------|----------------|----------------|
-| S10, S11, S12  | S13, S14, S15  | S16, S17, S18  |
-| e10, e11, e12  | e13, e14, e15  | e16, e17, e18  |
-|----------------|----------------|----------------|
-| → (apply)      | → (apply)      | → (apply)      |
-
-Accumulated gradient for the weight w1 after the second iteration (considering all GPUs):
-Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6 + e7 + e8 + e9 + e10 + e11 + e12 + e13 + e14 + e15 + e16 + e17 + e18
-
-Weight update for w1:
-w1_new = w1_old - learning rate x (Total gradient for w1 / 18)
-```
-
-**Example 2:**
-Micro batch size: 2
-Gradient accumulation steps: 1
-Number of GPUs: 3
-Total batch size = 2 * 1 * 3 = 6
-
-```
-| GPU 1     | GPU 2     | GPU 3     |
-|-----------|-----------|-----------|
-| S1, S2    | S3, S4    | S5, S6    |
-| e1, e2    | e3, e4    | e5, e6    |
-|-----------|-----------|-----------|
-| → (apply) | → (apply) | → (apply) |
-
-Accumulated gradient for the weight w1 (considering all GPUs):
-Total gradient for w1 = e1 + e2 + e3 + e4 + e5 + e6
-
-Weight update for w1:
-w1_new = w1_old - learning rate × (Total gradient for w1 / 6)
-```
-
-</details>
-
 ### Train

 Run
@@ -882,10 +766,6 @@ Pass the appropriate flag to the train command:
    --base_model="./completed-model" --prompter=None --load_in_8bit=True
  ```

-Please use `--sample_packing False` if you have it on and receive the error similar to below:
-
-> RuntimeError: stack expects each tensor to be equal size, but got [1, 32, 1, 128] at entry 0 and [1, 32, 8, 128] at entry 1
-
 ### Merge LORA to base

 Add below flag to train command above
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -5,9 +5,6 @@ ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
 ARG AXOLOTL_EXTRAS=""
 ARG CUDA="118"
 ENV BNB_CUDA_VERSION=$CUDA
-ARG PYTORCH_VERSION="2.0.1"
-
-ENV PYTORCH_VERSION=$PYTORCH_VERSION

 RUN apt-get update && \
    apt-get install -y vim curl
@@ -15,19 +12,17 @@ RUN apt-get update && \
 WORKDIR /workspace

 RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
-
-WORKDIR /workspace/axolotl
-
 # If AXOLOTL_EXTRAS is set, append it in brackets
-RUN sed -i "s/torch==.*/torch==$PYTORCH_VERSION/" requirements.txt
-RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
+RUN cd axolotl && \
+    if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
        pip install -e .[flash-attn,$AXOLOTL_EXTRAS]; \
    else \
        pip install -e .[flash-attn]; \
    fi

 # fix so that git fetch/pull from remote works
-RUN git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
+RUN cd axolotl && \
+    git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
    git config --get remote.origin.fetch

 # helper for huggingface-login cli
--- a/docker/Dockerfile-base
+++ b/docker/Dockerfile-base
@@ -14,7 +14,7 @@ ARG CUDA="118"
 ENV PYTHON_VERSION=$PYTHON_VERSION

 RUN apt-get update \
-    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev && rm -rf /var/lib/apt/lists/* \
+    && apt-get install -y wget git build-essential ninja-build git-lfs libaio-dev && rm -rf /var/lib/apt/lists/*
    && wget \
    https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
    && mkdir /root/.conda \
@@ -57,6 +57,11 @@ FROM base-builder
 ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"
 ENV TORCH_CUDA_ARCH_LIST=$TORCH_CUDA_ARCH_LIST

+# recompile apex
+RUN python3 -m pip uninstall -y apex
+RUN git clone https://github.com/NVIDIA/apex
+RUN cd apex && python3 -m pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" ./
+
 RUN mkdir -p /workspace/builds
 COPY --from=bnb-builder /workspace/bitsandbytes /workspace/builds/bitsandbytes

--- a/docs/multipack.md
+++ b/docs/multipack.md
@@ -1,51 +0,0 @@
-# Multipack
-
-4k context, bsz =4,
-each character represents 256 tokens
-X represents a padding token
-
-```
-   0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
-[[ A A A A A A A A A A A ]
-   B B B B B B ]
-   C C C C C C C ]
-   D D D D ]]
-
-[[ E E E E E E E E ]
- [ F F F F ]
- [ G G G ]
- [ H H H H ]]
-
-[[ I I I ]
- [ J J J ]
- [ K K K K K]
- [ L L L ]]
-```
-
-after padding to longest input in each step
-```
-   0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
-[[ A A A A A A A A A A A ]
-   B B B B B B X X X X X X ]
-   C C C C C C C X X X X ]
-   D D D D X X X X X X X ]]
-
-[[ E E E E E E E E ]
- [ F F F F X X X X ]
- [ G G G X X X X X ]
- [ H H H H X X X X ]]
-
-[[ I I I X X ]
- [ J J J X X ]
- [ K K K K K ]
- [ L L L X X ]]
-```
-
-w packing ( note it's the same effective number of tokens per step, but a true bsz of 1)
-```
-   0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5
-[[ A A A A A A A A A A A B B B B B
-   B C C C C C C C D D D D E E E E
-   E E E E F F F F F G G G H H H H
-   I I I J J J J K K K K K L L L X ]]
-```
--- a/examples/cerebras/qlora.yml
+++ b/examples/cerebras/qlora.yml
@@ -7,7 +7,7 @@ push_dataset_to_hub:
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 adapter: qlora
 lora_model_dir:
--- a/examples/code-llama/13b/lora.yml
+++ b/examples/code-llama/13b/lora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./lora-out

--- a/examples/code-llama/13b/qlora.yml
+++ b/examples/code-llama/13b/qlora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./qlora-out

--- a/examples/code-llama/34b/lora.yml
+++ b/examples/code-llama/34b/lora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./lora-out

--- a/examples/code-llama/34b/qlora.yml
+++ b/examples/code-llama/34b/qlora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./qlora-out

--- a/examples/code-llama/7b/lora.yml
+++ b/examples/code-llama/7b/lora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./lora-out

--- a/examples/code-llama/7b/qlora.yml
+++ b/examples/code-llama/7b/qlora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./qlora-out

--- a/examples/falcon/config-7b-lora.yml
+++ b/examples/falcon/config-7b-lora.yml
@@ -3,7 +3,6 @@ base_model_config: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-is_falcon_derived_model: true
 load_in_8bit: true
 load_in_4bit: false
 gptq: false
@@ -12,7 +11,7 @@ push_dataset_to_hub:
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca:chat
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 adapter: lora
 lora_model_dir:
--- a/examples/falcon/config-7b-qlora.yml
+++ b/examples/falcon/config-7b-qlora.yml
@@ -6,7 +6,6 @@ base_model_config: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-is_falcon_derived_model: true
 load_in_8bit: false
 # enable 4bit for QLoRA
 load_in_4bit: true
@@ -18,7 +17,7 @@ datasets:
    data_files:
      - Chain-of-Thought/formatted_cot_data/gsm8k_train.json
    type: "alpaca:chat"
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 # enable QLoRA
 adapter: qlora
--- a/examples/falcon/config-7b.yml
+++ b/examples/falcon/config-7b.yml
@@ -3,7 +3,6 @@ base_model_config: tiiuae/falcon-7b
 trust_remote_code: true
 model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
-is_falcon_derived_model: true
 load_in_8bit: false
 load_in_4bit: false
 gptq: false
@@ -12,7 +11,7 @@ push_dataset_to_hub:
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca:chat
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 adapter:
 lora_model_dir:
--- a/examples/gptj/qlora.yml
+++ b/examples/gptj/qlora.yml
@@ -7,7 +7,7 @@ push_dataset_to_hub:
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 adapter: qlora
 lora_model_dir:
--- a/examples/jeopardy-bot/config.yml
+++ b/examples/jeopardy-bot/config.yml
@@ -6,7 +6,7 @@ load_in_8bit: false
 datasets:
  - path: openaccess-ai-collective/jeopardy
    type: jeopardy
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 adapter:
 lora_model_dir:
--- a/examples/llama-2/gptq-lora.yml
+++ b/examples/llama-2/gptq-lora.yml
@@ -15,7 +15,7 @@ hf_use_auth_token: true
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 adapter: lora
 lora_model_dir:
--- a/examples/llama-2/lora.yml
+++ b/examples/llama-2/lora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./lora-out

@@ -56,7 +56,7 @@ flash_attention: true

 warmup_steps: 10
 eval_steps: 20
-eval_table_size:
+eval_table_size: 5
 eval_table_max_new_tokens: 128
 save_steps:
 debug:
--- a/examples/llama-2/qlora.yml
+++ b/examples/llama-2/qlora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./qlora-out

@@ -58,7 +58,7 @@ flash_attention: true

 warmup_steps: 10
 eval_steps: 20
-eval_table_size:
+eval_table_size: 5
 save_steps:
 debug:
 deepspeed:
--- a/examples/llama-2/relora.yml
+++ b/examples/llama-2/relora.yml
@@ -11,7 +11,7 @@ strict: false
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./relora-out

--- a/examples/llama-2/tiny-llama.yml
+++ b/examples/llama-2/tiny-llama.yml
@@ -12,7 +12,7 @@ strict: false
 datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 output_dir: ./lora-out

@@ -56,7 +56,7 @@ flash_attention: true

 warmup_steps: 10
 eval_steps: 20
-eval_table_size:
+eval_table_size: 5
 save_steps:
 debug:
 deepspeed:
--- a/examples/mistral/README.md
+++ b/examples/mistral/README.md
@@ -1,12 +0,0 @@
-**Mistral 7B** is a language model with a total of 7.3 billion parameters, showcasing a notable performance across a variety of benchmarks.
-
-Fine Tune:
-```shell
-accelerate launch -m axolotl.cli.train examples/mistral/config.yml
-
-```
-
-If you run into CUDA OOM, use deepspeed with config zero2.json:
-```shell
-accelerate launch -m axolotl.cli.train examples/mistral/config.yml --deepspeed deepspeed/zero2.json
-```
--- a/examples/mistral/config.yml
+++ b/examples/mistral/config.yml
@@ -1,62 +0,0 @@
-base_model: mistralai/Mistral-7B-v0.1
-base_model_config: mistralai/Mistral-7B-v0.1
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-is_mistral_derived_model: true
-
-load_in_8bit: false
-load_in_4bit: false
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path:
-val_set_size: 0.01
-output_dir: ./out
-
-sequence_len: 8192
-sample_packing: true
-pad_to_sequence_len: true
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 3
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.000005
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-eval_steps: 20
-eval_table_size: 5
-eval_table_max_new_tokens: 128
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
--- a/examples/mistral/qlora.yml
+++ b/examples/mistral/qlora.yml
@@ -1,79 +0,0 @@
-base_model: mistralai/Mistral-7B-v0.1
-base_model_config: mistralai/Mistral-7B-v0.1
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-is_mistral_derived_model: true
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-datasets:
-  - path: mhenrichsen/alpaca_2k_test
-    type: alpaca
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.01
-output_dir: ./qlora-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 8192
-sample_packing: true
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_run_id:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: true
-fp16: false
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-warmup_steps: 10
-eval_steps: 20
-eval_table_size: 5
-eval_table_max_new_tokens: 128
-save_steps:
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:
-  bos_token: "<s>"
-  eos_token: "</s>"
-  unk_token: "<unk>"
--- a/examples/mpt-7b/config.yml
+++ b/examples/mpt-7b/config.yml
@@ -6,7 +6,7 @@ load_in_8bit: false
 datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 adapter:
 lora_model_dir:
--- a/examples/openllama-3b/config.yml
+++ b/examples/openllama-3b/config.yml
@@ -9,7 +9,7 @@ push_dataset_to_hub:
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 adapter:
 lora_model_dir:
--- a/examples/openllama-3b/lora.yml
+++ b/examples/openllama-3b/lora.yml
@@ -9,7 +9,7 @@ push_dataset_to_hub:
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 adapter: lora
 lora_model_dir:
--- a/examples/openllama-3b/qlora.yml
+++ b/examples/openllama-3b/qlora.yml
@@ -9,7 +9,7 @@ push_dataset_to_hub:
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 adapter: qlora
 lora_model_dir:
--- a/examples/phi/phi-ft.yml
+++ b/examples/phi/phi-ft.yml
@@ -13,7 +13,7 @@ datasets:
  - path: garage-bAInd/Open-Platypus
    type: alpaca

-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
 output_dir: ./phi-sft-out

--- a/examples/phi/phi-qlora.yml
+++ b/examples/phi/phi-qlora.yml
@@ -13,7 +13,7 @@ datasets:
  - path: garage-bAInd/Open-Platypus
    type: alpaca

-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
 output_dir: ./phi-sft-out

--- a/examples/pythia-12b/config.yml
+++ b/examples/pythia-12b/config.yml
@@ -10,7 +10,7 @@ device_map: auto
 datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
 adapter:
 lora_model_dir:
--- a/examples/pythia/lora.yml
+++ b/examples/pythia/lora.yml
@@ -4,7 +4,7 @@ load_in_8bit: true
 datasets:
  - path: teknium/GPT4-LLM-Cleaned
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
 adapter: lora
 lora_model_dir:
@@ -28,8 +28,8 @@ num_epochs: 3
 learning_rate: 0.00001
 train_on_inputs: false
 group_by_length: false
-bf16: true
-tf32: true
+bf16: True
+tf32: True
 early_stopping_patience:
 resume_from_checkpoint:
 local_rank:
--- a/examples/redpajama/config-3b.yml
+++ b/examples/redpajama/config-3b.yml
@@ -7,7 +7,7 @@ load_in_8bit: false
 datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.02
 adapter:
 lora_model_dir:
--- a/examples/replit-3b/config-lora.yml
+++ b/examples/replit-3b/config-lora.yml
@@ -5,7 +5,7 @@ load_in_8bit: false
 datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.05
 adapter: lora
 lora_model_dir:
--- a/examples/xgen-7b/xgen-7b-8k-qlora.yml
+++ b/examples/xgen-7b/xgen-7b-8k-qlora.yml
@@ -16,7 +16,7 @@ datasets:
    data_files:
      - openassistant_best_replies_train.jsonl
    type: "completion"
-dataset_prepared_path:
+dataset_prepared_path: last_run_prepared
 val_set_size: 0.01
 # enable QLoRA
 adapter: qlora
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,19 +4,20 @@ torch==2.0.1
 auto-gptq
 packaging
 peft @ git+https://github.com/huggingface/peft.git
-transformers @ git+https://github.com/huggingface/transformers.git@bd6205919aad4d3a2300a39a98a642f1cc3a5348
+transformers @ git+https://github.com/huggingface/transformers.git
 bitsandbytes>=0.41.1
-accelerate @ git+https://github.com/huggingface/accelerate@80da9cfb09bb3cc9f1b385cb55d6b90d025a5fd9
+accelerate @ git+https://github.com/huggingface/accelerate
 deepspeed
 addict
+evaluate
 fire
 PyYAML>=6.0
 datasets
-flash-attn>=2.3.0
+flash-attn>=2.2.1
 sentencepiece
 wandb
 einops
-xformers>=0.0.22
+xformers
 optimum
 hf_transfer
 colorama
@@ -30,4 +31,3 @@ scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat==0.2.29
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -7,7 +7,6 @@ import transformers

 from axolotl.cli import (
    check_accelerate_default_config,
-    check_user_token,
    do_inference,
    do_merge_lora,
    load_cfg,
@@ -32,7 +31,6 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
    )
    parsed_cfg = load_cfg(config, **kwargs)
    check_accelerate_default_config()
-    check_user_token()
    parser = transformers.HfArgumentParser((TrainerCliArgs))
    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
        return_remaining_strings=True
--- a/setup.py
+++ b/setup.py
@@ -21,14 +21,6 @@ def parse_requirements():
            ):
                # Handle standard packages
                _install_requires.append(line)
-
-    # TODO(wing) remove once xformers release supports torch 2.1.0
-    if "torch==2.1.0" in _install_requires:
-        _install_requires.pop(_install_requires.index("xformers>=0.0.22"))
-        _install_requires.append(
-            "xformers @ git+https://github.com/facebookresearch/xformers.git@main"
-        )
-
    return _install_requires, _dependency_links


--- a/src/axolotl/cli/init.py
+++ b/src/axolotl/cli/init.py
@@ -14,8 +14,6 @@ import yaml
 # add src to the pythonpath so we don't need to pip install this
 from accelerate.commands.config import config_args
 from art import text2art
-from huggingface_hub import HfApi
-from huggingface_hub.utils import LocalTokenNotFoundError
 from transformers import GenerationConfig, TextStreamer

 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
@@ -51,7 +49,7 @@ def print_axolotl_text_art(suffix=None):


 def get_multi_line_input() -> Optional[str]:
-    print("Give me an instruction (Ctrl + D to submit): ")
+    print("Give me an instruction (Ctrl + D to finish): ")
    instruction = ""
    for line in sys.stdin:
        instruction += line  # pylint: disable=consider-using-join
@@ -194,7 +192,6 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
    # load the config from the yaml file
    with open(config, encoding="utf-8") as file:
        cfg: DictDefault = DictDefault(yaml.safe_load(file))
-    cfg.axolotl_config_path = config
    # if there are any options passed in the cli, if it is something that seems valid from the yaml,
    # then overwrite the value
    cfg_keys = cfg.keys()
@@ -250,16 +247,3 @@ def check_accelerate_default_config():
        LOG.warning(
            f"accelerate config file found at {config_args.default_yaml_config_file}. This can lead to unexpected errors"
        )
-
-
-def check_user_token():
-    # Verify if token is valid
-    api = HfApi()
-    try:
-        user_info = api.whoami()
-        return bool(user_info)
-    except LocalTokenNotFoundError:
-        LOG.warning(
-            "Error verifying HuggingFace token. Remember to log in using `huggingface-cli login` and get your access token from https://huggingface.co/settings/tokens if you want to use gated models or datasets."
-        )
-        return False
--- a/src/axolotl/cli/inference.py
+++ b/src/axolotl/cli/inference.py
@@ -14,7 +14,6 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
    # pylint: disable=duplicate-code
    print_axolotl_text_art()
    parsed_cfg = load_cfg(config, **kwargs)
-    parsed_cfg.sample_packing = False
    parser = transformers.HfArgumentParser((TrainerCliArgs))
    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
        return_remaining_strings=True
--- a/src/axolotl/cli/train.py
+++ b/src/axolotl/cli/train.py
@@ -1,45 +1,30 @@
 """
 CLI to run training on a model
 """
-import logging
 from pathlib import Path

 import fire
 import transformers
-from colorama import Fore

 from axolotl.cli import (
    check_accelerate_default_config,
-    check_user_token,
    load_cfg,
    load_datasets,
    print_axolotl_text_art,
 )
 from axolotl.common.cli import TrainerCliArgs
-from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.train import train

-LOG = logging.getLogger("axolotl.cli.train")
-

 def do_cli(config: Path = Path("examples/"), **kwargs):
    # pylint: disable=duplicate-code
    print_axolotl_text_art()
    parsed_cfg = load_cfg(config, **kwargs)
    check_accelerate_default_config()
-    check_user_token()
    parser = transformers.HfArgumentParser((TrainerCliArgs))
    parsed_cli_args, _ = parser.parse_args_into_dataclasses(
        return_remaining_strings=True
    )
-    if parsed_cli_args.prepare_ds_only and not parsed_cfg.dataset_prepared_path:
-        msg = (
-            Fore.RED
-            + "--prepare_ds_only called without dataset_prepared_path set."
-            + Fore.RESET
-        )
-        LOG.warning(msg)
-        parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH

    dataset_meta = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
    if parsed_cli_args.prepare_ds_only:
--- a/src/axolotl/common/const.py
+++ b/src/axolotl/common/const.py
@@ -1,5 +0,0 @@
-"""
-Various shared constants
-"""
-
-DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
--- a/src/axolotl/datasets.py
+++ b/src/axolotl/datasets.py
@@ -22,7 +22,7 @@ class TokenizedPromptDataset(Dataset):
    """
    Dataset that returns tokenized prompts from a stream of text files.
        Args:
-            prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for processing the data.
+            prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for proccessing the data.
            dataset (dataset.Dataset): Dataset with text files.
    """

@@ -55,7 +55,7 @@ class ConstantLengthDataset(IterableDataset):
    """
    Iterable dataset that returns constant length chunks of tokens from stream of text files.
        Args:
-            tokenizer (Tokenizer): The processor used for processing the data.
+            tokenizer (Tokenizer): The processor used for proccessing the data.
            dataset (dataset.Dataset): Dataset with text files.
            seq_length (int): Length of token sequences to return.
    """
--- a/src/axolotl/models/phi/modeling_mixformer_sequential.py
+++ b/src/axolotl/models/phi/modeling_mixformer_sequential.py
@@ -711,8 +711,12 @@ class ParallelBlock(nn.Module):
        self.resid_dropout = nn.Dropout(config.resid_pdrop)
        self.block_idx = block_idx

-        self.mixer = MHA(config, layer_idx=block_idx)
-        self.mlp = MLP(config)
+        self.mixer = MHA(config=config, **mixer, layer_idx=block_idx)
+        mlp_cls = mlp.pop("mlp_cls")
+        if mlp_cls == "fused_mlp":
+            self.mlp = FusedMLP(config=config, **mlp)
+        else:
+            self.mlp = MLP(config=config, **mlp)

    def forward(
        self,
--- a/src/axolotl/monkeypatch/btlm_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/btlm_attn_hijack_flash.py
@@ -7,7 +7,6 @@ import logging
 from typing import Optional, Tuple

 import torch
-from accelerate import init_empty_weights
 from flash_attn.flash_attn_interface import flash_attn_func
 from transformers import AutoConfig, AutoModelForCausalLM

@@ -18,8 +17,7 @@ def replace_btlm_attn_with_flash_attn(model_name="cerebras/btlm-3b-8k-base"):
    # this is a wonky hack to get the remotely loaded module
    model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
    # we need to load the model here in order for modeling_btlm to be available
-    with init_empty_weights():
-        AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+    AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
    module_name = model_config.__class__.__module__.replace(
        ".configuration_btlm", ".modeling_btlm"
    )
--- a/src/axolotl/monkeypatch/falcon_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/falcon_attn_hijack_flash.py
@@ -0,0 +1,101 @@
+"""
+Flash Attention monkey patch for Falcon
+
+copied from https://github.com/pacman100/DHS-LLM-Workshop/blob/main/chat_assistant/training/falcon_flash_attn_monkey_patch.py
+"""
+
+from typing import Optional, Tuple
+
+import torch
+import transformers
+from flash_attn import flash_attn_func
+
+
+def forward(
+    self,
+    hidden_states: torch.Tensor,
+    alibi: Optional[torch.Tensor],
+    attention_mask: torch.Tensor,  # pylint: disable=unused-argument
+    layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    head_mask: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
+    use_cache: bool = False,
+    output_attentions: bool = False,  # pylint: disable=unused-argument
+):
+    fused_qkv = self.query_key_value(
+        hidden_states
+    )  # [batch_size, seq_length, 3 x hidden_size]
+    num_kv_heads = (
+        self.num_heads if self.new_decoder_architecture else self.num_kv_heads
+    )
+    # 3 x [batch_size, seq_length, num_heads, head_dim]
+    (
+        query_layer,
+        key_layer,
+        value_layer,
+    ) = self._split_heads(  # pylint: disable=protected-access
+        fused_qkv
+    )
+
+    batch_size, query_length, _, _ = query_layer.shape
+
+    query_layer = query_layer.transpose(1, 2).reshape(
+        batch_size * self.num_heads, query_length, self.head_dim
+    )
+    key_layer = key_layer.transpose(1, 2).reshape(
+        batch_size * num_kv_heads,
+        query_length,
+        self.head_dim,
+    )
+    value_layer = value_layer.transpose(1, 2).reshape(
+        batch_size * num_kv_heads, query_length, self.head_dim
+    )
+
+    past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
+    query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
+
+    if layer_past is not None:
+        past_key, past_value = layer_past
+        # concatenate along seq_length dimension:
+        #  - key: [batch_size * self.num_heads, kv_length, head_dim]
+        #  - value: [batch_size * self.num_heads, kv_length, head_dim]
+        key_layer = torch.cat((past_key, key_layer), dim=1)
+        value_layer = torch.cat((past_value, value_layer), dim=1)
+
+    # unused
+    # _, kv_length, _ = key_layer.shape
+    if use_cache:
+        present = (key_layer, value_layer)
+    else:
+        present = None
+    # unused
+    # attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
+    query_layer_ = (
+        query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
+        .transpose(1, 2)
+        .to(torch.bfloat16)
+    )
+    key_layer_ = (
+        key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
+        .transpose(1, 2)
+        .to(torch.bfloat16)
+    )
+    value_layer_ = (
+        value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
+        .transpose(1, 2)
+        .to(torch.bfloat16)
+    )
+
+    if alibi is not None:
+        raise ValueError("`alibi` is not supported when `use_flash_attn` is True")
+
+    # below output will have shape (batch_size, seqlen, nheads, headdim)
+    attn_output = flash_attn_func(query_layer_, key_layer_, value_layer_, causal=True)
+    attn_output = attn_output.reshape(
+        batch_size, query_length, self.num_heads * self.head_dim
+    )
+    output_tensor = self.dense(attn_output)
+    return output_tensor, present
+
+
+def replace_falcon_attn_with_flash_attn():
+    transformers.models.falcon.modeling_falcon.FalconAttention.forward = forward
--- a/src/axolotl/monkeypatch/fastchat_conversation_turns.py
+++ b/src/axolotl/monkeypatch/fastchat_conversation_turns.py
@@ -1,174 +0,0 @@
-"""
-monkeypatch to add a get_turns method
-"""
-
-import logging
-from typing import Generator, Tuple
-
-from fastchat.conversation import SeparatorStyle
-
-LOG = logging.getLogger("axolotl.monkeypatch.fastchat_conversation_turns")
-
-
-def get_prompt(self) -> str:
-    ret = ""
-    for role, msg in self.get_turns():
-        ret += role + msg
-    return ret
-
-
-def get_turns(  # pylint: disable=too-many-return-statements
-    self,
-) -> Generator[Tuple[str, str], None, None]:
-    """Get the prompt for generation."""
-    system_prompt = self.system_template.format(system_message=self.system_message)
-    if self.sep_style == SeparatorStyle.ADD_COLON_SINGLE:
-        yield "", system_prompt + self.sep
-        for role, message in self.messages:
-            if message:
-                yield role + ": ", message + self.sep
-            else:
-                yield role + ":", ""
-        return
-    if self.sep_style == SeparatorStyle.ADD_COLON_TWO:
-        seps = [self.sep, self.sep2]
-        yield "", system_prompt + seps[0]
-        for i, (role, message) in enumerate(self.messages):
-            if message:
-                yield role + ": ", message + seps[i % 2]
-            else:
-                yield role + ":", ""
-        return
-    if self.sep_style == SeparatorStyle.ADD_COLON_SPACE_SINGLE:
-        yield "", system_prompt + self.sep
-        for role, message in self.messages:
-            if message:
-                yield role + ": ", message + self.sep
-            else:
-                yield role + ": ", ""  # must be end with a space
-        return
-    if self.sep_style == SeparatorStyle.ADD_NEW_LINE_SINGLE:
-        yield "", "" if system_prompt == "" else system_prompt + self.sep
-        for role, message in self.messages:
-            if message:
-                yield role + "\n", message + self.sep
-            else:
-                yield role + "\n", ""
-        return
-    if self.sep_style == SeparatorStyle.NO_COLON_SINGLE:
-        yield "", system_prompt
-        for role, message in self.messages:
-            if message:
-                yield role, message + self.sep
-            else:
-                yield role, ""
-        return
-    if self.sep_style == SeparatorStyle.NO_COLON_TWO:
-        seps = [self.sep, self.sep2]
-        yield "", system_prompt
-        for i, (role, message) in enumerate(self.messages):
-            if message:
-                yield role, message + seps[i % 2]
-            else:
-                yield role, ""
-        return
-    if self.sep_style == SeparatorStyle.RWKV:
-        yield "", system_prompt
-        for i, (role, message) in enumerate(self.messages):
-            if message:
-                yield role + ": ", message.replace("\r\n", "\n").replace(
-                    "\n\n", "\n"
-                ) + "\n\n"
-            else:
-                yield role + ":", ""
-        return
-    if self.sep_style == SeparatorStyle.LLAMA2:
-        seps = [self.sep, self.sep2]
-        if self.system_message:
-            yield "", system_prompt
-        else:
-            yield "", "[INST] "
-        for i, (role, message) in enumerate(self.messages[1:]):
-            if message:
-                yield role + " ", message + seps[i % 2]
-            else:
-                yield role, ""
-        return
-    if self.sep_style == SeparatorStyle.CHATGLM:
-        # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
-        # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926
-        round_add_n = 1 if self.name == "chatglm2" else 0
-        if system_prompt:
-            yield "", system_prompt + self.sep
-
-        for i, (role, message) in enumerate(self.messages):
-            if i % 2 == 0:
-                yield "", f"[Round {i//2 + round_add_n}]{self.sep}"
-
-            if message:
-                yield f"{role}：", f"{message}{self.sep}"
-            else:
-                yield f"{role}：", ""
-        return
-    if self.sep_style == SeparatorStyle.CHATML:
-        yield "", "" if system_prompt == "" else system_prompt + self.sep + "\n"
-        for role, message in self.messages:
-            if message:
-                yield role + "\n", message + self.sep + "\n"
-            else:
-                yield role + "\n", ""
-        return
-    if self.sep_style == SeparatorStyle.CHATINTERN:
-        # source: https://huggingface.co/internlm/internlm-chat-7b-8k/blob/bd546fa984b4b0b86958f56bf37f94aa75ab8831/modeling_internlm.py#L771
-        seps = [self.sep, self.sep2]
-        yield "", system_prompt
-        for i, (role, message) in enumerate(self.messages):
-            prefix = "<s>" if i % 2 == 0 else ""
-            if message:
-                yield prefix + role + ":", message + seps[i % 2] + "\n"
-            else:
-                yield role + ":", ""
-        return
-    if self.sep_style == SeparatorStyle.DOLLY:
-        seps = [self.sep, self.sep2]
-        yield "", system_prompt
-        for i, (role, message) in enumerate(self.messages):
-            if message:
-                suffix = "\n\n" if i % 2 == 1 else ""
-                yield role + ":\n", message + seps[i % 2] + suffix
-            else:
-                yield role + ":\n", ""
-        return
-    if self.sep_style == SeparatorStyle.PHOENIX:
-        yield "", system_prompt
-        for role, message in self.messages:
-            if message:
-                yield role + ": ", "<s>" + message + "</s>"
-            else:
-                yield role + ": " + "<s>", ""
-        return
-    if self.sep_style == SeparatorStyle.ROBIN:
-        yield "", system_prompt + self.sep
-        for role, message in self.messages:
-            if message:
-                yield role + ":\n", message + self.sep
-            else:
-                yield role + ":\n", ""
-        return
-    if self.sep_style == SeparatorStyle.FALCON_CHAT:
-        if self.system_message:
-            yield "", system_prompt + self.sep
-        for role, message in self.messages:
-            if message:
-                yield role + ": ", message + self.sep
-            else:
-                yield role + ":", ""
-    else:
-        raise ValueError(f"Invalid style: {self.sep_style}")
-
-
-def add_get_turns_to_conversation():
-    import fastchat.conversation
-
-    fastchat.conversation.Conversation.get_turns = get_turns
-    fastchat.conversation.Conversation.get_prompt = get_prompt
--- a/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
@@ -12,6 +12,8 @@ import torch.nn.functional as F
 import transformers
 from einops import rearrange
 from flash_attn.bert_padding import pad_input, unpad_input
+from torch import nn
+from transformers import LlamaConfig
 from transformers.modeling_outputs import BaseModelOutputWithPast
 from transformers.models.llama.modeling_llama import (
    LlamaDecoderLayer as OriginalLlamaDecoderLayer,
@@ -38,62 +40,57 @@ except ImportError:
 LOG = logging.getLogger("axolotl")


-def replace_llama_attn_with_flash_attn(
-    packed: Optional[bool] = False,
-    cross_entropy: Optional[bool] = False,
-    rms_norm: Optional[bool] = False,
-    noisy_embeddings_alpha: Optional[int] = False,
-):
+def replace_llama_attn_with_flash_attn(packed: Optional[bool] = False):
    transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
        _prepare_decoder_attention_mask
    )
    transformers.models.llama.modeling_llama.LlamaAttention.forward = flashattn_forward
-    if noisy_embeddings_alpha:
-        transformers.models.llama.modeling_llama.LlamaModel.get_inputs_embeds = partial(
-            llama_model_get_inputs_embeds, noisy_embeddings_alpha=noisy_embeddings_alpha
-        )
-    else:
-        transformers.models.llama.modeling_llama.LlamaModel.get_inputs_embeds = (
-            llama_model_get_inputs_embeds
-        )
-
    if packed:
        transformers.models.llama.modeling_llama.LlamaDecoderLayer = LlamaDecoderLayer
        transformers.models.llama.modeling_llama.LlamaModel.forward = (
            llama_model_forward
        )

-    # skip only if explicitly disabled
-    if cross_entropy:
-        try:
-            from flash_attn.losses.cross_entropy import CrossEntropyLoss
+    try:
+        from flash_attn.losses.cross_entropy import CrossEntropyLoss

-            LOG.info("patching with flash_attn.losses.cross_entropy")
-            transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
-                CrossEntropyLoss, inplace_backward=True
-            )
-        except ImportError:
-            LOG.info(
-                "optimized flash-attention CrossEntropyLoss not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=xentropy_cuda_lib&subdirectory=csrc/xentropy'`)"
-            )
+        LOG.info("patching with flash_attn.losses.cross_entropy")
+        transformers.models.llama.modeling_llama.CrossEntropyLoss = partial(
+            CrossEntropyLoss, inplace_backward=True
+        )
+    except ImportError:
+        LOG.info(
+            "optimized flash-attention CrossEntropyLoss not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=xentropy_cuda_lib&subdirectory=csrc/xentropy'`)"
+        )

-    # skip only if explicitly disabled
-    if rms_norm:
-        try:
-            from flash_attn.ops.rms_norm import RMSNorm
+    try:
+        from flash_attn.ops.rms_norm import RMSNorm

-            class LlamaRMSNorm(RMSNorm):
-                """Patched LLamaRMSNorm"""
+        class LlamaRMSNorm(RMSNorm):
+            """Patched LLamaRMSNorm"""

-                def __init__(self, hidden_size, eps=1e-6):
-                    super().__init__(hidden_size, eps=eps)
+            def __init__(self, hidden_size, eps=1e-6):
+                super().__init__(hidden_size, eps=eps)

-            LOG.info("patching with flash_attn.ops.rms_norm")
-            transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
-        except ImportError:
-            LOG.info(
-                "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
-            )
+        LOG.info("patching with flash_attn.ops.rms_norm")
+        transformers.models.llama.modeling_llama.LlamaRMSNorm = LlamaRMSNorm
+    except ImportError:
+        LOG.info(
+            "optimized flash-attention RMSNorm not found (run `pip install 'git+https://github.com/Dao-AILab/flash-attention.git#egg=dropout_layer_norm&subdirectory=csrc/layer_norm'`)"
+        )
+
+
+class GaussianDropout(nn.Module):
+    def __init__(self, p=0.5):
+        super(GaussianDropout, self).__init__()
+        if p <= 0 or p >= 1:
+            raise Exception("p value should accomplish 0 < p < 1")
+        self.p = p
+
+    def forward(self, x):
+        stddev = (self.p / (1.0 - self.p)) ** 0.5
+        epsilon = torch.randn_like(x) * stddev
+        return x * epsilon


 # Disable the transformation of the attention mask in LlamaModel as the flash attention
@@ -117,7 +114,6 @@ def flashattn_forward(
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
-    padding_mask: Optional[torch.LongTensor] = None,  # pylint: disable=unused-argument
    cu_seqlens: Optional[torch.Tensor] = None,
    max_seqlen: Optional[torch.Tensor] = None,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
@@ -221,7 +217,7 @@ def flashattn_forward(
        qkv = rearrange(qkv, "b s ... -> (b s) ...")

        output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
+            qkv, cu_seqlens, max_seqlen, dropout_p=0.0, softmax_scale=None, causal=True
        )
        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
    elif query_states.shape == key_states.shape:
@@ -421,28 +417,6 @@ def generate_qkv(
    )


-def llama_model_get_inputs_embeds(
-    self,
-    input_ids: torch.LongTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    noisy_embeddings_alpha: Optional[int] = None,
-):
-    inputs_embeds = self.embed_tokens(input_ids)
-
-    if noisy_embeddings_alpha:
-        input_mask = attention_mask.to(inputs_embeds)  # B x L
-        input_lengths = torch.sum(input_mask, 1)  # B
-
-        noise_ = torch.zeros_like(inputs_embeds).uniform_(-1, 1)
-        delta = noise_ * input_mask.unsqueeze(2)
-        dims = input_lengths * inputs_embeds.size(-1)
-        mag = noisy_embeddings_alpha / torch.sqrt(dims)
-        delta = (delta * mag.view(-1, 1, 1)).detach()
-        inputs_embeds += delta
-
-    return inputs_embeds
-
-
 def llama_model_forward(
    self,
    input_ids: torch.LongTensor = None,
@@ -509,8 +483,7 @@ def llama_model_forward(
        cu_seqlens = cu_seqlens.squeeze()

    if inputs_embeds is None:
-        inputs_embeds = self.get_inputs_embeds(input_ids, attention_mask)
-
+        inputs_embeds = self.embed_tokens(input_ids)
    # embed positions
    if attention_mask is None:
        attention_mask = torch.ones(
@@ -518,13 +491,6 @@ def llama_model_forward(
            dtype=torch.bool,
            device=inputs_embeds.device,
        )
-        padding_mask = None
-    else:
-        if 0 in attention_mask:
-            padding_mask = attention_mask
-        else:
-            padding_mask = None
-
    attention_mask = (
        self._prepare_decoder_attention_mask(  # pylint: disable=protected-access
            attention_mask,
@@ -559,9 +525,7 @@ def llama_model_forward(
            def create_custom_forward(module):
                def custom_forward(*inputs):
                    # None for past_key_value
-                    return module(
-                        *inputs,
-                    )
+                    return module(*inputs)

                return custom_forward

@@ -570,10 +534,9 @@ def llama_model_forward(
                hidden_states,
                attention_mask,
                position_ids,
-                past_key_value,
+                None,
                output_attentions,
                None,
-                padding_mask,
                cu_seqlens,
                max_seqlen,
            )
@@ -585,7 +548,6 @@ def llama_model_forward(
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
-                padding_mask=padding_mask,
                cu_seqlens=cu_seqlens,
                max_seqlen=max_seqlen,
            )
@@ -624,6 +586,15 @@ class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
    patched version of LlamaDecoderLayer to pass through the precalculated cu_seqlens
    """

+    def __init__(self, config: LlamaConfig):
+        super(LlamaDecoderLayer, self).__init__(config)
+        self.attn_dropout = None
+        self.mlp_dropout = None
+        if config.dropout_attn:
+            self.attn_dropout = GaussianDropout(p=config.dropout_attn)
+        if config.dropout_mlp:
+            self.mlp_dropout = GaussianDropout(p=config.dropout_mlp)
+
    def forward(
        self,
        hidden_states: torch.Tensor,
@@ -632,7 +603,6 @@ class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
-        padding_mask: Optional[torch.LongTensor] = None,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[torch.Tensor] = None,
    ) -> Tuple[
@@ -665,16 +635,19 @@ class LlamaDecoderLayer(OriginalLlamaDecoderLayer):
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
-            padding_mask=padding_mask,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
        )
+        if self.training and self.attn_dropout:
+            hidden_states = self.attn_dropout(hidden_states)
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
+        if self.training and self.mlp_dropout:
+            hidden_states = self.mlp_dropout(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)
--- a/src/axolotl/monkeypatch/llama_embeddings_hijack.py
+++ b/src/axolotl/monkeypatch/llama_embeddings_hijack.py
@@ -1,40 +0,0 @@
-"""
-patch to add noisy embeddings per https://arxiv.org/abs/2310.05914
-"""
-
-import torch
-import transformers.models.llama.modeling_llama
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-def replace_llama_embeddings_with_uniform_distribution(noise_alpha=5):
-    # pylint: disable=duplicate-code
-    def noised_embed(orig_embed, noise_alpha, model):
-        def new_func(input_ids):
-            # during training, we add noise to the embedding
-            # during generation, we don't add noise to the embedding
-            if model.training:
-                embed_init = orig_embed(input_ids)
-                dims = torch.tensor(embed_init.size(1) * embed_init.size(2))
-                mag_norm = noise_alpha / torch.sqrt(dims)
-                return embed_init + torch.zeros_like(embed_init).uniform_(
-                    -mag_norm, mag_norm
-                )
-            return orig_embed(input_ids)
-
-        return new_func
-
-    def post_init(orig_post_init):
-        def new_func(self):
-            orig_post_init(self)
-            self.embed_tokens.forward = noised_embed(
-                self.embed_tokens.forward, noise_alpha, self
-            )
-
-        return new_func
-
-    transformers.models.llama.modeling_llama.LlamaModel.post_init = post_init(
-        transformers.models.llama.modeling_llama.LlamaModel.post_init
-    )
--- a/src/axolotl/monkeypatch/mistral_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/mistral_attn_hijack_flash.py
@@ -1,541 +0,0 @@
-"""Flash attention monkey patch for mistral model"""
-# pylint: disable=duplicate-code
-
-import logging
-from typing import List, Optional, Tuple, Union
-
-import torch
-import transformers
-from einops import rearrange
-from flash_attn.bert_padding import pad_input, unpad_input
-from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
-    flash_attn_kvpacked_func,
-    flash_attn_varlen_kvpacked_func,
-    flash_attn_varlen_qkvpacked_func,
-)
-from transformers.modeling_outputs import BaseModelOutputWithPast
-from transformers.models.mistral.modeling_mistral import (
-    MistralDecoderLayer as OriginalMistralDecoderLayer,
-)
-from transformers.models.mistral.modeling_mistral import apply_rotary_pos_emb, repeat_kv
-
-from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-
-LOG = logging.getLogger("axolotl.monkeypatch.mistral")
-
-
-def replace_mistral_attn_with_flash_attn(
-    packed: Optional[bool] = False,
-):
-    transformers.models.mistral.modeling_mistral.MistralModel._prepare_decoder_attention_mask = (  # pylint: disable=protected-access
-        _prepare_decoder_attention_mask
-    )
-    transformers.models.mistral.modeling_mistral.MistralAttention.forward = (
-        flashattn_forward
-    )
-    if packed:
-        transformers.models.mistral.modeling_mistral.MistralDecoderLayer = (
-            MistralDecoderLayer
-        )
-        transformers.models.mistral.modeling_mistral.MistralModel.forward = (
-            mistral_model_forward
-        )
-
-
-# Disable the transformation of the attention mask in LlamaModel as the flash attention
-# requires the attention mask to be the same as the key_padding_mask
-def _prepare_decoder_attention_mask(
-    self,
-    attention_mask,
-    input_shape,
-    inputs_embeds,
-    past_key_values_length,
-    sliding_window,
-):  # pylint: disable=unused-argument
-    # [bsz, seq_len]
-    return attention_mask
-
-
-def flashattn_forward(
-    self,
-    hidden_states: torch.Tensor,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: bool = False,
-    use_cache: bool = False,
-    cu_seqlens: Optional[torch.Tensor] = None,
-    max_seqlen: Optional[torch.Tensor] = None,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    bsz, q_len, _ = hidden_states.size()
-
-    query_states = self.q_proj(hidden_states)
-    key_states = self.k_proj(hidden_states)
-    value_states = self.v_proj(hidden_states)
-
-    query_states = query_states.view(
-        bsz, q_len, self.num_heads, self.head_dim
-    ).transpose(1, 2)
-    key_states = key_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
-    value_states = value_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = apply_rotary_pos_emb(
-        query_states, key_states, cos, sin, position_ids
-    )
-
-    if past_key_value is not None:
-        # reuse k, v, self_attention
-        key_states = torch.cat([past_key_value[0], key_states], dim=2)
-        value_states = torch.cat([past_key_value[1], value_states], dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    # repeat k/v heads if n_kv_heads < n_heads
-    key_states = repeat_kv(key_states, self.num_key_value_groups)
-    value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-    if self.training:
-        # during training q,k,v always have same seqlen
-        assert key_states.shape == query_states.shape
-        is_causal = True
-    else:
-        # turn off FA causal mask after first inference autoregressive iteration
-        # only on first autoregressive step q,k,v have same seqlen
-        is_causal = key_states.shape == query_states.shape
-
-    if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
-        # special handling using sample packing
-        qkv = torch.stack(
-            [query_states, key_states, value_states], dim=2
-        )  # [bsz, nh, 3, q_len, hd]
-        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
-        qkv = rearrange(qkv, "b s ... -> (b s) ...")
-
-        output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=None, causal=True
-        )
-        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-    elif query_states.shape == key_states.shape:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        qkv_unpad, cu_seqlens_q, max_seqlen_q, _, output_pad_fn = generate_qkv(
-            query_states,
-            key_states,
-            value_states,
-            qkvpacked=True,
-            # We have disabled _prepare_decoder_attention_mask in LlamaModel
-            # the attention_mask should be the same as the key_padding_mask
-            key_padding_mask=attention_mask,
-            query_padding_mask=attention_mask[:, -query_states.size(1) :]
-            if attention_mask is not None
-            else None,
-        )
-        output_unpad = flash_attn_varlen_qkvpacked_func(
-            qkv_unpad,
-            cu_seqlens_q,
-            max_seqlen_q,
-            0.0,
-            softmax_scale=None,
-            causal=is_causal,
-        )
-        output = output_pad_fn(output_unpad)
-    else:
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-        if attention_mask is None or attention_mask.all().item():
-            output = flash_attn_kvpacked_func(
-                query_states,
-                torch.stack([key_states, value_states], 2),
-                causal=is_causal,
-            )
-        else:
-            (  # pylint: disable=unbalanced-tuple-unpacking
-                q_unpad,
-                kv_unpad,
-                cu_seqlens_q,
-                cu_seqlens_k,
-                max_seqlen_q,
-                max_seqlen_k,
-                _,
-                _,
-                output_pad_fn,
-            ) = generate_qkv(
-                query_states,
-                key_states,
-                value_states,
-                kvpacked=True,
-                key_padding_mask=attention_mask,
-                query_padding_mask=attention_mask[:, -query_states.size(1) :]
-                if attention_mask is not None
-                else None,
-            )
-            if q_unpad.dtype != kv_unpad.dtype:
-                kv_unpad = kv_unpad.to(q_unpad.dtype)
-            output_unpad = flash_attn_varlen_kvpacked_func(
-                q_unpad,
-                kv_unpad,
-                cu_seqlens_q,
-                cu_seqlens_k,
-                max_seqlen_q,
-                max_seqlen_k,
-                0.0,
-                softmax_scale=None,
-                causal=is_causal,
-            )
-            output = output_pad_fn(output_unpad)
-
-    attn_output = output
-    if attn_output.size() != (bsz, q_len, self.num_heads, self.head_dim):
-        raise ValueError(
-            f"`attn_output` should be of size {(bsz, q_len, self.num_heads, self.head_dim)}, but is"
-            f" {attn_output.size()}"
-        )
-    attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
-
-    attn_output = self.o_proj(attn_output)
-
-    if not output_attentions:
-        attn_weights = None
-
-    return attn_output, attn_weights, past_key_value
-
-
-# based on https://github.com/Dao-AILab/flash-attention/blob/364a5b/tests/test_flash_attn.py#L38
-def generate_qkv(
-    q,
-    k,
-    v,
-    query_padding_mask=None,
-    key_padding_mask=None,
-    kvpacked=False,
-    qkvpacked=False,
-):  # pylint: disable=invalid-name,unnecessary-lambda-assignment
-    """
-    Arguments:
-        q: (batch_size, seqlen_q, nheads, d)
-        k: (batch_size, seqlen_k, nheads_k, d)
-        v: (batch_size, seqlen_k, nheads_k, d)
-        query_padding_mask: (batch_size, seqlen), bool
-        key_padding_mask: (batch_size, seqlen), bool
-    """
-    assert not (kvpacked and qkvpacked)
-    batch_size, seqlen_q, nheads, d = q.shape
-    _, seqlen_k, nheads_k, _ = k.shape
-    assert k.shape == (batch_size, seqlen_k, nheads_k, d)
-    assert v.shape == (batch_size, seqlen_k, nheads_k, d)
-
-    if query_padding_mask is not None:
-        q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(
-            q, query_padding_mask
-        )
-
-        output_pad_fn = lambda output_unpad: pad_input(  # noqa: E731
-            output_unpad, indices_q, batch_size, seqlen_q
-        )
-
-    else:
-        q_unpad = rearrange(q, "b s h d -> (b s) h d")
-        cu_seqlens_q = torch.arange(
-            0,
-            (batch_size + 1) * seqlen_q,
-            step=seqlen_q,
-            dtype=torch.int32,
-            device=q_unpad.device,
-        )
-        max_seqlen_q = seqlen_q
-
-        output_pad_fn = lambda output_unpad: rearrange(  # noqa: E731
-            output_unpad, "(b s) h d -> b s h d", b=batch_size
-        )
-
-    if key_padding_mask is not None:
-        k_unpad, _, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask)
-        v_unpad, _, _, _ = unpad_input(v, key_padding_mask)
-    else:
-        k_unpad = rearrange(k, "b s h d -> (b s) h d")
-        v_unpad = rearrange(v, "b s h d -> (b s) h d")
-        cu_seqlens_k = torch.arange(
-            0,
-            (batch_size + 1) * seqlen_k,
-            step=seqlen_k,
-            dtype=torch.int32,
-            device=k_unpad.device,
-        )
-        max_seqlen_k = seqlen_k
-
-    if qkvpacked:
-        assert nheads == nheads_k
-        qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1)
-        qkv = torch.stack([q, k, v], dim=2)
-        return (qkv_unpad, cu_seqlens_q, max_seqlen_q, qkv, output_pad_fn)
-
-    if kvpacked:
-        kv_unpad = torch.stack([k_unpad, v_unpad], dim=1)
-        kv = torch.stack([k, v], dim=2)
-        return (
-            q_unpad,
-            kv_unpad,
-            cu_seqlens_q,
-            cu_seqlens_k,
-            max_seqlen_q,
-            max_seqlen_k,
-            q,
-            kv,
-            output_pad_fn,
-        )
-
-    return (
-        q_unpad,
-        k_unpad,
-        v_unpad,
-        cu_seqlens_q,
-        cu_seqlens_k,
-        max_seqlen_q,
-        max_seqlen_k,
-        q,
-        k,
-        v,
-        output_pad_fn,
-    )
-
-
-def mistral_model_forward(
-    self,
-    input_ids: torch.LongTensor = None,
-    attention_mask: Optional[torch.Tensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[List[torch.FloatTensor]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-) -> Union[Tuple, BaseModelOutputWithPast]:
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # retrieve input_ids and inputs_embeds
-    if input_ids is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
-        )
-    if input_ids is not None:
-        batch_size, seq_length = input_ids.shape
-    elif inputs_embeds is not None:
-        batch_size, seq_length, _ = inputs_embeds.shape
-    else:
-        raise ValueError(
-            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
-        )
-
-    seq_length_with_past = seq_length
-    past_key_values_length = 0
-
-    if past_key_values is not None:
-        past_key_values_length = past_key_values[0][0].shape[2]
-        seq_length_with_past = seq_length_with_past + past_key_values_length
-
-    cu_seqlens = None
-    max_seqlen = None
-    if position_ids is None:
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-        position_ids = torch.arange(
-            past_key_values_length,
-            seq_length + past_key_values_length,
-            dtype=torch.long,
-            device=device,
-        )
-        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-    else:
-        position_ids = position_ids.view(-1, seq_length).long()
-        cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
-        cu_seqlens = cu_seqlens.squeeze()
-
-    if inputs_embeds is None:
-        inputs_embeds = self.embed_tokens(input_ids)
-    # embed positions
-    if attention_mask is None:
-        attention_mask = torch.ones(
-            (batch_size, seq_length_with_past),
-            dtype=torch.bool,
-            device=inputs_embeds.device,
-        )
-    attention_mask = (
-        self._prepare_decoder_attention_mask(  # pylint: disable=protected-access
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-            sliding_window=self.config.sliding_window,
-        )
-    )
-
-    hidden_states = inputs_embeds
-
-    if self.gradient_checkpointing and self.training:
-        if use_cache:
-            transformers.logger.warning_once(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-            )
-            use_cache = False
-
-    # decoder layers
-    all_hidden_states = () if output_hidden_states else None
-    all_self_attns = () if output_attentions else None
-    next_decoder_cache = () if use_cache else None
-
-    for idx, decoder_layer in enumerate(self.layers):
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        past_key_value = past_key_values[idx] if past_key_values is not None else None
-
-        if self.gradient_checkpointing and self.training:
-
-            def create_custom_forward(module):
-                def custom_forward(*inputs):
-                    # None for past_key_value
-                    return module(*inputs)
-
-                return custom_forward
-
-            layer_outputs = torch.utils.checkpoint.checkpoint(
-                create_custom_forward(decoder_layer),
-                hidden_states,
-                attention_mask,
-                position_ids,
-                past_key_value,
-                output_attentions,
-                None,
-                cu_seqlens,
-                max_seqlen,
-            )
-        else:
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_value,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cu_seqlens=cu_seqlens,
-                max_seqlen=max_seqlen,
-            )
-
-        hidden_states = layer_outputs[0]
-
-        if use_cache:
-            next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-
-        if output_attentions:
-            all_self_attns += (layer_outputs[1],)
-
-    hidden_states = self.norm(hidden_states)
-
-    # add hidden states from the last decoder layer
-    if output_hidden_states:
-        all_hidden_states += (hidden_states,)
-
-    next_cache = next_decoder_cache if use_cache else None
-    if not return_dict:
-        return tuple(
-            v
-            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
-            if v is not None
-        )
-    return BaseModelOutputWithPast(
-        last_hidden_state=hidden_states,
-        past_key_values=next_cache,
-        hidden_states=all_hidden_states,
-        attentions=all_self_attns,
-    )
-
-
-class MistralDecoderLayer(OriginalMistralDecoderLayer):
-    """
-    patched version of MistralDecoderLayer to pass through the precalculated cu_seqlens
-    """
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cu_seqlens: Optional[torch.Tensor] = None,
-        max_seqlen: Optional[torch.Tensor] = None,
-    ) -> Tuple[
-        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
-    ]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-            cu_seqlens (`torch.Tensor`, *optional*) cumulative sequence len when packing
-        """
-
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
--- a/src/axolotl/monkeypatch/mistral_embeddings_hijack.py
+++ b/src/axolotl/monkeypatch/mistral_embeddings_hijack.py
@@ -1,40 +0,0 @@
-"""
-patch to add noisy embeddings per https://arxiv.org/abs/2310.05914
-"""
-
-import torch
-import transformers.models.mistral.modeling_mistral
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-def replace_mistral_embeddings_with_uniform_distribution(noise_alpha=5):
-    # pylint: disable=duplicate-code
-    def noised_embed(orig_embed, noise_alpha, model):
-        def new_func(input_ids):
-            # during training, we add noise to the embedding
-            # during generation, we don't add noise to the embedding
-            if model.training:
-                embed_init = orig_embed(input_ids)
-                dims = torch.tensor(embed_init.size(1) * embed_init.size(2))
-                mag_norm = noise_alpha / torch.sqrt(dims)
-                return embed_init + torch.zeros_like(embed_init).uniform_(
-                    -mag_norm, mag_norm
-                )
-            return orig_embed(input_ids)
-
-        return new_func
-
-    def post_init(orig_post_init):
-        def new_func(self):
-            orig_post_init(self)
-            self.embed_tokens.forward = noised_embed(
-                self.embed_tokens.forward, noise_alpha, self
-            )
-
-        return new_func
-
-    transformers.models.mistral.modeling_mistral.MistralModel.post_init = post_init(
-        transformers.models.mistral.modeling_mistral.MistralModel.post_init
-    )
--- a/src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/stablelm_attn_hijack_flash.py
@@ -1,415 +0,0 @@
-# coding=utf-8
-# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-# This code is based off the following work:
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
-# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
-""" PyTorch StableLM Epoch model. """
-import importlib
-import math
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.utils.checkpoint
-from accelerate import init_empty_weights
-from einops import rearrange
-from flash_attn.flash_attn_interface import (  # pylint: disable=ungrouped-imports
-    flash_attn_varlen_qkvpacked_func,
-)
-from torch import nn
-from transformers import AutoConfig, AutoModelForCausalLM
-from transformers.modeling_outputs import BaseModelOutputWithPast
-from transformers.utils import logging
-
-from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-
-logger = logging.get_logger(__name__)
-
-
-def replace_stablelm_attn_with_flash_attn(model_name="stabilityai/stablelm-3b-4e1t"):
-    # this is a wonky hack to get the remotely loaded module
-    model_config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
-    # we need to load the model here in order for modeling_stablelm_epoch to be available
-    with init_empty_weights():
-        AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
-    module_name = model_config.__class__.__module__.replace(
-        ".configuration_stablelm_epoch", ".modeling_stablelm_epoch"
-    )
-    modeling_stablelm = importlib.import_module(module_name)
-    modeling_stablelm.Attention.forward = (  # pylint: disable=protected-access
-        flashattn_attn
-    )
-    modeling_stablelm.StableLMEpochModel.forward = (  # pylint: disable=protected-access
-        stablelm_model_forward
-    )
-    modeling_stablelm.DecoderLayer.forward = (  # pylint: disable=protected-access
-        decoder_layer_forward
-    )
-
-
-def rotate_half(x: torch.Tensor):
-    """Rotates half the hidden dims of the input."""
-    # pylint: disable=invalid-name
-    x1, x2 = torch.chunk(x, 2, dim=-1)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
-    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
-    # pylint: disable=invalid-name
-    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
-    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
-    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
-    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
-    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(
-        batch, num_key_value_heads, n_rep, slen, head_dim
-    )
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def flashattn_attn(
-    self,
-    hidden_states: torch.FloatTensor,
-    attention_mask: torch.FloatTensor,
-    position_ids: torch.LongTensor,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: Optional[bool] = False,  # pylint: disable=unused-argument
-    use_cache: Optional[bool] = False,
-    cu_seqlens: Optional[torch.Tensor] = None,
-    max_seqlen: Optional[torch.Tensor] = None,
-) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    bsz, q_len, _ = hidden_states.size()
-
-    query_states = self.q_proj(hidden_states)
-    key_states = self.k_proj(hidden_states)
-    value_states = self.v_proj(hidden_states)
-
-    query_states = query_states.view(
-        bsz, q_len, self.num_heads, self.head_dim
-    ).transpose(1, 2)
-    key_states = key_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
-    value_states = value_states.view(
-        bsz, q_len, self.num_key_value_heads, self.head_dim
-    ).transpose(1, 2)
-
-    query_rot = query_states[..., : self.rotary_ndims]
-    query_pass = query_states[..., self.rotary_ndims :]
-    key_rot = key_states[..., : self.rotary_ndims]
-    key_pass = key_states[..., self.rotary_ndims :]
-
-    kv_seq_len = key_states.shape[-2]
-    if past_key_value is not None:
-        kv_seq_len += past_key_value[0].shape[-2]
-    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-    query_states, key_states = apply_rotary_pos_emb(
-        query_rot, key_rot, cos, sin, position_ids
-    )
-
-    # [batch_size, num_heads, seq_len, head_dim]
-    query_states = torch.cat((query_states, query_pass), dim=-1)
-    key_states = torch.cat((key_states, key_pass), dim=-1)
-
-    if past_key_value is not None:
-        # Reuse k, v, self_attention
-        key_states = torch.cat((past_key_value[0], key_states), dim=2)
-        value_states = torch.cat((past_key_value[1], value_states), dim=2)
-
-    past_key_value = (key_states, value_states) if use_cache else None
-
-    # Repeat k/v heads if n_kv_heads < n_heads
-    key_states = repeat_kv(key_states, self.num_key_value_groups)
-    value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-    if cu_seqlens is not None and max_seqlen is not None and cu_seqlens.dim() == 1:
-        # special handling using sample packing
-        qkv = torch.stack(
-            [query_states, key_states, value_states], dim=2
-        )  # [bsz, nh, 3, q_len, hd]
-        qkv = qkv.transpose(1, 3)  # [bsz, q_len, 3, nh, hd]
-        qkv = rearrange(qkv, "b s ... -> (b s) ...")
-        softmax_scale = None
-
-        output = flash_attn_varlen_qkvpacked_func(
-            qkv, cu_seqlens, max_seqlen, 0.0, softmax_scale=softmax_scale, causal=True
-        )
-
-        attn_output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
-        attn_output = rearrange(attn_output, "b s h d -> b s (h d)")
-    else:
-        attn_weights = torch.matmul(
-            query_states, key_states.transpose(2, 3)
-        ) / math.sqrt(self.head_dim)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights + attention_mask
-
-        # Upcast attention to fp32
-        attn_weights = nn.functional.softmax(
-            attn_weights, dim=-1, dtype=torch.float32
-        ).to(query_states.dtype)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        # Merge heads
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-    # Final linear projection
-    attn_output = self.o_proj(attn_output)
-
-    return attn_output, None, past_key_value
-
-
-def decoder_layer_forward(
-    self,
-    hidden_states: Optional[torch.FloatTensor],
-    attention_mask: Optional[torch.FloatTensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_value: Optional[Tuple[torch.Tensor]] = None,
-    output_attentions: Optional[bool] = False,
-    use_cache: Optional[bool] = False,
-    cu_seqlens: Optional[torch.Tensor] = None,
-    max_seqlen: Optional[torch.Tensor] = None,
-) -> Union[
-    Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]
-]:
-    # pylint: disable=duplicate-code
-    residual = hidden_states
-
-    hidden_states = self.input_layernorm(hidden_states)
-
-    # Self Attention
-    hidden_states, self_attn_weights, present_key_value = self.self_attn(
-        hidden_states=hidden_states,
-        attention_mask=attention_mask,
-        position_ids=position_ids,
-        past_key_value=past_key_value,
-        output_attentions=output_attentions,
-        use_cache=use_cache,
-        cu_seqlens=cu_seqlens,
-        max_seqlen=max_seqlen,
-    )
-    hidden_states = residual + hidden_states
-
-    # Fully Connected
-    residual = hidden_states
-    hidden_states = self.post_attention_layernorm(hidden_states)
-    hidden_states = self.mlp(hidden_states)
-    hidden_states = residual + hidden_states
-
-    outputs = (hidden_states,)
-
-    if output_attentions:
-        outputs += (self_attn_weights,)
-
-    if use_cache:
-        outputs += (present_key_value,)
-
-    return outputs
-
-
-def stablelm_model_forward(
-    self,
-    input_ids: Optional[torch.LongTensor] = None,
-    attention_mask: Optional[torch.FloatTensor] = None,
-    position_ids: Optional[torch.LongTensor] = None,
-    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
-    inputs_embeds: Optional[torch.FloatTensor] = None,
-    use_cache: Optional[bool] = None,
-    output_attentions: Optional[bool] = None,
-    output_hidden_states: Optional[bool] = None,
-    return_dict: Optional[bool] = None,
-) -> Union[Tuple, BaseModelOutputWithPast]:
-    # pylint: disable=duplicate-code
-    output_attentions = (
-        output_attentions
-        if output_attentions is not None
-        else self.config.output_attentions
-    )
-    output_hidden_states = (
-        output_hidden_states
-        if output_hidden_states is not None
-        else self.config.output_hidden_states
-    )
-    use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-    return_dict = (
-        return_dict if return_dict is not None else self.config.use_return_dict
-    )
-
-    # Retrieve input_ids and inputs_embeds
-    if input_ids is not None and inputs_embeds is not None:
-        raise ValueError(
-            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
-        )
-    if input_ids is not None:
-        batch_size, seq_length = input_ids.shape
-    elif inputs_embeds is not None:
-        batch_size, seq_length, _ = inputs_embeds.shape
-    else:
-        raise ValueError(
-            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
-        )
-
-    seq_length_with_past = seq_length
-    past_key_values_length = 0
-
-    if past_key_values is not None:
-        past_key_values_length = past_key_values[0][0].shape[2]
-        seq_length_with_past = seq_length_with_past + past_key_values_length
-
-    cu_seqlens = None
-    max_seqlen = None
-    if position_ids is None:
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-        position_ids = torch.arange(
-            past_key_values_length,
-            seq_length + past_key_values_length,
-            dtype=torch.long,
-            device=device,
-        )
-        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
-    else:
-        position_ids = position_ids.view(-1, seq_length).long()
-        cu_seqlens, max_seqlen = get_cu_seqlens_from_pos_ids(position_ids)
-        cu_seqlens = cu_seqlens.squeeze()
-
-    if inputs_embeds is None:
-        inputs_embeds = self.embed_tokens(input_ids)
-    # Embed positions
-    if attention_mask is None:
-        attention_mask = torch.ones(
-            (batch_size, seq_length_with_past),
-            dtype=torch.bool,
-            device=inputs_embeds.device,
-        )
-    attention_mask = (
-        self._prepare_decoder_attention_mask(  # pylint: disable=protected-access
-            attention_mask,
-            (batch_size, seq_length),
-            inputs_embeds,
-            past_key_values_length,
-        )
-    )
-
-    hidden_states = inputs_embeds
-
-    if self.gradient_checkpointing and self.training:
-        if use_cache:
-            logger.warning(
-                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-            )
-            use_cache = False
-
-    # Decoder layers
-    all_hidden_states = () if output_hidden_states else None
-    all_self_attns = () if output_attentions else None
-    next_decoder_cache = () if use_cache else None
-
-    for idx, decoder_layer in enumerate(self.layers):
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        past_key_value = past_key_values[idx] if past_key_values is not None else None
-
-        if self.gradient_checkpointing and self.training:
-
-            def create_custom_forward(module):
-                def custom_forward(*inputs):
-                    # None for past_key_value
-                    return module(*inputs)
-
-                return custom_forward
-
-            layer_outputs = torch.utils.checkpoint.checkpoint(
-                create_custom_forward(decoder_layer),
-                hidden_states,
-                attention_mask,
-                position_ids,
-                past_key_value,
-                output_attentions,
-                None,
-                cu_seqlens,
-                max_seqlen,
-            )
-        else:
-            layer_outputs = decoder_layer(
-                hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_value,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-                cu_seqlens=cu_seqlens,
-                max_seqlen=max_seqlen,
-            )
-
-        hidden_states = layer_outputs[0]
-
-        if use_cache:
-            next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
-
-        if output_attentions:
-            all_self_attns += (layer_outputs[1],)
-
-    hidden_states = self.norm(hidden_states)
-
-    # Add hidden states from the last decoder layer
-    if output_hidden_states:
-        all_hidden_states += (hidden_states,)
-
-    next_cache = next_decoder_cache if use_cache else None
-    if not return_dict:
-        return tuple(
-            v
-            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
-            if v is not None
-        )
-    return BaseModelOutputWithPast(
-        last_hidden_state=hidden_states,
-        past_key_values=next_cache,
-        hidden_states=all_hidden_states,
-        attentions=all_self_attns,
-    )
--- a/src/axolotl/prompt_strategies/context_qa.py
+++ b/src/axolotl/prompt_strategies/context_qa.py
@@ -24,15 +24,6 @@ def load(tokenizer, cfg):
    )


-def load_v2(tokenizer, cfg):
-    return ContextQaV2PromptTokenizingStrategy(
-        ContextV2Prompter(),
-        tokenizer,
-        cfg.train_on_inputs,
-        cfg.sequence_len,
-    )
-
-
 class AlpacaContextPrompter(AlpacaPrompter):
    """
    Customized system prompted for concise QA
@@ -59,38 +50,6 @@ class AlpacaContextPromptTokenizingStrategy(InstructionPromptTokenizingStrategy)
        )


-class ContextQaV2PromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
-    """
-    Tokenization Strategy to combine in-context article with a question and answer
-    """
-
-    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
-        return (
-            "Context: "
-            + prompt["context"]
-            + "\nQuestion: "
-            + prompt["question"]
-            + "\n",
-            "",
-            "Answer: " + prompt["answer"],
-        )
-
-
-class ContextV2Prompter(AlpacaPrompter):
-    """
-    Customized system prompted for concise QA
-    """
-
-    system_prompt = ""
-    system_no_input_prompt = ""
-
-    def match_prompt_style(self):
-        # pylint: disable=duplicate-code
-        self.turn_format = "{instruction}\n{input}"
-        self.turn_no_input_format = "{instruction}"
-        self.system_format = "{system}"
-
-
 class AlpacaMissingInfoContextPromptTokenizingStrategy(
    InstructionPromptTokenizingStrategy
 ):
--- a/src/axolotl/prompt_strategies/sharegpt_jokes.py
+++ b/src/axolotl/prompt_strategies/sharegpt_jokes.py
@@ -1,11 +1,11 @@
 """Module for Jokes prompts using sharegpt style """
 from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
-from axolotl.prompters import ShareGPTPrompterV2
+from axolotl.prompters import PromptStyle, ShareGPTPrompter


 def load(tokenizer, cfg):
    return SimpleJokesShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompterV2(),
+        ShareGPTPrompter(PromptStyle.CHAT.value),
        tokenizer,
        cfg.train_on_inputs,
        cfg.sequence_len,
--- a/src/axolotl/prompt_strategies/sharegpt_simple.py
+++ b/src/axolotl/prompt_strategies/sharegpt_simple.py
@@ -1,35 +1,12 @@
 """Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
-from typing import Any, Dict, Optional
-
-from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template

 from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
-from axolotl.prompters import ShareGPTPrompterV2
-
-register_conv_template(
-    Conversation(
-        name="chatml",
-        system_template="<|im_start|>system\n{system_message}",
-        system_message="You are a helpful assistant.",
-        roles=["<|im_start|>user", "<|im_start|>assistant"],
-        sep_style=SeparatorStyle.CHATML,
-        sep="<|im_end|>\n",
-    )
-)
+from axolotl.prompters import PromptStyle, ShareGPTPrompter


-def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
-    conversation = (
-        ds_cfg["conversation"] if ds_cfg and "conversation" in ds_cfg else None
-    )
-    field_human = ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
-    field_model = ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
+def load(tokenizer, cfg):
    return SimpleShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompterV2(
-            conversation=conversation,
-            role_key_model=field_model,
-            role_key_human=field_human,
-        ),
+        ShareGPTPrompter(PromptStyle.CHAT.value),
        tokenizer,
        cfg.train_on_inputs,
        cfg.sequence_len,
@@ -38,7 +15,7 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):

 def load_role(tokenizer, cfg):
    return SimpleRoleShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompterV2(),
+        ShareGPTPrompter(PromptStyle.CHAT.value),
        tokenizer,
        cfg.train_on_inputs,
        cfg.sequence_len,
@@ -47,7 +24,7 @@ def load_role(tokenizer, cfg):

 def load_guanaco(tokenizer, cfg):
    return GuanacoShareGPTPromptTokenizingStrategy(
-        ShareGPTPrompterV2(),
+        ShareGPTPrompter(PromptStyle.CHAT.value),
        tokenizer,
        cfg.train_on_inputs,
        cfg.sequence_len,
--- a/src/axolotl/prompt_tokenizers.py
+++ b/src/axolotl/prompt_tokenizers.py
@@ -2,15 +2,12 @@

 import abc
 import copy
+import functools
 import logging
 from typing import Dict, List, Tuple, Union

-from fastchat.conversation import Conversation
 from transformers import BatchEncoding, PreTrainedTokenizer

-from axolotl.monkeypatch.fastchat_conversation_turns import (
-    add_get_turns_to_conversation,
-)
 from axolotl.prompters import IGNORE_TOKEN_ID

 LOG = logging.getLogger("axolotl")
@@ -21,8 +18,6 @@ LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
 LLAMA_DEFAULT_BOS_TOKEN = "<s>"  # nosec
 LLAMA_DEFAULT_UNK_TOKEN = "<unk>"  # nosec

-add_get_turns_to_conversation()
-

 class InvalidDataException(Exception):
    """
@@ -56,11 +51,31 @@ class PromptTokenizingStrategy(abc.ABC):
    def supports_batched(self):
        return False

+    @functools.lru_cache(maxsize=128)
+    def _get_user_token(self):
+        try:
+            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
+            if isinstance(id_or_ids, (int,)):
+                return id_or_ids
+        except KeyError:
+            pass
+        return False
+
+    @functools.lru_cache(maxsize=128)
+    def _get_assistant_token(self):
+        try:
+            id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
+            if isinstance(id_or_ids, (int,)):
+                return id_or_ids
+        except KeyError:
+            pass
+        return False
+
    def _tokenize(
        self, prompt: str, add_eos_token: bool = True, strip_bos_token: bool = False
    ) -> BatchEncoding:
        result: BatchEncoding
-        if not prompt:
+        if not prompt.strip():
            LOG.warning("Empty text requested for tokenization.")
            result = BatchEncoding(data={"input_ids": [], "attention_mask": []})
        else:
@@ -335,82 +350,55 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):

    def tokenize_prompt(self, prompt):
        result, current_len = tokenize_prompt_default()
-        conversation: Conversation = (
-            self.prompter._conversation.copy()  # pylint: disable=protected-access
-        )
-
-        # support for custom roles from the dataset, only useful for vicuna style prompts/roles
-        role_remap = []
-        if (
-            conversation.name == "vicuna_v1.1"
-            and "roles" in prompt
-            and len(prompt["roles"]) >= 2
-        ):
-            role_remap = [
-                {"from": conversation.roles[0], "to": prompt["roles"][0]},
-                {"from": conversation.roles[1], "to": prompt["roles"][1]},
-            ]
-
+        user_token = self._get_user_token()
+        assistant_token = self._get_assistant_token()
        try:
            for _, part in enumerate(
                self.prompter.build_prompt(self.get_conversation_thread(prompt))
            ):
                if isinstance(part, tuple):
-                    if conversation.roles[0] in part[0]:
-                        role = (
-                            part[0].replace(role_remap[0]["from"], role_remap[0]["to"])
-                            if role_remap
-                            else part[0]
-                        )
-                        turn = role + part[1]
+                    if part[0] == "USER:":
+                        turn = part[0] + part[1] if not user_token else part[1]
                        # this is still the user query, we should
                        if not part[1].strip():
                            LOG.warning(f"user turn has empty text: {prompt}")
                        res = self._tokenize(
-                            turn,
+                            turn.strip(),
                            add_eos_token=False,
                            strip_bos_token=True,
                        )
+                        if user_token:
+                            res["input_ids"] = [user_token, *res["input_ids"]]
                        # everything from this is masked out from the labels
                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
-                    elif conversation.roles[1] in part[0]:
+                    elif part[0] == "ASSISTANT:":
                        # TODO label assistant token/tokens w/ IGNORE_TOKEN_ID
-                        role = (
-                            part[0].replace(role_remap[1]["from"], role_remap[1]["to"])
-                            if role_remap
-                            else part[0]
-                        )
-                        turn = role + part[1]
+                        turn = part[0] + part[1] if not assistant_token else part[1]
                        # this should be the assistant response, should end with an eos token
                        if not part[1].strip():
                            LOG.warning(f"assistant turn has empty text: {prompt}")
                        res = self._tokenize(
-                            turn,
+                            turn.strip(),
                            add_eos_token=True,
                            strip_bos_token=True,
                        )
-                        role_res = self._tokenize(
-                            role.rstrip(),
-                            add_eos_token=False,
-                            strip_bos_token=True,
-                        )
+                        if assistant_token:
+                            res["input_ids"] = [
+                                assistant_token,
+                                *res["input_ids"],
+                            ]
                        # not masked out from labels
                        labels = copy.deepcopy(res["input_ids"])
-                        len_role = len(role_res["input_ids"])
-                        labels[:len_role] = [IGNORE_TOKEN_ID] * min(
-                            len_role, len(labels)
-                        )
-                    elif part[0] == "":
-                        turn = part[1]
+                    elif part[0] == "SYSTEM:":
+                        part = part[1]  # Ignore the system role from preamble
                        # this is only ever the first part, should include the bos token and the user query
                        res = self._tokenize(
-                            turn, add_eos_token=False, strip_bos_token=False
+                            part.strip(), add_eos_token=False, strip_bos_token=False
                        )
                        # everything from this is masked out from the labels
                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
                    else:
                        LOG.warning(f"unhandled role: {part[0]}")
-                        continue

                # pylint: disable=duplicate-code
                result, current_len = parse_tokenized_to_result(
--- a/src/axolotl/prompters.py
+++ b/src/axolotl/prompters.py
@@ -1,10 +1,9 @@
 """Module containing prompters"""

+import dataclasses
 import logging
-from enum import Enum
-from typing import Generator, Optional, Union
-
-from fastchat.conversation import Conversation, get_conv_template
+from enum import Enum, auto
+from typing import Generator, List, Optional, Tuple, Union

 LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100
@@ -215,6 +214,53 @@ class ReflectAlpacaPrompter:
        yield res


+class SeparatorStyle(Enum):
+    """Different separator style."""
+
+    SINGLE = auto()
+    TWO = auto()
+    DOLLY = auto()
+
+
+# TODO clean this 💩 up
+@dataclasses.dataclass
+class Conversation:
+    """A class that keeps all conversation history."""
+
+    system: str
+    roles: List[str]
+    messages: List[List[str]]
+    offset: int
+    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
+    sep: str = "###"
+    sep2: Optional[str] = None
+
+    def get_prompt(self) -> Generator[Tuple[str, str], None, None]:
+        # seps = [self.sep, self.sep2]
+        preamble = self.system + self.sep
+        yield ("SYSTEM:", preamble)
+        for _, (role, message) in enumerate(self.messages):
+            if message:
+                yield (role + ":", " " + message)
+            else:
+                LOG.warning(f"role with empty message: {role}")
+                yield (role + ":", "")
+
+    def copy(self):
+        return Conversation(
+            system=self.system,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+        )
+
+    def append_message(self, role, message):
+        self.messages.append([role, message])
+
+
 SHAREGPT_ASSERTION_FAILED_ROLE = (
    "Role did not alternate between turns (gpt and human). Please check your data."
 )
@@ -225,27 +271,28 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
    A prompter that generates prompts for the ShareGPT
    """

-    role_key_human = "human"
-    role_key_model = "gpt"
-
-    def __init__(
-        self,
-        prompt_style=None,  # pylint: disable=unused-argument
-        conversation: Optional[Union[str, Conversation]] = None,
-        role_key_human: Optional[str] = None,
-        role_key_model: Optional[str] = None,
-    ):
-        if conversation:
-            if isinstance(conversation, Conversation):
-                self._conversation = conversation
-            else:
-                self._conversation = get_conv_template(conversation)
-        else:
-            self._conversation = get_conv_template("vicuna_v1.1")
-        if role_key_human:
-            self.role_key_human = role_key_human
-        if role_key_model:
-            self.role_key_model = role_key_model
+    def __init__(self, prompt_style=None, system_prompt: Optional[str] = None):
+        if prompt_style != PromptStyle.CHAT.value:
+            raise ValueError(
+                f"unsupported prompt_style for ShareGPTPrompter({prompt_style})"
+            )
+        system: str = (
+            system_prompt
+            if system_prompt
+            else (
+                "A chat between a curious user and an artificial intelligence assistant. "
+                "The assistant gives helpful, detailed, and polite answers to the user's questions."
+            )
+        )
+        self._conversation = Conversation(
+            system=system,
+            roles=["USER", "ASSISTANT"],
+            messages=[],
+            offset=0,
+            sep_style=SeparatorStyle.TWO,
+            sep=" ",
+            sep2=" ",
+        )

    def build_prompt(self, source) -> Generator[str, None, None]:
        if len(source) < 2:
@@ -259,14 +306,17 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods

        # Add the conversation system prompt if provided, otherwise use the default one
        if source[0]["from"] == "system":
-            conv.set_system_message(source[0]["value"])
+            conv.system = source[0]["value"]
            source.pop(0)

-        roles = {self.role_key_human: conv.roles[0], self.role_key_model: conv.roles[1]}
+        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}

        try:
            # Apply prompt templates
-            if source[0]["from"] not in roles:
+            if (
+                source[0]["from"] not in roles
+                or roles[source[0]["from"]] != conv.roles[0]
+            ):
                # Skip the first one if it is not from human
                source = source[1:]
        except IndexError as err:
@@ -274,33 +324,10 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
            raise err

        conv.messages = []
-        for _, sentence in enumerate(source):
+        for j, sentence in enumerate(source):
            role = roles[sentence["from"]]
-            if len(conv.messages) > 0 and (
-                (role == conv.messages[-1][0]) or (role not in conv.roles)
-            ):
-                LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
            conv.append_message(role, sentence["value"])

-        for part in conv.get_turns():
-            if part[0] and not part[1]:
-                LOG.warning(f"role with empty message: {part[0]}")
+        for part in conv.get_prompt():
            yield part
-
-
-class ShareGPTPrompterV2(ShareGPTPrompter):
-    """
-    A V2 prompter that generates prompts for the ShareGPT
-    """
-
-    def __init__(
-        self,
-        conversation: Optional[Union[str, Conversation]] = None,
-        role_key_human: Optional[str] = None,
-        role_key_model: Optional[str] = None,
-    ):
-        super().__init__(
-            conversation=conversation,
-            role_key_human=role_key_human,
-            role_key_model=role_key_model,
-        )
--- a/src/axolotl/utils/callbacks.py
+++ b/src/axolotl/utils/callbacks.py
@@ -514,27 +514,3 @@ def log_prediction_callback_factory(trainer: Trainer, tokenizer):
            return control

    return LogPredictionCallback
-
-
-class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
-    """Callback to save axolotl config to wandb"""
-
-    def __init__(self, axolotl_config_path):
-        self.axolotl_config_path = axolotl_config_path
-
-    def on_train_begin(
-        self,
-        args: AxolotlTrainingArguments,  # pylint: disable=unused-argument
-        state: TrainerState,  # pylint: disable=unused-argument
-        control: TrainerControl,
-        **kwargs,  # pylint: disable=unused-argument
-    ):
-        if is_main_process():
-            try:
-                artifact = wandb.Artifact(name="axolotl-config", type="config")
-                artifact.add_file(local_path=self.axolotl_config_path)
-                wandb.run.log_artifact(artifact)
-                LOG.info("Axolotl config has been saved to WandB as an artifact.")
-            except (FileNotFoundError, ConnectionError) as err:
-                LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
-        return control
--- a/src/axolotl/utils/config.py
+++ b/src/axolotl/utils/config.py
@@ -49,8 +49,6 @@ def normalize_config(cfg):
    cfg.batch_size = (
        cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
    )
-    if cfg.eval_batch_size is None:
-        cfg.eval_batch_size = cfg.micro_batch_size
    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
    cfg.eval_table_size = cfg.eval_table_size or 0
@@ -77,8 +75,6 @@ def normalize_config(cfg):
    else:
        cfg.torch_dtype = torch.float32

-    cfg.dataset_processes = cfg.dataset_processes or os.cpu_count()
-
    model_config = load_model_config(cfg)
    cfg.model_config_type = model_config.model_type

@@ -86,39 +82,10 @@ def normalize_config(cfg):
    cfg.is_llama_derived_model = (
        (hasattr(model_config, "model_type") and model_config.model_type == "llama")
        or cfg.is_llama_derived_model
-        or "llama" in cfg.base_model.lower()
+        or "llama" in cfg.base_model
        or (cfg.model_type and "llama" in cfg.model_type.lower())
    )

-    # figure out if the model is falcon
-    cfg.is_falcon_derived_model = (
-        (
-            hasattr(model_config, "model_type")
-            and model_config.model_type
-            in [
-                "falcon",
-                "RefinedWebModel",
-                "RefinedWeb",
-            ]
-        )
-        or cfg.is_falcon_derived_model
-        or "falcon" in cfg.base_model.lower()
-        or (cfg.model_type and "rwforcausallm" in cfg.model_type.lower())
-    )
-
-    cfg.is_mistral_derived_model = (
-        (
-            hasattr(model_config, "model_type")
-            and model_config.model_type
-            in [
-                "mistral",
-            ]
-        )
-        or cfg.is_mistral_derived_model
-        or "mistral" in cfg.base_model.lower()
-        or (cfg.model_type and "mistral" in cfg.model_type.lower())
-    )
-
    log_gpu_memory_usage(LOG, "baseline", cfg.device)


@@ -159,11 +126,6 @@ def validate_config(cfg):
            "batch_size is not recommended. Please use gradient_accumulation_steps instead.",
            "To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.",
        )
-    if cfg.eval_batch_size != cfg.micro_batch_size:
-        LOG.warning(
-            "eval_batch_size != micro_batch_size. This can lead to VRAM instability."
-        )
-
    if cfg.load_4bit:
        raise ValueError("cfg.load_4bit parameter has been deprecated")

@@ -300,45 +262,6 @@ def validate_config(cfg):
                    "`model_type: MixFormerSequentialForCausalLM` required for sample_packing"
                )

-    if cfg.datasets:
-        for idx, ds_cfg in enumerate(cfg.datasets):
-            if not ds_cfg.type:
-                continue
-            if ds_cfg.type == "sharegpt:chat":
-                LOG.warning(
-                    PendingDeprecationWarning(
-                        "`type: sharegpt:chat` will soon be deprecated. simply use `type: sharegpt` instead."
-                    )
-                )
-                cfg.datasets[idx].type = "sharegpt"
-            if "sharegpt_simple" in ds_cfg.type:
-                LOG.warning(
-                    PendingDeprecationWarning(
-                        "`type: sharegpt_simple` will soon be deprecated. simply use `type: sharegpt` instead."
-                    )
-                )
-                cfg.datasets[idx].type = cfg.datasets[idx].type.replace(
-                    "sharegpt_simple", "sharegpt"
-                )
-    if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
-        raise ValueError(
-            "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
-        )
-
-    if (
-        cfg.evaluation_strategy
-        and cfg.eval_steps
-        and cfg.evaluation_strategy != "steps"
-    ):
-        raise ValueError(
-            "evaluation_strategy and eval_steps mismatch. Please set evaluation_strategy to 'steps' or remove eval_steps."
-        )
-
-    if cfg.val_set_size == 0 and (cfg.eval_steps or cfg.evaluation_strategy):
-        raise ValueError(
-            "eval_steps and evaluation_strategy are not supported with val_set_size == 0"
-        )
-
    # TODO
    # MPT 7b
    # https://github.com/facebookresearch/bitsandbytes/issues/25
--- a/src/axolotl/utils/data.py
+++ b/src/axolotl/utils/data.py
@@ -16,7 +16,6 @@ from datasets import (
 from huggingface_hub import hf_hub_download
 from transformers import PreTrainedTokenizerBase

-from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.datasets import ConstantLengthDataset, TokenizedPromptDataset
 from axolotl.prompt_strategies import load
 from axolotl.prompt_tokenizers import (
@@ -26,6 +25,7 @@ from axolotl.prompt_tokenizers import (
    GPTeacherPromptTokenizingStrategy,
    JeopardyPromptTokenizingStrategy,
    OpenAssistantPromptTokenizingStrategy,
+    ShareGPTPromptTokenizingStrategy,
    SummarizeTLDRPromptTokenizingStrategy,
 )
 from axolotl.prompters import (
@@ -35,6 +35,7 @@ from axolotl.prompters import (
    MultipleChoiceConcisePrompter,
    MultipleChoiceExplainPrompter,
    ReflectAlpacaPrompter,
+    ShareGPTPrompter,
    SummarizeTLDRPrompter,
 )
 from axolotl.utils.dict import DictDefault
@@ -45,6 +46,7 @@ from axolotl.utils.trainer import (
 )

 LOG = logging.getLogger("axolotl")
+DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"


 def md5(to_hash: str, encoding: str = "utf-8") -> str:
@@ -74,7 +76,7 @@ def prepare_dataset(cfg, tokenizer):

    with zero_first(is_main_process()):
        train_dataset, eval_dataset = process_datasets_for_packing(
-            cfg, train_dataset, eval_dataset, tokenizer
+            cfg, train_dataset, eval_dataset
        )
    if cfg.max_steps:
        total_num_steps = min(
@@ -114,7 +116,7 @@ def load_tokenized_prepared_datasets(
        if cfg.push_dataset_to_hub:
            dataset = load_dataset(
                f"{cfg.push_dataset_to_hub}/{ds_hash}",
-                token=use_auth_token,
+                use_auth_token=use_auth_token,
            )
            dataset = dataset["train"]
    except Exception:  # pylint: disable=broad-except # nosec
@@ -122,7 +124,7 @@ def load_tokenized_prepared_datasets(

    if dataset:
        ...
-    elif cfg.dataset_prepared_path and any(prepared_ds_path.glob("*")):
+    elif any(prepared_ds_path.glob("*")):
        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
        dataset = load_from_disk(str(prepared_ds_path))
        LOG.info("Prepared dataset loaded from disk...")
@@ -155,7 +157,7 @@ def load_tokenized_prepared_datasets(
                    d.path,
                    name=d.name,
                    streaming=True,
-                    token=use_auth_token,
+                    use_auth_token=use_auth_token,
                )
                ds_from_hub = True
            except FileNotFoundError:
@@ -202,29 +204,14 @@ def load_tokenized_prepared_datasets(
                    name=d.name,
                    streaming=False,
                    data_files=d.data_files,
-                    token=use_auth_token,
+                    use_auth_token=use_auth_token,
                )
            else:
-                if isinstance(d.data_files, str):
-                    fp = hf_hub_download(
-                        repo_id=d.path,
-                        repo_type="dataset",
-                        filename=d.data_files,
-                    )
-                elif isinstance(d.data_files, list):
-                    fp = []
-                    for file in d.data_files:
-                        fp.append(
-                            hf_hub_download(
-                                repo_id=d.path,
-                                repo_type="dataset",
-                                filename=file,
-                            )
-                        )
-                else:
-                    raise ValueError(
-                        "data_files must be either a string or list of strings"
-                    )
+                fp = hf_hub_download(
+                    repo_id=d.path,
+                    repo_type="dataset",
+                    filename=d.data_files,
+                )
                ds = load_dataset(
                    "json", name=d.name, data_files=fp, streaming=False, split=None
                )
@@ -247,16 +234,6 @@ def load_tokenized_prepared_datasets(
                d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
            if "train" in ds:
                ds = ds["train"]
-            elif (
-                isinstance(ds, DatasetDict)
-                and d.train_on_split
-                and d.train_on_split in ds
-            ):
-                ds = ds[d.train_on_split]
-            elif isinstance(ds, DatasetDict):
-                raise ValueError(
-                    f"no train split found for dataset {d.path}, you may specify a split with 'train_on_split: `"
-                )
            if (
                "input_ids" in ds.features
                and "attention_mask" in ds.features
@@ -343,6 +320,15 @@ def load_tokenized_prepared_datasets(
                )
                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
                datasets.append(ds_wrapper)
+            elif d_base_type == "sharegpt":
+                ds_strategy = ShareGPTPromptTokenizingStrategy(
+                    ShareGPTPrompter(d_prompt_style),
+                    tokenizer,
+                    cfg.train_on_inputs,
+                    cfg.sequence_len,
+                )
+                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
+                datasets.append(ds_wrapper)
            else:
                suffix = ""
                if ":load_" in d.type:
@@ -417,7 +403,7 @@ def load_prepare_datasets(
                )
                dataset = load_dataset(
                    f"{cfg.push_dataset_to_hub}/{ds_hash}",
-                    token=use_auth_token,
+                    use_auth_token=use_auth_token,
                )
                dataset = dataset["train"]
        except Exception:  # pylint: disable=broad-except # nosec
@@ -425,7 +411,7 @@ def load_prepare_datasets(

        if dataset:
            ...
-        elif cfg.dataset_prepared_path and any(prepared_ds_path.glob("*")):
+        elif any(prepared_ds_path.glob("*")):
            LOG.info(
                f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
            )
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -1,4 +1,5 @@
 """Module for models and model loading"""
+import importlib
 import logging
 import math
 import os
@@ -11,7 +12,6 @@ from optimum.bettertransformer import BetterTransformer
 from peft import PeftConfig, prepare_model_for_kbit_training
 from peft.tuners.lora import QuantLinear
 from transformers import (  # noqa: F401
-    AddedToken,
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
@@ -81,22 +81,11 @@ def load_tokenizer(cfg):
        tokenizer.add_special_tokens({"pad_token": "[PAD]"})
        os.environ["TOKENIZERS_PARALLELISM"] = "false"

-    # Mistral's official FA implementation requires left padding
-    if cfg.is_mistral_derived_model and cfg.flash_attention and not cfg.sample_packing:
-        tokenizer.padding_side = "left"
-
    if cfg.special_tokens:
        for k, val in cfg.special_tokens.items():
-            tokenizer.add_special_tokens(
-                {k: AddedToken(val, rstrip=False, lstrip=False, normalized=False)}
-            )
+            tokenizer.add_special_tokens({k: val})
    if cfg.tokens:
-        tokenizer.add_tokens(
-            [
-                AddedToken(token, rstrip=False, lstrip=False, normalized=False)
-                for token in cfg.tokens
-            ]
-        )
+        tokenizer.add_tokens(list(cfg.tokens))

    return tokenizer

@@ -125,34 +114,26 @@ def load_model(

            replace_btlm_attn_with_flash_attn(cfg.base_model)

-    if (
-        hasattr(model_config, "model_type")
-        and model_config.model_type == "stablelm_epoch"
-    ):
-        if cfg.flash_attention and cfg.sample_packing:
-            from axolotl.monkeypatch.stablelm_attn_hijack_flash import (
-                replace_stablelm_attn_with_flash_attn,
+    if hasattr(model_config, "model_type") and model_config.model_type in [
+        "falcon",
+        "RefinedWebModel",
+        "RefinedWeb",
+    ]:
+        if cfg.flash_attention:
+            from axolotl.monkeypatch.falcon_attn_hijack_flash import (
+                replace_falcon_attn_with_flash_attn,
            )

-            replace_stablelm_attn_with_flash_attn(cfg.base_model)
+            replace_falcon_attn_with_flash_attn()

-    if (
-        cfg.is_llama_derived_model
-        and cfg.flash_attention
-        and (cfg.noisy_embeddings_alpha or cfg.sample_packing)
-    ):
+    if cfg.is_llama_derived_model and cfg.flash_attention:
        if cfg.device not in ["mps", "cpu"] and not inference:
            from axolotl.monkeypatch.llama_attn_hijack_flash import (
                replace_llama_attn_with_flash_attn,
            )

-            LOG.info("patching with flash attention for sample packing")
-            replace_llama_attn_with_flash_attn(
-                packed=cfg.sample_packing,
-                cross_entropy=cfg.flash_attn_cross_entropy,
-                rms_norm=cfg.flash_attn_rms_norm,
-                noisy_embeddings_alpha=cfg.noisy_embeddings_alpha,
-            )
+            LOG.info("patching with flash attention")
+            replace_llama_attn_with_flash_attn(packed=cfg.sample_packing)
    elif cfg.is_llama_derived_model and cfg.xformers_attention:
        from axolotl.monkeypatch.llama_attn_hijack_xformers import (
            hijack_llama_attention,
@@ -177,34 +158,6 @@ def load_model(
        # Note: This might overwrite previous additional_special_tokens
        tokenizer.add_special_tokens({"additional_special_tokens": [MEM_TOKEN]})

-    if cfg.is_mistral_derived_model and cfg.flash_attention and cfg.sample_packing:
-        from axolotl.monkeypatch.mistral_attn_hijack_flash import (
-            replace_mistral_attn_with_flash_attn,
-        )
-
-        LOG.info("patching with flash attention")
-        replace_mistral_attn_with_flash_attn(packed=cfg.sample_packing)
-
-    # if cfg.is_llama_derived_model and cfg.noisy_embedding_alpha:
-    #     from axolotl.monkeypatch.llama_embeddings_hijack import (
-    #         replace_llama_embeddings_with_uniform_distribution,
-    #     )
-    #
-    #     LOG.info("patching with noisy embeddings")
-    #     replace_llama_embeddings_with_uniform_distribution(
-    #         noise_alpha=cfg.noisy_embedding_alpha
-    #     )
-    #
-    if cfg.is_mistral_derived_model and cfg.noisy_embedding_alpha:
-        from axolotl.monkeypatch.mistral_embeddings_hijack import (
-            replace_mistral_embeddings_with_uniform_distribution,
-        )
-
-        LOG.info("patching with noisy embeddings")
-        replace_mistral_embeddings_with_uniform_distribution(
-            noise_alpha=cfg.noisy_embedding_alpha
-        )
-
    if cfg.is_llama_derived_model and cfg.xpos_rope:
        from axolotl.monkeypatch.xpos_rope_llama_monkey_patch import (
            replace_llama_rope_with_xpos_rope,
@@ -223,11 +176,21 @@ def load_model(
        LOG.info("patching _expand_mask")
        hijack_expand_mask()

+    # special handling b/c remote MixFormers code doesn't have _no_split_modules set
+    if (
+        "MixFormerSequentialConfig" in model_config.__class__.__name__
+        and cfg.model_type == "AutoModelForCausalLM"
+    ):
+        module_name = model_config.__class__.__module__.replace(
+            ".configuration_mixformer_sequential", ".modeling_mixformer_sequential"
+        )
+        modeling_phi = importlib.import_module(module_name)
+        # pylint:disable=protected-access
+        modeling_phi.MixFormerSequentialForCausalLM._no_split_modules = [
+            "ParallelBlock"
+        ]
+
    model_kwargs = {}
-
-    model_kwargs["device_map"] = cfg.device_map
-    model_kwargs["torch_dtype"] = cfg.torch_dtype
-
    if cfg.model_revision:
        model_kwargs["revision"] = cfg.model_revision
    if cfg.gptq:
@@ -250,15 +213,6 @@ def load_model(
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type="nf4",
        )
-    # sample packing uses custom FA2 patch
-    if cfg.flash_attention and not cfg.sample_packing:
-        if (
-            cfg.is_llama_derived_model
-            or cfg.is_falcon_derived_model
-            or cfg.is_mistral_derived_model
-        ):
-            model_kwargs["use_flash_attention_2"] = True
-
    try:
        if cfg.is_llama_derived_model and not cfg.trust_remote_code and not cfg.gptq:
            from transformers import LlamaForCausalLM
@@ -273,8 +227,10 @@ def load_model(
            model = LlamaForCausalLM.from_pretrained(
                base_model,
                config=config,
+                device_map=cfg.device_map,
                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+                torch_dtype=cfg.torch_dtype,
                **model_kwargs,
            )
        # elif model_type == "GPTNeoXForCausalLM" and cfg.flash_attention:
@@ -308,22 +264,28 @@ def load_model(

            model = MixFormerSequentialForCausalLM.from_pretrained(
                base_model,
+                device_map=cfg.device_map,
                load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+                torch_dtype=cfg.torch_dtype,
                **model_kwargs,
            )
        elif model_type and not cfg.trust_remote_code:
            if cfg.gptq:
                model = AutoModelForCausalLM.from_pretrained(
                    base_model,
+                    device_map=cfg.device_map,
+                    torch_dtype=cfg.torch_dtype,
                    trust_remote_code=cfg.trust_remote_code or False,
                    **model_kwargs,
                )
            else:
                model = getattr(transformers, model_type).from_pretrained(
                    base_model,
+                    device_map=cfg.device_map,
                    load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                    load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+                    torch_dtype=cfg.torch_dtype,
                    trust_remote_code=cfg.trust_remote_code or False,
                    **model_kwargs,
                )
@@ -352,6 +314,8 @@ def load_model(
                model = AutoModelForCausalLM.from_pretrained(
                    base_model,
                    config=config,
+                    device_map=cfg.device_map,
+                    torch_dtype=cfg.torch_dtype,
                    trust_remote_code=cfg.trust_remote_code or False,
                    **model_kwargs,
                )
@@ -359,8 +323,10 @@ def load_model(
                model = AutoModelForCausalLM.from_pretrained(
                    base_model,
                    config=config,
+                    device_map=cfg.device_map,
                    load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
                    load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+                    torch_dtype=cfg.torch_dtype,
                    trust_remote_code=cfg.trust_remote_code or False,
                    **model_kwargs,
                )
@@ -371,8 +337,10 @@ def load_model(
        LOG.exception(err)
        model = AutoModelForCausalLM.from_pretrained(
            base_model,
+            device_map=cfg.device_map,
            load_in_8bit=cfg.load_in_8bit and cfg.adapter is not None,
            load_in_4bit=cfg.load_in_4bit and cfg.adapter is not None,
+            torch_dtype=cfg.torch_dtype,
            trust_remote_code=cfg.trust_remote_code or False,
            **model_kwargs,
        )
--- a/src/axolotl/utils/tokenization.py
+++ b/src/axolotl/utils/tokenization.py
@@ -31,9 +31,7 @@ def check_example_labels(example, tokenizer, text_only=False):
        )
        colored_tokens.append(colored_token)

-    delimiter = "" if text_only else " "
-    LOG.info(delimiter.join(colored_tokens))
+    LOG.info(" ".join(colored_tokens))
    LOG.info("\n\n\n")
-    print(" ".join(colored_tokens))

    return " ".join(colored_tokens)
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -30,7 +30,6 @@ from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
 from axolotl.utils.callbacks import (
    EvalFirstStepCallback,
    GPUStatsCallback,
-    SaveAxolotlConfigtoWandBCallback,
    SaveBetterTransformerModelCallback,
    bench_eval_callback_factory,
    log_prediction_callback_factory,
@@ -398,36 +397,23 @@ def disable_datasets_caching():
        set_caching_enabled(True)


-def process_datasets_for_packing(cfg, train_dataset, eval_dataset, tokenizer):
+def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
    drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
    with zero_first(is_main_process()):
-        train_dataset = train_dataset.filter(drop_long, num_proc=cfg.dataset_processes)
+        train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count())
        if eval_dataset:
-            eval_dataset = eval_dataset.filter(
-                drop_long, num_proc=cfg.dataset_processes
-            )
+            eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count())

        if cfg.group_by_length:
-            train_dataset = train_dataset.map(
-                add_length, num_proc=cfg.dataset_processes
-            )
+            train_dataset = train_dataset.map(add_length, num_proc=os.cpu_count())

        if cfg.sample_packing:
-            train_dataset = train_dataset.map(
-                add_position_ids, num_proc=cfg.dataset_processes
-            )
+            train_dataset = train_dataset.map(add_position_ids, num_proc=os.cpu_count())
            if cfg.eval_sample_packing is not False:
                if eval_dataset:
                    eval_dataset = eval_dataset.map(
-                        add_position_ids, num_proc=cfg.dataset_processes
+                        add_position_ids, num_proc=os.cpu_count()
                    )
-
-        # Phi doesn't want the attention_mask feature when training
-        if "CodeGenTokenizer" in tokenizer.__class__.__name__:
-            train_dataset = train_dataset.remove_columns("attention_mask")
-            if eval_dataset:
-                eval_dataset = eval_dataset.remove_columns("attention_mask")
-
    return train_dataset, eval_dataset


@@ -611,19 +597,26 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
            "sample_packing_efficiency"
        ] = cfg.sample_packing_eff_est

-    if cfg.eval_steps:
-        training_arguments_kwargs["evaluation_strategy"] = "steps"
-        training_arguments_kwargs["eval_steps"] = cfg.eval_steps
-    elif cfg.evaluation_strategy:
+    if cfg.eval_steps and cfg.evaluation_strategy:
+        # assume if the user set both, they know what they're doing
        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
+        training_arguments_kwargs["eval_steps"] = cfg.eval_steps
    elif cfg.val_set_size == 0:
        # no eval set, so don't eval
        training_arguments_kwargs["evaluation_strategy"] = "no"
+    elif cfg.evaluation_strategy and cfg.evaluation_strategy in ["epoch", "no"]:
+        # if explicitly set for epoch, just set, and eval steps don't matter
+        training_arguments_kwargs["evaluation_strategy"] = cfg.evaluation_strategy
+    elif cfg.eval_steps:
+        # steps isn't used w/ epochs
+        training_arguments_kwargs["evaluation_strategy"] = "steps"
+        training_arguments_kwargs["eval_steps"] = cfg.eval_steps
    else:
        # we have an eval set, but no steps defined, default to use epoch
        training_arguments_kwargs["evaluation_strategy"] = "epoch"

    if cfg.save_steps:
+        # save_steps implies save_strategy of steps
        training_arguments_kwargs["save_strategy"] = "steps"
        training_arguments_kwargs["save_steps"] = cfg.save_steps
    elif cfg.save_strategy:
@@ -669,7 +662,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
        max_steps=total_num_steps if cfg.max_steps else -1,
        max_seq_length=cfg.sequence_len,
        per_device_train_batch_size=cfg.micro_batch_size,
-        per_device_eval_batch_size=cfg.eval_batch_size,
+        per_device_eval_batch_size=cfg.eval_batch_size
+        if cfg.eval_batch_size is not None
+        else cfg.micro_batch_size,
        gradient_accumulation_steps=cfg.gradient_accumulation_steps,
        eval_accumulation_steps=cfg.gradient_accumulation_steps,
        num_train_epochs=cfg.num_epochs,
@@ -776,9 +771,6 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_
        LogPredictionCallback = log_prediction_callback_factory(trainer, tokenizer)
        trainer.add_callback(LogPredictionCallback(cfg))

-    if cfg.use_wandb:
-        trainer.add_callback(SaveAxolotlConfigtoWandBCallback(cfg.axolotl_config_path))
-
    if cfg.do_bench_eval:
        trainer.add_callback(bench_eval_callback_factory(trainer, tokenizer))

--- a/tests/e2e/test_mistral.py
+++ b/tests/e2e/test_mistral.py
@@ -1,116 +0,0 @@
-"""
-E2E tests for lora llama
-"""
-
-import logging
-import os
-import tempfile
-import unittest
-from pathlib import Path
-
-from transformers.utils import is_torch_bf16_gpu_available
-
-from axolotl.cli import load_datasets
-from axolotl.common.cli import TrainerCliArgs
-from axolotl.train import train
-from axolotl.utils.config import normalize_config
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestMistral(unittest.TestCase):
-    """
-    Test case for Llama models using LoRA
-    """
-
-    def test_lora(self):
-        # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
-        cfg = DictDefault(
-            {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
-                "base_model_config": "openaccess-ai-collective/tiny-mistral",
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 32,
-                "lora_alpha": 64,
-                "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": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "adapter_model.bin").exists()
-
-    def test_ft(self):
-        # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
-        cfg = DictDefault(
-            {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
-                "base_model_config": "openaccess-ai-collective/tiny-mistral",
-                "flash_attention": True,
-                "sequence_len": 1024,
-                "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": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
-            }
-        )
-        if is_torch_bf16_gpu_available():
-            cfg.bf16 = True
-        else:
-            cfg.fp16 = True
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "pytorch_model.bin").exists()
--- a/tests/e2e/test_mistral_samplepack.py
+++ b/tests/e2e/test_mistral_samplepack.py
@@ -1,118 +0,0 @@
-"""
-E2E tests for lora llama
-"""
-
-import logging
-import os
-import tempfile
-import unittest
-from pathlib import Path
-
-from transformers.utils import is_torch_bf16_gpu_available
-
-from axolotl.cli import load_datasets
-from axolotl.common.cli import TrainerCliArgs
-from axolotl.train import train
-from axolotl.utils.config import normalize_config
-from axolotl.utils.dict import DictDefault
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestMistral(unittest.TestCase):
-    """
-    Test case for Llama models using LoRA
-    """
-
-    def test_lora_packing(self):
-        # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
-        cfg = DictDefault(
-            {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
-                "base_model_config": "openaccess-ai-collective/tiny-mistral",
-                "flash_attention": True,
-                "sample_packing": True,
-                "sequence_len": 1024,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 32,
-                "lora_alpha": 64,
-                "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": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "adapter_model.bin").exists()
-
-    def test_ft_packing(self):
-        # pylint: disable=duplicate-code
-        output_dir = tempfile.mkdtemp()
-        cfg = DictDefault(
-            {
-                "base_model": "openaccess-ai-collective/tiny-mistral",
-                "base_model_config": "openaccess-ai-collective/tiny-mistral",
-                "flash_attention": True,
-                "sample_packing": True,
-                "sequence_len": 1024,
-                "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": 2,
-                "micro_batch_size": 2,
-                "gradient_accumulation_steps": 1,
-                "output_dir": output_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "eval_steps": 10,
-            }
-        )
-        if is_torch_bf16_gpu_available():
-            cfg.bf16 = True
-        else:
-            cfg.fp16 = True
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(output_dir) / "pytorch_model.bin").exists()
--- a/tests/fixtures/conversation.tokenized.json
+++ b/tests/fixtures/conversation.tokenized.json
--- a/tests/test_prompt_tokenizers.py
+++ b/tests/test_prompt_tokenizers.py
@@ -21,7 +21,7 @@ from axolotl.prompt_tokenizers import (
    AlpacaPromptTokenizingStrategy,
    ShareGPTPromptTokenizingStrategy,
 )
-from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompterV2
+from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompter

 LOG = logging.getLogger("axolotl")

@@ -60,7 +60,7 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
        ) as fin:
            data = fin.read()
            tokenized_conversation = json.loads(data)
-        prompter = ShareGPTPrompterV2()
+        prompter = ShareGPTPrompter("chat")
        strat = ShareGPTPromptTokenizingStrategy(
            prompter,
            self.tokenizer,
@@ -79,7 +79,7 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
        ) as fin:
            data = fin.read()
            conversation = json.loads(data)
-        prompter = ShareGPTPrompterV2()
+        prompter = ShareGPTPrompter("chat")
        strat = ShareGPTPromptTokenizingStrategy(
            prompter,
            self.tokenizer,
@@ -90,73 +90,6 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
            strat.tokenize_prompt(conversation)
            assert "assistant turn has empty text" in self._caplog.records[1].message

-    def test_sharegpt_warnings_turns(self):
-        conversation = {
-            "conversations": [
-                {"from": "system", "value": "lorem"},
-                {"from": "gpt", "value": "ipsum"},
-                {"from": "human", "value": "dolor"},
-                {"from": "human", "value": "dolor"},
-                {"from": "gpt", "value": "sit"},
-            ]
-        }
-        prompter = ShareGPTPrompterV2()
-        strat = ShareGPTPromptTokenizingStrategy(
-            prompter,
-            self.tokenizer,
-            False,
-            2048,
-        )
-        with self._caplog.at_level(logging.WARNING):
-            strat.tokenize_prompt(conversation)
-            assert (
-                "Role did not alternate between turns (gpt and human)"
-                in self._caplog.records[0].message
-            )
-
-    def test_sharegpt_changes_roles(self):
-        conversation = {
-            "roles": ["USER", "CHARACTER"],
-            "conversations": [
-                {"from": "system", "value": "lorem"},
-                {"from": "gpt", "value": "ipsum"},
-                {"from": "human", "value": "dolor"},
-                {"from": "gpt", "value": "sit"},
-            ],
-        }
-        prompter = ShareGPTPrompterV2()
-        strat = ShareGPTPromptTokenizingStrategy(
-            prompter,
-            self.tokenizer,
-            False,
-            2048,
-        )
-        with self._caplog.at_level(logging.WARNING):
-            res = strat.tokenize_prompt(conversation)
-            assert "CHARACTER" in self.tokenizer.decode(res["input_ids"])
-
-    def test_sharegpt_assistant_label_ignore(self):
-        conversation = {
-            "roles": ["user", "assistant"],
-            "conversations": [
-                {"from": "system", "value": "lorem"},
-                {"from": "gpt", "value": "ipsum"},
-                {"from": "human", "value": "dolor"},
-                {"from": "gpt", "value": "sit"},
-            ],
-        }
-        prompter = ShareGPTPrompterV2()
-        strat = ShareGPTPromptTokenizingStrategy(
-            prompter,
-            self.tokenizer,
-            False,
-            2048,
-        )
-        with self._caplog.at_level(logging.WARNING):
-            res = strat.tokenize_prompt(conversation)
-            idx = res["input_ids"].index(20255)  # assistant token
-            assert res["labels"][idx] == -100
-
    def test_no_sys_prompt(self):
        """
        tests the interface between the user and assistant parts
--- a/tests/test_validation.py
+++ b/tests/test_validation.py
@@ -374,194 +374,3 @@ class ValidationTest(unittest.TestCase):
        )

        validate_config(cfg)
-
-    def test_sharegpt_deprecation(self):
-        cfg = DictDefault(
-            {"datasets": [{"path": "lorem/ipsum", "type": "sharegpt:chat"}]}
-        )
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert any(
-                "`type: sharegpt:chat` will soon be deprecated." in record.message
-                for record in self._caplog.records
-            )
-        assert cfg.datasets[0].type == "sharegpt"
-
-        cfg = DictDefault(
-            {"datasets": [{"path": "lorem/ipsum", "type": "sharegpt_simple:load_role"}]}
-        )
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert any(
-                "`type: sharegpt_simple` will soon be deprecated." in record.message
-                for record in self._caplog.records
-            )
-        assert cfg.datasets[0].type == "sharegpt:load_role"
-
-    def test_no_conflict_save_strategy(self):
-        cfg = DictDefault(
-            {
-                "save_strategy": "epoch",
-                "save_steps": 10,
-            }
-        )
-
-        with pytest.raises(
-            ValueError, match=r".*save_strategy and save_steps mismatch.*"
-        ):
-            validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "save_strategy": "no",
-                "save_steps": 10,
-            }
-        )
-
-        with pytest.raises(
-            ValueError, match=r".*save_strategy and save_steps mismatch.*"
-        ):
-            validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "save_strategy": "steps",
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "save_strategy": "steps",
-                "save_steps": 10,
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "save_steps": 10,
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "save_strategy": "no",
-            }
-        )
-
-        validate_config(cfg)
-
-    def test_no_conflict_eval_strategy(self):
-        cfg = DictDefault(
-            {
-                "evaluation_strategy": "epoch",
-                "eval_steps": 10,
-            }
-        )
-
-        with pytest.raises(
-            ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
-        ):
-            validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "evaluation_strategy": "no",
-                "eval_steps": 10,
-            }
-        )
-
-        with pytest.raises(
-            ValueError, match=r".*evaluation_strategy and eval_steps mismatch.*"
-        ):
-            validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "evaluation_strategy": "steps",
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "evaluation_strategy": "steps",
-                "eval_steps": 10,
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "eval_steps": 10,
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "evaluation_strategy": "no",
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "evaluation_strategy": "epoch",
-                "val_set_size": 0,
-            }
-        )
-
-        with pytest.raises(
-            ValueError,
-            match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
-        ):
-            validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "eval_steps": 10,
-                "val_set_size": 0,
-            }
-        )
-
-        with pytest.raises(
-            ValueError,
-            match=r".*eval_steps and evaluation_strategy are not supported with val_set_size == 0.*",
-        ):
-            validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "val_set_size": 0,
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "eval_steps": 10,
-                "val_set_size": 0.01,
-            }
-        )
-
-        validate_config(cfg)
-
-        cfg = DictDefault(
-            {
-                "evaluation_strategy": "epoch",
-                "val_set_size": 0.01,
-            }
-        )
-
-        validate_config(cfg)