Fix logic errors

Remove redundant assert
Fix saving logic
2023-08-12 20:31:59 -04:00 · 2023-08-12 20:31:59 -04:00 · 2023-08-12 20:31:59 -04:00 · 2023-08-12 20:31:59 -04:00 · 2023-08-12 20:31:59 -04:00 · 2023-08-12 20:31:57 -04:00
71 changed files with 3898 additions and 403 deletions
--- a/.github/workflows/base.yml
+++ b/.github/workflows/base.yml
@@ -12,28 +12,19 @@ jobs:
    # this job needs to be run on self-hosted GPU runners...
    runs-on: self-hosted
    strategy:
+      fail-fast: false
      matrix:
        include:
          - cuda: "118"
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.0
-            axolotl_extras:
+            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: 2.0.0
-            axolotl_extras:
-          - cuda: "117"
-            cuda_version: 11.7.0
-            python_version: "3.9"
-            pytorch: 1.13.1
-            axolotl_extras:
-          - cuda: "118"
-            cuda_version: 11.8.0
-            python_version: "3.9"
-            pytorch: 2.0.0
-            axolotl_extras: gptq
+            pytorch: 2.0.1
+            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 9.0+PTX"
    steps:
      - name: Checkout
        uses: actions/checkout@v3
@@ -57,11 +48,9 @@ jobs:
          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 }}
          labels: ${{ steps.metadata.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
          build-args: |
            CUDA_VERSION=${{ matrix.cuda_version }}
            CUDA=${{ matrix.cuda }}
            PYTHON_VERSION=${{ matrix.python_version }}
            PYTORCH_VERSION=${{ matrix.pytorch }}
-            AXOLOTL_EXTRAS=${{ matrix.axolotl_extras }}
+            TORCH_CUDA_ARCH_LIST=${{ matrix.torch_cuda_arch_list }}
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -11,28 +11,24 @@ jobs:
    if: github.repository_owner == 'OpenAccess-AI-Collective'
    # this job needs to be run on self-hosted GPU runners...
    strategy:
+      fail-fast: false
      matrix:
        include:
          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.0
+            pytorch: 2.0.1
            axolotl_extras:
          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.0.0
+            pytorch: 2.0.1
            axolotl_extras:
          - cuda: cu118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.0
+            pytorch: 2.0.1
            axolotl_extras: gptq
-          - cuda: cu117
-            cuda_version: 11.7.0
-            python_version: "3.9"
-            pytorch: 1.13.1
-            axolotl_extras:
    runs-on: self-hosted
    steps:
      - name: Checkout
@@ -54,13 +50,11 @@ jobs:
        with:
          context: .
          build-args: |
-            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_name }}-base-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}
          file: ./docker/Dockerfile
          push: ${{ github.event_name != 'pull_request' }}
          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          labels: ${{ steps.metadata.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
  build-axolotl-runpod:
    needs: build-axolotl
    if: github.repository_owner == 'OpenAccess-AI-Collective'
@@ -68,26 +62,21 @@ jobs:
    strategy:
      matrix:
        include:
-          - cuda: cu118
+          - cuda: 118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.0
+            pytorch: 2.0.1
            axolotl_extras:
-          - cuda: cu118
+          - cuda: 118
            cuda_version: 11.8.0
            python_version: "3.10"
-            pytorch: 2.0.0
+            pytorch: 2.0.1
            axolotl_extras:
-          - cuda: cu118
+          - cuda: 118
            cuda_version: 11.8.0
            python_version: "3.9"
-            pytorch: 2.0.0
+            pytorch: 2.0.1
            axolotl_extras: gptq
-          - cuda: cu117
-            cuda_version: 11.7.0
-            python_version: "3.9"
-            pytorch: 1.13.1
-            axolotl_extras:
    runs-on: self-hosted
    steps:
      - name: Checkout
@@ -109,10 +98,9 @@ jobs:
        with:
          context: .
          build-args: |
-            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            BASE_TAG=${{ github.ref_name }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+            CUDA=${{ matrix.cuda }}
          file: ./docker/Dockerfile-runpod
          push: ${{ github.event_name != 'pull_request' }}
-          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
+          tags: ${{ steps.metadata.outputs.tags }}-py${{ matrix.python_version }}-cu${{ matrix.cuda }}-${{ matrix.pytorch }}${{ matrix.axolotl_extras != '' && '-' || '' }}${{ matrix.axolotl_extras }}
          labels: ${{ steps.metadata.outputs.labels }}
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -7,6 +7,7 @@ jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
+      fail-fast: false
      matrix:
        python_version: ["3.9", "3.10"]
    timeout-minutes: 10
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,5 +1,5 @@
 default_language_version:
-    python: python3.9
+    python: python3

 repos:
 -   repo: https://github.com/pre-commit/pre-commit-hooks
--- a/202
+++ b/202
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   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.
--- a/README.md
+++ b/README.md
@@ -24,11 +24,12 @@
 | mpt      | ✅         | ❌    | ❓     | ❌    | ❓            | ❌                 | ❌          | ❓             |
 | falcon   | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❌          | ✅             |
 | gpt-j    | ✅         | ✅    | ✅     | ❌    | ❓            | ❌                 | ❓          | ✅             |
+| XGen     | ✅         | ❓    | ✅     | ❓    | ❓            | ❓                 | ❓          | ✅


 ## Quickstart ⚡

-**Requirements**: Python 3.9 and Pytorch 2.0.
+**Requirements**: Python >=3.9 and Pytorch >=2.0.

 ```bash
 git clone https://github.com/OpenAccess-AI-Collective/axolotl
@@ -36,8 +37,6 @@ git clone https://github.com/OpenAccess-AI-Collective/axolotl
 pip3 install -e .
 pip3 install -U git+https://github.com/huggingface/peft.git

-accelerate config
-
 # finetune lora
 accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml

@@ -52,11 +51,10 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \

 - Docker
  ```bash
-  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.9-cu118-2.0.0
+  docker run --gpus '"all"' --rm -it winglian/axolotl:main-py3.10-cu118-2.0.1
  ```
-  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0`: for runpod
-  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.0-gptq`: for gptq
-  - `winglian/axolotl:dev`: dev branch (not usually up to date)
+  - `winglian/axolotl-runpod:main-py3.10-cu118-2.0.1`: for runpod
+  - `winglian/axolotl-runpod:main-py3.9-cu118-2.0.1-gptq`: for gptq

  Or run on the current files for development:

@@ -108,7 +106,7 @@ accelerate launch scripts/finetune.py examples/openllama-3b/lora.yml \

  3. Install torch
  ```bash
-  pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+  pip3 install -U torch --index-url https://download.pytorch.org/whl/cu118
  ```

  4. Axolotl
@@ -138,7 +136,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
  ```json
  {"instruction": "...", "input": "...", "output": "..."}
  ```
- `sharegpt`: conversations
+- `sharegpt:chat`: conversations
  ```json
  {"conversations": [{"from": "...", "value": "..."}]}
  ```
@@ -195,6 +193,10 @@ Have dataset(s) in one of the following format (JSONL recommended):
  ```json
  {"message_1": "...", "message_2": "..."}
  ```
+- `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct
+  ```json
+  {"system_prompt": "...", "question": "...", "response": "..."}
+  ```
 - `context_qa`: in context question answering from an article
  ```json
  {"article": "...", "question": "...", "answer": "..."}
@@ -233,7 +235,7 @@ Have dataset(s) in one of the following format (JSONL recommended):
 #### How to add custom prompts

  1. Add your method to a file in [prompt_strategies](src/axolotl/prompt_strategies). Please see other files as example.
-  2. Use your custom file name as the dataset type.
+  2. Use your custom file name as the dataset type `<prompt_strategies_file>.load_<load_fn>`.

 Optionally, download some datasets, see [data/README.md](data/README.md)

@@ -241,7 +243,7 @@ Optionally, download some datasets, see [data/README.md](data/README.md)

 ### Config

-See sample configs in [configs](configs) folder or [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:
+See [examples](examples) for quick start. It is recommended to duplicate and modify to your needs. The most important options are:

 - model
  ```yaml
@@ -251,10 +253,24 @@ See sample configs in [configs](configs) folder or [examples](examples) for quic

 - dataset
  ```yaml
+  sequence_len: 2048 # max token length for prompt
+
+  # huggingface repo
  datasets:
-    - path: vicgalle/alpaca-gpt4 # local or huggingface repo
+    - path: vicgalle/alpaca-gpt4
+      type: alpaca # format from earlier
+
+  # huggingface repo with specific configuration/subset
+  datasets:
+    - path: EleutherAI/pile
+      name: enron_emails
+      type: completion # format from earlier
+
+  # local
+  datasets:
+    - path: json
+      data_files: data.jsonl # or json
      type: alpaca # format from earlier
-  sequence_len: 2048 # max token length / prompt
  ```

 - loading
@@ -264,6 +280,8 @@ See sample configs in [configs](configs) folder or [examples](examples) for quic
  bf16: true # require >=ampere
  fp16: true
  tf32: true # require >=ampere
+  bfloat16: true # require >=ampere, use instead of bf16 when you don't want AMP (automatic mixed precision)
+  float16: true # use instead of fp16 when you don't want AMP
  ```
  Note: Repo does not do 4-bit quantization.

@@ -291,6 +309,8 @@ 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
 base_model_config: ./llama-7b-hf
+# 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
 tokenizer_config:
@@ -300,6 +320,11 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Trust remote code for untrusted source
 trust_remote_code:
+# use_fast option for tokenizer loading from_pretrained, default to True
+tokenizer_use_fast:
+# 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:

 # whether you are training a 4-bit GPTQ quantized model
 gptq: true
@@ -320,18 +345,21 @@ tf32: true # require >=ampere

 # a list of one or more datasets to finetune the model with
 datasets:
-  # this can be either a hf dataset, or relative path
+  # 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 OR format:prompt_style (chat/instruct)
+    type: alpaca # format | format:<prompt_style> (chat/instruct) | <prompt_strategies>.load_<load_fn>
    data_files: # path to source data files
    shards: # number of shards to split data into
+    name: # name of dataset configuration to load

 # 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_dataset_to_hub: # repo path
+# push checkpoints to hub
+hub_model_id: # repo path to push finetuned model
 # 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
@@ -347,7 +375,14 @@ dataset_shard_idx:
 sequence_len: 2048
 # 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'
+sample_packing:
+# 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
 adapter: lora
@@ -373,11 +408,12 @@ lora_out_dir:
 lora_fan_in_fan_out: false

 # wandb configuration if you're using it
-wandb_mode:
-wandb_project:
+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_watch:
-wandb_run_id:
-wandb_log_model: # 'checkpoint'
+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 finished model to
 output_dir: ./completed-model
@@ -392,10 +428,16 @@ learning_rate: 0.00003
 logging_steps:
 save_steps:
 eval_steps:
+save_total_limit:
+
+# save model as safetensors (require safetensors package)
+save_safetensors:

 # whether to mask out or include the human's prompt from the training labels
 train_on_inputs: false
-# don't use this, leads to wonky training (according to someone on the internet)
+# 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
@@ -420,7 +462,15 @@ log_sweep_max_lr:
 optimizer:
 # specify weight decay
 weight_decay:
+# adamw hyperparams
+adam_beta1:
+adam_beta2:
+adam_epsilon:
+# Gradient clipping max norm
+max_grad_norm:

+# whether to bettertransformers
+flash_optimum:
 # whether to use xformers attention patch https://github.com/facebookresearch/xformers:
 xformers_attention:
 # whether to use flash attention patch https://github.com/HazyResearch/flash-attention:
@@ -433,6 +483,10 @@ 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
 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_checkpoint:
@@ -464,6 +518,9 @@ torchdistx_path:
 # Set padding for data collator to 'longest'
 collator_pad_to_longest:

+# Set to HF dataset for type: 'completion' for streaming instead of pre-tokenize
+pretraining_dataset:
+
 # Debug mode
 debug:

@@ -476,17 +533,6 @@ strict:

 </details>

-### Accelerate
-
-Configure accelerate
-
-```bash
-accelerate config
-
-# Edit manually
-# nano ~/.cache/huggingface/accelerate/default_config.yaml
-```
-
 ### Train

 Run
@@ -494,6 +540,40 @@ Run
 accelerate launch scripts/finetune.py configs/your_config.yml
 ```

+#### Multi-GPU
+
+You can optionally pre-tokenize dataset with the following before finetuning:
+```bash
+CUDA_VISIBLE_DEVICES="" accelerate ... --prepare_ds_only
+```
+
+##### Config
+
+- llama FSDP
+```yaml
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_offload_params: true
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+```
+
+- llama Deepspeed: append `ACCELERATE_USE_DEEPSPEED=true` in front of finetune command
+
+##### Weights & Biases Logging
+
+- wandb options
+```yaml
+wandb_mode:
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+```
+
 ### Inference

 Pass the appropriate flag to the train command:
@@ -520,6 +600,12 @@ Add below flag to train command above
 --merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
 ```

+If you run out of CUDA memory, you can try to merge in system RAM with
+
+```bash
+CUDA_VISIBLE_DEVICES="" python3 scripts/finetune.py ...
+```
+
 ## Common Errors 🧰

 > Cuda out of memory
@@ -538,6 +624,10 @@ Try set `fp16: true`

 Try to turn off xformers.

+> accelerate config missing
+
+It's safe to ignore it.
+
 ## Need help? 🙋♂️

 Join our [Discord server](https://discord.gg/HhrNrHJPRb) where we can help you
--- a/data/README.md
+++ b/data/README.md
@@ -10,10 +10,10 @@ curl https://github.com/teknium1/GPTeacher/blob/main/Roleplay/roleplay-similarit
 ## Convert the JSON data files to JSONL.

 ```shell
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/alpaca_data_gpt4.json > data/alpaca_data_gpt4.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/vicuna_cleaned.json > data/vicuna_cleaned.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/roleplay-similarity_0.6-instruct-dataset.json > data/roleplay-similarity_0.6-instruct-dataset.jsonl
-python3 ./scripts/alpaca_json_to_jsonl.py --input data/raw/gpt4-instruct-similarity-0.6-dataset.json > data/gpt4-instruct-similarity-0.6-dataset.jsonl
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/alpaca_data_gpt4.json --output data/alpaca_data_gpt4.jsonl
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/vicuna_cleaned.json --output data/vicuna_cleaned.jsonl
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/roleplay-similarity_0.6-instruct-dataset.json --output data/roleplay-similarity_0.6-instruct-dataset.jsonl
+python3 ./scripts/alpaca_json_to_jsonl.py --file data/raw/gpt4-instruct-similarity-0.6-dataset.json --output data/gpt4-instruct-similarity-0.6-dataset.jsonl
 ```
 ---

--- a/deepspeed/zero3.json
+++ b/deepspeed/zero3.json
@@ -37,18 +37,18 @@
      "lr": "auto",
      "betas": [
        0.9,
-        0.999
+        0.95
      ],
      "eps": 1e-8,
      "weight_decay": "auto"
    }
  },
  "scheduler": {
-    "type": "OneCycle",
+    "type": "WarmupLR",
    "params": {
-      "cycle_min_lr": 0.00001,
-      "cycle_max_lr": 0.00003,
-      "cycle_first_step_size": 120
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto"
    }
  },
  "train_batch_size": "auto",
--- a/docker/Dockerfile
+++ b/docker/Dockerfile
@@ -3,16 +3,15 @@ FROM winglian/axolotl-base:$BASE_TAG

 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

 RUN apt-get update && \
    apt-get install -y vim curl

 WORKDIR /workspace

-RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.git@main" \
-            "accelerate @ git+https://github.com/huggingface/accelerate.git@main" \
-            "transformers @ git+https://github.com/huggingface/transformers.git@main"
-
+RUN pip3 install --force-reinstall "peft @ git+https://github.com/huggingface/peft.git@main"
 RUN git clone --depth=1 https://github.com/OpenAccess-AI-Collective/axolotl.git
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN cd axolotl && \
@@ -22,5 +21,10 @@ RUN cd axolotl && \
        pip install -e .; \
    fi

+# fix so that git fetch/pull from remote works
+RUN cd axolotl && \
+    git config remote.origin.fetch "+refs/heads/*:refs/remotes/origin/*" && \
+    git config --get remote.origin.fetch
+
 # helper for huggingface-login cli
 RUN git config --global credential.helper store
--- a/docker/Dockerfile-base
+++ b/docker/Dockerfile-base
@@ -8,7 +8,7 @@ FROM nvidia/cuda:$CUDA_VERSION-cudnn$CUDNN_VERSION-devel-ubuntu$UBUNTU_VERSION a
 ENV PATH="/root/miniconda3/bin:${PATH}"

 ARG PYTHON_VERSION="3.9"
-ARG PYTORCH="2.0.0"
+ARG PYTORCH_VERSION="2.0.1"
 ARG CUDA="118"

 ENV PYTHON_VERSION=$PYTHON_VERSION
@@ -29,17 +29,18 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace

 RUN python3 -m pip install --upgrade pip && pip3 install packaging && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH} torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu$CUDA
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA


 FROM base-builder AS flash-attn-builder

 WORKDIR /workspace

-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"

-RUN git clone https://github.com/HazyResearch/flash-attention.git && \
+RUN git clone https://github.com/Dao-AILab/flash-attention.git && \
    cd flash-attention && \
+    git checkout v2.0.1  && \
    python3 setup.py bdist_wheel && \
    cd csrc/fused_dense_lib && \
    python3 setup.py bdist_wheel && \
@@ -52,7 +53,7 @@ RUN git clone https://github.com/HazyResearch/flash-attention.git && \

 FROM base-builder AS deepspeed-builder

-ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6+PTX"
+ARG TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 9.0+PTX"

 WORKDIR /workspace

@@ -73,11 +74,14 @@ RUN git clone https://github.com/TimDettmers/bitsandbytes.git && \

 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
 #  `MAX_JOBS=1` disables parallel building to avoid cpu memory OOM when building image on GitHub Action (standard) runners
-RUN cd apex && MAX_JOBS=1 python3 -m pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check .
+RUN cd apex && MAX_JOBS=1 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
@@ -97,4 +101,4 @@ RUN cd /workspace/builds/bitsandbytes && python3 setup.py install
 RUN git lfs install --skip-repo
 RUN pip3 install awscli && \
    # The base image ships with `pydantic==1.8.2` which is not working
-    pip3 install -U --no-cache-dir pydantic
+    pip3 install -U --no-cache-dir pydantic==1.10.10
--- a/docker/Dockerfile-runpod
+++ b/docker/Dockerfile-runpod
@@ -1,6 +1,10 @@
 ARG BASE_TAG=main
 FROM winglian/axolotl:$BASE_TAG

+ENV HF_DATASETS_CACHE="/workspace/data/huggingface-cache/datasets"
+ENV HUGGINGFACE_HUB_CACHE="/workspace/data/huggingface-cache/hub"
+ENV TRANSFORMERS_CACHE="/workspace/data/huggingface-cache/hub"
+
 COPY scripts/runpod-entrypoint.sh /root/runpod-entrypoint.sh

 RUN apt install --yes --no-install-recommends openssh-server tmux && \
--- a/examples/cerebras/qlora.yml
+++ b/examples/cerebras/qlora.yml
@@ -23,6 +23,7 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -35,7 +36,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: true
 fp16: false
 tf32: true
--- a/examples/falcon/config-7b-lora.yml
+++ b/examples/falcon/config-7b-lora.yml
@@ -24,6 +24,7 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/falcon/config-7b-qlora.yml
+++ b/examples/falcon/config-7b-qlora.yml
@@ -38,6 +38,7 @@ lora_target_linear: true
 lora_fan_in_fan_out:

 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/falcon/config-7b.yml
+++ b/examples/falcon/config-7b.yml
@@ -24,6 +24,7 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/gptj/qlora.yml
+++ b/examples/gptj/qlora.yml
@@ -20,6 +20,7 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -32,7 +33,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0001
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: true
 fp16: false
 tf32: true
--- a/examples/gptq-lora-7b/config.yml
+++ b/examples/gptq-lora-7b/config.yml
@@ -22,6 +22,7 @@ lora_target_modules:
  - v_proj
 lora_fan_in_fan_out: false
 wandb_project: llama-7b-lora-int4
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/jeopardy-bot/config.yml
+++ b/examples/jeopardy-bot/config.yml
@@ -18,6 +18,7 @@ lora_dropout:
 lora_target_modules:
 lora_fan_in_fan_out: false
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/llama-2/README.md
+++ b/examples/llama-2/README.md
@@ -0,0 +1,20 @@
+# Overview
+
+This is an example of a llama-2 configuration for 7b and 13b. The yaml file contains configuration for the 7b variant, but you can just aswell use the same settings for 13b.
+
+The 7b variant fits on any 24GB VRAM GPU and will take up about 17 GB of VRAM during training if using qlora and 20 GB if using lora. On a RTX 4090 it trains 3 epochs of the default dataset in about 15 minutes.
+
+The 13b variant will fit if you change these settings to these values:
+gradient_accumulation_steps: 2
+micro_batch_size: 1
+
+```shell
+accelerate launch scripts/finetune.py examples/llama-2/qlora.yml
+
+```
+or
+
+```shell
+accelerate launch scripts/finetune.py examples/llama-2/lora.yml
+
+```
--- a/examples/llama-2/lora.yml
+++ b/examples/llama-2/lora.yml
@@ -0,0 +1,67 @@
+base_model: meta-llama/Llama-2-7b-hf
+base_model_config: meta-llama/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: mhenrichsen/alpaca_2k_test
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: ./lora-out
+
+sequence_len: 4096
+max_packed_sequence_len: 4096
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+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: 0.0002
+
+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: true
+flash_attention:
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
+  pad_token: "<pad>"
--- a/examples/llama-2/qlora.yml
+++ b/examples/llama-2/qlora.yml
@@ -0,0 +1,68 @@
+base_model: meta-llama/Llama-2-7b-hf
+base_model_config: meta-llama/Llama-2-7b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: LlamaTokenizer
+
+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: 4096
+max_packed_sequence_len: 4096
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: paged_adamw_32bit
+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: true
+flash_attention:
+
+warmup_steps: 10
+eval_steps: 20
+save_steps:
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"
+  pad_token: "<pad>"
--- a/examples/mpt-7b/config.yml
+++ b/examples/mpt-7b/config.yml
@@ -20,6 +20,7 @@ lora_target_modules:
  - v_proj
 lora_fan_in_fan_out: false
 wandb_project: mpt-alpaca-7b
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/openllama-3b/config.yml
+++ b/examples/openllama-3b/config.yml
@@ -22,6 +22,7 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/openllama-3b/lora.yml
+++ b/examples/openllama-3b/lora.yml
@@ -28,6 +28,7 @@ lora_target_modules:
  - o_proj
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/openllama-3b/qlora.yml
+++ b/examples/openllama-3b/qlora.yml
@@ -22,6 +22,7 @@ lora_target_modules:
 lora_target_linear: true
 lora_fan_in_fan_out:
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
@@ -34,7 +35,7 @@ torchdistx_path:
 lr_scheduler: cosine
 learning_rate: 0.0002
 train_on_inputs: false
-group_by_length: true
+group_by_length: false
 bf16: true
 fp16: false
 tf32: true
--- a/examples/pythia-12b/README.md
+++ b/examples/pythia-12b/README.md
@@ -0,0 +1,9 @@
+# Pythia 12B
+
+- Single-GPU A100 only (?)
+
+```shell
+python scripts/finetune.py examples/pythia-12b/config.yml
+```
+
+⚠️ Multiple-GPU A100 - Doesn't seem to work with multi-gpu without causing OOM! ⚠️
--- a/examples/pythia-12b/config.yml
+++ b/examples/pythia-12b/config.yml
@@ -0,0 +1,50 @@
+base_model: EleutherAI/pythia-12b-deduped
+base_model_config: EleutherAI/pythia-12b-deduped
+base_model_ignore_patterns: pytorch*  # prefer safetensors
+model_type: GPTNeoXForCausalLM
+tokenizer_type: AutoTokenizer
+load_in_8bit: false
+load_in_4bit: false
+gptq: false
+device_map: auto
+datasets:
+  - path: vicgalle/alpaca-gpt4
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+adapter:
+lora_model_dir:
+sequence_len: 2048
+max_packed_sequence_len: 2048
+lora_r: 64
+lora_alpha: 32
+lora_dropout: 0.0
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./pythia-12b
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 5
+learning_rate: 0.00003
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+train_on_inputs: false
+group_by_length: false
+bf16: false
+fp16: false
+float16: true
+tf32: true
+flash_optimum: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+gradient_checkpointing: true
+fsdp:
+fsdp_config:
+collator_pad_to_longest: true
--- a/examples/pythia/lora.yml
+++ b/examples/pythia/lora.yml
@@ -17,6 +17,7 @@ lora_target_modules:
 lora_target_linear:
 lora_fan_in_fan_out: true  # pythia/GPTNeoX lora specific
 wandb_project:
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/redpajama/config-3b.yml
+++ b/examples/redpajama/config-3b.yml
@@ -1,7 +1,7 @@
 base_model: togethercomputer/RedPajama-INCITE-Chat-3B-v1
 base_model_config: togethercomputer/RedPajama-INCITE-Chat-3B-v1
 model_type: GPTNeoXForCausalLM
-tokenizer_type: GPTNeoXTokenizer
+tokenizer_type: AutoTokenizer
 trust_remote_code:
 load_in_8bit: false
 datasets:
@@ -21,6 +21,7 @@ lora_target_modules:
  - v_proj
 lora_fan_in_fan_out: false
 wandb_project: redpajama-alpaca-3b
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/replit-3b/config-lora.yml
+++ b/examples/replit-3b/config-lora.yml
@@ -20,6 +20,7 @@ lora_target_modules:
  - mlp_down
 lora_fan_in_fan_out:
 wandb_project: lora-replit
+wandb_entity:
 wandb_watch:
 wandb_run_id:
 wandb_log_model:
--- a/examples/xgen-7b/xgen-7b-8k-qlora.yml
+++ b/examples/xgen-7b/xgen-7b-8k-qlora.yml
@@ -0,0 +1,91 @@
+# An example finetuning Saleforce's XGen-7b model with 8k context using qlora
+# on Tim Dettmer's Guanaco dataset.
+base_model: Salesforce/xgen-7b-8k-base
+base_model_config: Salesforce/xgen-7b-8k-base
+trust_remote_code: true
+model_type: AutoModelForCausalLM
+tokenizer_type: AutoTokenizer
+load_in_8bit: false
+# enable 4bit for QLoRA
+load_in_4bit: true
+gptq: false
+strict: false
+push_dataset_to_hub:
+datasets:
+  - path: timdettmers/openassistant-guanaco
+    data_files:
+      - openassistant_best_replies_train.jsonl
+    type: "completion"
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+# enable QLoRA
+adapter: qlora
+lora_model_dir:
+sequence_len: 8192
+max_packed_sequence_len:
+
+# hyperparameters from QLoRA paper Appendix B.2
+# "We find hyperparameters to be largely robust across datasets"
+lora_r: 64
+lora_alpha: 16
+# 0.1 for models up to 13B
+# 0.05 for 33B and 65B models
+lora_dropout: 0.05
+# add LoRA modules on all linear layers of the base model
+lora_target_modules:
+lora_target_linear: true
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+output_dir: ./qlora-out
+
+# QLoRA paper Table 9
+# - 16 for 7b & 13b
+# - 32 for 33b, 64 for 64b
+# Max size tested on A6000
+# - 7b: 40
+# - 40b: 4
+# decrease if OOM, increase for max VRAM utilization
+micro_batch_size: 1
+gradient_accumulation_steps: 1
+num_epochs: 3
+# Optimizer for QLoRA
+optimizer: paged_adamw_32bit
+torchdistx_path:
+lr_scheduler: cosine
+# QLoRA paper Table 9
+# - 2e-4 for 7b & 13b
+# - 1e-4 for 33b & 64b
+learning_rate: 0.00002
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+gradient_checkpointing: true
+# 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
+resume_from_checkpoint:
+auto_resume_from_checkpoints: true
+local_rank:
+logging_steps: 1
+xformers_attention: true
+flash_attention:
+gptq_groupsize:
+gptq_model_v1:
+warmup_steps: 10
+eval_steps: 50
+save_steps: 50
+debug:
+deepspeed:
+weight_decay: 0.0
+special_tokens:
+  eos_token: "<|endoftext|>"
+  bos_token: "<|endoftext|>"
+  unk_token: "<|endoftext|>"
+  pad_token: "<|endoftext|>"
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
 peft @ git+https://github.com/huggingface/peft.git
 transformers @ git+https://github.com/huggingface/transformers.git
-bitsandbytes>=0.39.0
-accelerate
+bitsandbytes>=0.41.1
+accelerate @ git+https://github.com/huggingface/accelerate@2a289f6108e77a77a4efffb3f6316bc98538413b
 addict
 fire
 PyYAML==6.0
@@ -11,9 +11,14 @@ sentencepiece
 wandb
 einops
 xformers
+optimum
+hf_transfer
+numba
+numpy==1.24.4
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0
 rouge-score==0.1.2
 scipy
 scikit-learn==1.2.2
+pynvml
--- a/scripts/alpaca_json_to_jsonl.py
+++ b/scripts/alpaca_json_to_jsonl.py
@@ -15,6 +15,9 @@ from axolotl.convert import (
    JsonToJsonlConverter,
    StdoutWriter,
 )
+from axolotl.logging_config import configure_logging
+
+configure_logging()

 # add src to the pythonpath so we don't need to pip install this
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
--- a/scripts/finetune.py
+++ b/scripts/finetune.py
@@ -12,15 +12,23 @@ from typing import Any, Dict, List, Optional, Union
 import fire
 import torch
 import yaml
-from transformers import GenerationConfig, TextStreamer
-
-from axolotl.utils.data import load_prepare_datasets
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.models import load_model, load_tokenizer

 # add src to the pythonpath so we don't need to pip install this
+from optimum.bettertransformer import BetterTransformer
+from transformers import GenerationConfig, TextStreamer
+
+from axolotl.logging_config import configure_logging
+from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.data import load_prepare_datasets, load_pretraining_dataset
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.distributed import barrier, is_main_process
+from axolotl.utils.models import load_model, load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
-from axolotl.utils.trainer import setup_trainer
+from axolotl.utils.trainer import (
+    calculate_total_num_steps,
+    process_datasets_for_packing,
+    setup_trainer,
+)
 from axolotl.utils.validation import validate_config
 from axolotl.utils.wandb import setup_wandb_env_vars

@@ -28,9 +36,12 @@ project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
 src_dir = os.path.join(project_root, "src")
 sys.path.insert(0, src_dir)

+configure_logging()
+LOG = logging.getLogger("axolotl.scripts")
+

-logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"


 def choose_device(cfg):
@@ -211,18 +222,42 @@ def train(

    # load the tokenizer first
    tokenizer_config = cfg.tokenizer_config or cfg.base_model_config
-    logging.info(f"loading tokenizer... {tokenizer_config}")
+    LOG.info(f"loading tokenizer... {tokenizer_config}")
    tokenizer = load_tokenizer(tokenizer_config, cfg.tokenizer_type, cfg)

    if (
        check_not_in(["shard", "merge_lora"], kwargs) and not cfg.inference
    ):  # don't need to load dataset for these
-        train_dataset, eval_dataset = load_prepare_datasets(
-            tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
-        )
+        if not cfg.pretraining_dataset:
+            train_dataset, eval_dataset = load_prepare_datasets(
+                tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
+            )
+        else:
+            train_dataset = load_pretraining_dataset(
+                cfg.pretraining_dataset,
+                tokenizer,
+                max_tokens=cfg.sequence_len,
+                seed=cfg.seed or 42,
+            )
+            # https://discuss.huggingface.co/t/how-to-use-huggingface-trainer-streaming-datasets-without-wrapping-it-with-torchdatas-iterablewrapper/25230
+            train_dataset = train_dataset.with_format("torch")
+            eval_dataset = None
+
+        if is_main_process():
+            # process on rank 0 first so it gets cached so other ranks load from cache
+            train_dataset, eval_dataset = process_datasets_for_packing(
+                cfg, train_dataset, eval_dataset
+            )
+        barrier()
+        if not is_main_process():
+            train_dataset, eval_dataset = process_datasets_for_packing(
+                cfg, train_dataset, eval_dataset
+            )
+        barrier()
+        total_num_steps = calculate_total_num_steps(cfg, train_dataset, tokenizer)

    if cfg.debug or "debug" in kwargs:
-        logging.info("check_dataset_labels...")
+        LOG.info("check_dataset_labels...")
        check_dataset_labels(
            train_dataset.select(
                [random.randrange(0, len(train_dataset) - 1) for _ in range(5)]  # nosec
@@ -231,32 +266,33 @@ def train(
        )

    if prepare_ds_only:
-        logging.info("Finished preparing dataset. Exiting...")
+        LOG.info("Finished preparing dataset. Exiting...")
        return

+    log_gpu_memory_usage(LOG, "baseline", cfg.device)
+
    # Load the model and tokenizer
-    logging.info("loading model and peft_config...")
-    model, peft_config = load_model(
-        cfg.base_model,
-        cfg.base_model_config,
-        cfg.model_type,
-        tokenizer,
-        cfg,
-        adapter=cfg.adapter,
-    )
+    LOG.info("loading model and (optionally) peft_config...")
+    model, peft_config = load_model(cfg, tokenizer)
+
+    safe_serialization = cfg.save_safetensors is True

    if "merge_lora" in kwargs and cfg.adapter is not None:
-        logging.info("running merge of LoRA with base model")
+        LOG.info("running merge of LoRA with base model")
        model = model.merge_and_unload()
        model.to(dtype=torch.float16)

        if cfg.local_rank == 0:
-            logging.info("saving merged model")
-            model.save_pretrained(str(Path(cfg.output_dir) / "merged"))
+            LOG.info("saving merged model")
+            model.save_pretrained(
+                str(Path(cfg.output_dir) / "merged"),
+                safe_serialization=safe_serialization,
+            )
+            tokenizer.save_pretrained(str(Path(cfg.output_dir) / "merged"))
        return

    if cfg.inference:
-        logging.info("calling do_inference function")
+        LOG.info("calling do_inference function")
        prompter: Optional[str] = "AlpacaPrompter"
        if "prompter" in kwargs:
            if kwargs["prompter"] == "None":
@@ -267,35 +303,40 @@ def train(
        return

    if "shard" in kwargs:
-        model.save_pretrained(cfg.output_dir)
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
        return

-    trainer = setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer)
+    trainer = setup_trainer(
+        cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps
+    )

    model.config.use_cache = False

    if torch.__version__ >= "2" and sys.platform != "win32":
-        logging.info("Compiling torch model")
+        LOG.info("Compiling torch model")
        model = torch.compile(model)

    # go ahead and presave, so we have the adapter config available to inspect
    if peft_config:
-        logging.info(f"Pre-saving adapter config to {cfg.output_dir}")
+        LOG.info(f"Pre-saving adapter config to {cfg.output_dir}")
        peft_config.save_pretrained(cfg.output_dir)

    # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
    if cfg.local_rank == 0:
+
+        def terminate_handler(_, __, model):
+            if cfg.flash_optimum:
+                model = BetterTransformer.reverse(model)
+            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+            sys.exit(0)
+
        signal.signal(
-            signal.SIGINT,
-            lambda signal, frame: (
-                model.save_pretrained(cfg.output_dir),
-                sys.exit(0),
-            ),
+            signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
        )

-    logging.info("Starting trainer...")
+    LOG.info("Starting trainer...")
    if cfg.group_by_length:
-        logging.info("hang tight... sorting dataset for group_by_length")
+        LOG.info("hang tight... sorting dataset for group_by_length")
    resume_from_checkpoint = cfg.resume_from_checkpoint
    if cfg.resume_from_checkpoint is None and cfg.auto_resume_from_checkpoints:
        possible_checkpoints = [
@@ -307,20 +348,34 @@ def train(
                key=lambda path: int(path.split("-")[-1]),
            )
            resume_from_checkpoint = sorted_paths[-1]
-            logging.info(
+            LOG.info(
                f"Using Auto-resume functionality to start with checkpoint at {resume_from_checkpoint}"
            )

    if not Path(cfg.output_dir).is_dir():
        os.makedirs(cfg.output_dir, exist_ok=True)
-    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+    if cfg.flash_optimum:
+        with torch.backends.cuda.sdp_kernel(
+            enable_flash=True, enable_math=True, enable_mem_efficient=True
+        ):
+            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+    else:
+        trainer.train(resume_from_checkpoint=resume_from_checkpoint)

-    logging.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")
+    LOG.info(f"Training Completed!!! Saving pre-trained model to {cfg.output_dir}")

    # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
    # only save on rank 0, otherwise it corrupts output on multi-GPU when multiple processes attempt to write the same file
-    if cfg.local_rank == 0:
-        model.save_pretrained(cfg.output_dir)
+    if cfg.fsdp:
+        trainer.save_model(cfg.output_dir)
+    elif cfg.local_rank == 0:
+        if cfg.flash_optimum:
+            model = BetterTransformer.reverse(model)
+
+        if cfg.adapter == "lora" and cfg.relora_steps:
+            model = model.merge_and_unload()
+
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)

    # trainer.save_model(cfg.output_dir)  # TODO this may be needed for deepspeed to work? need to review another time

--- a/scripts/runpod-entrypoint.sh
+++ b/scripts/runpod-entrypoint.sh
@@ -1,10 +1,21 @@
 #!/bin/bash

-echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
-chmod 700 -R ~/.ssh
+# Export specific ENV variables to /etc/rp_environment
+echo "Exporting environment variables..."
+printenv | grep -E '^RUNPOD_|^PATH=|^_=' | sed 's/^\(.*\)=\(.*\)$/export \1="\2"/' >> /etc/rp_environment
+echo 'source /etc/rp_environment' >> ~/.bashrc

-# Start the SSH service in the background
-service ssh start
+if [[ $PUBLIC_KEY ]]
+then
+    mkdir -p ~/.ssh
+    chmod 700 ~/.ssh
+    echo $PUBLIC_KEY >> ~/.ssh/authorized_keys
+    chmod 700 -R ~/.ssh
+    # Start the SSH service in the background
+    service ssh start
+else
+    echo "No PUBLIC_KEY ENV variable provided, not starting openSSH daemon"
+fi

 # Execute the passed arguments (CMD)
 exec "$@"
--- a/src/axolotl/datasets.py
+++ b/src/axolotl/datasets.py
@@ -1,12 +1,13 @@
 """Module containing Dataset functionality"""

 import logging
+import os
 from typing import List

 import torch
-from datasets import IterableDataset
+from datasets import Dataset, IterableDataset

-from .prompt_tokenizers import InvalidDataException, PromptTokenizingStrategy
+from .prompt_tokenizers import PromptTokenizingStrategy

 # We want this to be a wrapper for an existing dataset that we have loaded
 # lets use the concept of middlewares to wrap each dataset, for example
@@ -14,10 +15,12 @@ from .prompt_tokenizers import InvalidDataException, PromptTokenizingStrategy
 # let's check to ensure we don't truncate an item in the middle, we'll use
 # the collators later on to pad the datasets

+LOG = logging.getLogger("axolotl")

-class TokenizedPromptDataset(IterableDataset):
+
+class TokenizedPromptDataset(Dataset):
    """
-    Iterable dataset that returns tokenized prompts from a stream of text files.
+    Dataset that returns tokenized prompts from a stream of text files.
        Args:
            prompt_tokenizer (PromptTokenizingStrategy): The prompt tokenizing method for proccessing the data.
            dataset (dataset.Dataset): Dataset with text files.
@@ -27,22 +30,19 @@ class TokenizedPromptDataset(IterableDataset):
        self,
        prompt_tokenizer: PromptTokenizingStrategy,
        dataset: IterableDataset,
+        **kwargs,
    ):
        self.prompt_tokenizer = prompt_tokenizer
-        self.dataset = dataset
+        super().__init__(self.process(dataset).data, **kwargs)

-    def __iter__(self):
-        iterator = iter(self.dataset)
-        count = 0
-        # Loop through the entire dataset
-        for example in iterator:
-            try:
-                yield self.prompt_tokenizer.tokenize_prompt(example)
-                count += 1
-            except InvalidDataException:
-                pass
-        if count == 0:
-            raise RuntimeError("Expected at least one datapoint in dataset.")
+    def process(self, dataset):
+        features = dataset.features.keys()
+        num_proc = min(64, os.cpu_count())
+        return dataset.map(
+            self.prompt_tokenizer.tokenize_prompt,
+            num_proc=num_proc,
+            remove_columns=features,
+        )


 # TODO this isn't the best since it can't interleave datasets
@@ -76,14 +76,21 @@ class ConstantLengthDataset(IterableDataset):
            self.tokens_dtype = torch.int64

    def __iter__(self):
-        buffer = {"input_ids": [], "attention_mask": [], "labels": []}
+        buffer = {
+            "input_ids": [],
+            "attention_mask": [],
+            "labels": [],
+            "position_ids": [],
+        }
        buffer_len = 0
        for dataset in self.datasets:
+            idx = 0
            iterator = iter(dataset)
            more_examples = True
            while more_examples:
                try:
                    example = next(iterator)
+                    idx += 1
                except StopIteration:
                    more_examples = False
                    example = None
@@ -105,6 +112,9 @@ class ConstantLengthDataset(IterableDataset):
                        attention_mask = torch.cat(buffer["attention_mask"], dim=-1)[
                            : self.seq_length
                        ]
+                        position_ids = torch.cat(buffer["position_ids"], dim=-1)[
+                            : self.seq_length
+                        ]
                        labels = torch.cat(buffer["labels"], dim=-1)[: self.seq_length]
                        if labels.size() == input_ids.size() and (
                            attention_mask.size() == input_ids.size()
@@ -113,29 +123,28 @@ class ConstantLengthDataset(IterableDataset):
                                "input_ids": input_ids,
                                "labels": labels,
                                "attention_mask": attention_mask,
+                                "position_ids": position_ids,
                            }
                        else:
-                            logging.warning(
+                            LOG.warning(
                                f"dropping batch due to tensor size mismatch input_ids: {input_ids.size()}, labels: {labels.size()}, attention_mask: {attention_mask.size()}"
                            )
                    buffer = {
                        "input_ids": [],
                        "attention_mask": [],
                        "labels": [],
+                        "position_ids": [],
                    }
                    buffer_len = 0
+                    idx = 1

                if example:
+                    # FIXME
                    # just going to drop data points that are too long
                    if len(example["input_ids"]) <= self.seq_length:
                        input_ids = example["input_ids"]
                        attention_mask = example["attention_mask"]
                        labels = example["labels"]
-                        if (
-                            buffer["input_ids"]
-                            and input_ids[0] == self.tokenizer.bos_token_id
-                        ):
-                            attention_mask[0] = 0

                        if add_concat_token:
                            input_ids.append(self.concat_token_id)
@@ -146,13 +155,17 @@ class ConstantLengthDataset(IterableDataset):
                            input_ids, dtype=self.tokens_dtype
                        )
                        attention_mask_with_concat = torch.tensor(
-                            attention_mask, dtype=self.tokens_dtype
+                            [idx * m for m in attention_mask], dtype=torch.int16
                        )
                        labels_with_concat = torch.tensor(
                            labels, dtype=self.tokens_dtype
                        )
+                        position_ids = torch.arange(
+                            len(input_ids), dtype=self.tokens_dtype
+                        )

                        buffer["input_ids"].append(input_ids_with_concat)
                        buffer["attention_mask"].append(attention_mask_with_concat)
                        buffer["labels"].append(labels_with_concat)
+                        buffer["position_ids"].append(position_ids)
                        buffer_len += len(input_ids)
--- a/src/axolotl/logging_config.py
+++ b/src/axolotl/logging_config.py
@@ -0,0 +1,33 @@
+"""Logging configuration settings"""
+
+import os
+import sys
+from logging.config import dictConfig
+from typing import Any, Dict
+
+DEFAULT_LOGGING_CONFIG: Dict[str, Any] = {
+    "version": 1,
+    "formatters": {
+        "simple": {
+            "format": "[%(asctime)s] [%(levelname)s] [%(name)s.%(funcName)s:%(lineno)d] [PID:%(process)d] %(message)s",
+        },
+    },
+    "filters": {},
+    "handlers": {
+        "console": {
+            "class": "logging.StreamHandler",
+            "formatter": "simple",
+            "filters": [],
+            "stream": sys.stdout,
+        },
+    },
+    "root": {"handlers": ["console"], "level": os.getenv("LOG_LEVEL", "INFO")},
+    "loggers": {
+        "axolotl": {"handlers": ["console"], "level": "DEBUG", "propagate": False},
+    },
+}
+
+
+def configure_logging():
+    """Configure with default logging"""
+    dictConfig(DEFAULT_LOGGING_CONFIG)
--- a/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
+++ b/src/axolotl/monkeypatch/llama_attn_hijack_flash.py
@@ -8,9 +8,18 @@ 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 flash_attn_unpadded_qkvpacked_func
+
+try:
+    from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func
+except ImportError:
+    from flash_attn.flash_attn_interface import (
+        flash_attn_unpadded_qkvpacked_func as flash_attn_varlen_qkvpacked_func,
+    )
+
 from transformers.models.llama.modeling_llama import apply_rotary_pos_emb

+from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
+

 def forward(
    self,
@@ -79,7 +88,17 @@ def forward(
            dtype=torch.int32,
            device=qkv.device,
        )
-        output = flash_attn_unpadded_qkvpacked_func(
+        output = flash_attn_varlen_qkvpacked_func(
+            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
+        )
+        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
+    elif position_ids.shape[0] == 1:
+        # special handling using sample packing
+        qkv = rearrange(qkv, "b s ... -> (b s) ...")
+        cu_q_lens, max_s = get_cu_seqlens_from_pos_ids(position_ids)
+        cu_q_lens = cu_q_lens.squeeze()
+
+        output = flash_attn_varlen_qkvpacked_func(
            qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True
        )
        output = rearrange(output, "(b s) ... -> b s ...", b=bsz)
@@ -95,7 +114,7 @@ def forward(
            three=3,
            h=nheads,
        )
-        output_unpad = flash_attn_unpadded_qkvpacked_func(
+        output_unpad = flash_attn_varlen_qkvpacked_func(
            x_unpad,
            cu_q_lens,
            max_s,
@@ -113,6 +132,7 @@ def forward(
            "b s (h d) -> b s h d",
            h=nheads,
        )
+
    return (
        self.o_proj(rearrange(output, "b s h d -> b s (h d)")),
        None,
--- a/src/axolotl/monkeypatch/llama_attn_hijack_xformers.py
+++ b/src/axolotl/monkeypatch/llama_attn_hijack_xformers.py
@@ -7,6 +7,7 @@ import math
 from typing import Optional, Tuple

 import torch
+import torch.nn.functional as F
 import transformers.models.llama.modeling_llama
 from torch import nn

@@ -38,21 +39,48 @@ def xformers_forward(
    # pylint: disable=duplicate-code
    bsz, q_len, _ = hidden_states.size()

-    query_states = (
-        self.q_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-    key_states = (
-        self.k_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
-    value_states = (
-        self.v_proj(hidden_states)
-        .view(bsz, q_len, self.num_heads, self.head_dim)
-        .transpose(1, 2)
-    )
+    if not hasattr(self, "pretraining_tp"):
+        self.pretraining_tp = 1
+
+    if self.pretraining_tp > 1:
+        key_value_slicing = (
+            self.num_key_value_heads * self.head_dim
+        ) // self.pretraining_tp
+        query_slices = self.q_proj.weight.split(
+            (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
+        )
+        key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+        value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+        query_states = [
+            F.linear(hidden_states, query_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        query_states = torch.cat(query_states, dim=-1)
+
+        key_states = [
+            F.linear(hidden_states, key_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        key_states = torch.cat(key_states, dim=-1)
+
+        value_states = [
+            F.linear(hidden_states, value_slices[i]) for i in range(self.pretraining_tp)
+        ]
+        value_states = torch.cat(value_states, dim=-1)
+
+    else:
+        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:
@@ -73,6 +101,14 @@ def xformers_forward(

    past_key_value = (key_states, value_states) if use_cache else None

+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = transformers.models.llama.modeling_llama.repeat_kv(
+        key_states, self.num_key_value_groups
+    )
+    value_states = transformers.models.llama.modeling_llama.repeat_kv(
+        value_states, self.num_key_value_groups
+    )
+
    # We only apply xformers optimizations if we don't need to output the whole attention matrix
    if not output_attentions:
        query_states = query_states.transpose(1, 2)
@@ -92,6 +128,7 @@ def xformers_forward(
                query_states,
                key_states,
                value_states,
+                # attn_bias=attention_mask,
                attn_bias=xformers.ops.LowerTriangularMask(),
            )
        attn_weights = None
@@ -128,10 +165,23 @@ def xformers_forward(
                f" {attn_output.size()}"
            )

-        attn_output = attn_output.transpose(1, 2)
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        # end x-formers vs. not x-formers if-else block

    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-    attn_output = self.o_proj(attn_output)
+
+    if self.pretraining_tp > 1:
+        attn_output = attn_output.split(self.hidden_size // self.pretraining_tp, dim=2)
+        o_proj_slices = self.o_proj.weight.split(
+            self.hidden_size // self.pretraining_tp, dim=1
+        )
+        attn_output = sum(
+            F.linear(attn_output[i], o_proj_slices[i])
+            for i in range(self.pretraining_tp)
+        )
+    else:
+        attn_output = self.o_proj(attn_output)
+
    return attn_output, attn_weights, past_key_value


@@ -184,14 +234,15 @@ def sdp_attention_forward(

    # We only apply sdp attention if we don't need to output the whole attention matrix
    if not output_attentions:
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=attention_mask,
-            is_causal=False,
-        )
-        attn_weights = None
+        with torch.backends.cuda.sdp_kernel():
+            attn_output = torch.nn.functional.scaled_dot_product_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_mask=attention_mask,
+                is_causal=False,
+            )
+            attn_weights = None
    else:
        attn_weights = torch.matmul(
            query_states, key_states.transpose(2, 3)
--- a/src/axolotl/monkeypatch/llama_expand_mask.py
+++ b/src/axolotl/monkeypatch/llama_expand_mask.py
@@ -0,0 +1,52 @@
+"""
+expands the binary attention mask per 3.2.2 of https://arxiv.org/pdf/2107.02027.pdf
+"""
+from typing import Optional
+
+import torch
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    This expansion handles packed sequences so that sequences share the same attention mask integer value
+    when they attend to each other within that sequence.
+    This expansion transforms the mask to lower triangular form to prevent future peeking.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    mask = mask.unsqueeze(1).unsqueeze(2)
+    mask = mask.expand(bsz, 1, tgt_len, src_len)
+
+    # Create a binary mask from the original mask where zeros remain zeros and all other values are set to one
+    binary_mask = torch.where(
+        mask != 0,
+        torch.tensor(1).to(dtype),
+        torch.tensor(0).to(dtype),
+    )
+
+    # Create a block-diagonal mask.
+    # we multiply by the binary mask so that 0's in the original mask are correctly excluded
+    zero_one_mask = torch.eq(mask, mask.transpose(-1, -2)).int() * binary_mask
+
+    # Now let's create a lower triangular mask of ones that will zero out the upper triangular part
+    lower_triangular_ones = torch.tril(torch.ones((tgt_len, src_len), dtype=dtype)).to(
+        mask.device
+    )
+
+    # Use the lower triangular mask to zero out the upper triangular part of the zero_one_mask
+    masked_zero_one_mask = zero_one_mask * lower_triangular_ones
+    inverted_mask = 1.0 - masked_zero_one_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+def hijack_expand_mask():
+    import transformers
+
+    transformers.models.llama.modeling_llama._expand_mask = (  # pylint: disable=protected-access
+        _expand_mask
+    )
--- a/src/axolotl/monkeypatch/llama_landmark_attn.py
+++ b/src/axolotl/monkeypatch/llama_landmark_attn.py
@@ -53,7 +53,7 @@ from transformers.utils import (
    replace_return_docstrings,
 )

-logger = logging.get_logger(__name__)
+LOG = logging.getLogger("axolotl")

 _CONFIG_FOR_DOC = "LlamaConfig"

@@ -862,7 +862,7 @@ class LlamaModel(LlamaPreTrainedModel):

        if self.gradient_checkpointing and self.training:
            if use_cache:
-                logger.warning_once(
+                LOG.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False
--- a/src/axolotl/monkeypatch/relora.py
+++ b/src/axolotl/monkeypatch/relora.py
@@ -0,0 +1,302 @@
+# pylint: skip-file
+import glob
+import json
+import logging
+import os.path
+import shutil
+from pathlib import Path
+from typing import Dict, List, Sequence
+
+import bitsandbytes as bnb
+import peft
+import safetensors.torch as st
+import torch
+from torch.optim.lr_scheduler import LRScheduler
+from torch.optim.optimizer import Optimizer
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+
+from axolotl.utils.dict import DictDefault
+
+LOG = logging.getLogger("axolotl.relora")
+
+
+def reset_optimizer(optimizer: torch.optim.Optimizer):
+    for group in optimizer.param_groups:
+        for param in group["params"]:
+            param_state = optimizer.state[param]
+            for key in param_state:
+                if "qmap" in key:
+                    continue
+                elif key == "step" and isinstance(param_state[key], int):
+                    param_state[key] = 0
+                else:
+                    param_state[key] = torch.zeros_like(param_state[key])
+
+
+class ReLoRACallback(TrainerCallback):
+    def __init__(self, cfg: DictDefault):
+        self.relora_steps = cfg.relora_steps
+        self.cpu_offload = cfg.relora_cpu_offload
+        self.quantised = cfg.load_in_4bit or cfg.load_in_8bit
+        self.last_full_model = cfg.base_model
+
+        assert os.path.exists(
+            self.last_full_model
+        ), "for ReLORA base_model must be a local path"
+
+        self.num_lora_restarts = 0
+        self.need_full_save = False
+
+    def on_step_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        model: peft.LoraModel,
+        optimizer: torch.optim.Optimizer,
+        **_kwargs,
+    ):
+        if state.global_step > 0 and state.global_step % self.relora_steps == 0:
+            checkpoint_folder = os.path.join(
+                args.output_dir,
+                f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
+            )
+
+            with torch.no_grad():
+                merge_and_save(
+                    model,
+                    self.last_full_model,
+                    checkpoint_folder,
+                    reinit=True,
+                    quantized=self.quantised,
+                )
+                reset_optimizer(optimizer)
+
+            if self.quantised:
+                self.last_full_model = checkpoint_folder
+            self.num_lora_restarts += 1
+
+        return control
+
+    def on_save(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        model: peft.LoraModel,
+        **kwargs,
+    ):
+        checkpoint_folder = os.path.join(
+            args.output_dir,
+            f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
+        )
+        if (
+            state.global_step >= self.relora_steps
+            and state.global_step % self.relora_steps != 0
+        ):
+            if self.quantised and self.last_full_model != checkpoint_folder:
+                # ensure the latest full parameter save is in the latest checkpoint
+                # folder, so that automatic pruning of checkpoints does not remove it
+                LOG.info(f"moving last full parameter save to {checkpoint_folder}")
+                chunks = glob.glob(
+                    f"{self.last_full_model}/model*.safetensors"
+                ) + glob.glob(f"{self.last_full_model}/model*.index.json")
+                for path in chunks:
+                    shutil.move(path, checkpoint_folder)
+                self.last_full_model = checkpoint_folder
+            else:
+                model.model.save_pretrained(checkpoint_folder, save_safetensors=True)
+
+        return control
+
+    def on_log(
+        self,
+        _args: TrainingArguments,
+        _state: TrainerState,
+        control: TrainerControl,
+        logs: Dict[str, float],
+        **_kwargs,
+    ):
+        logs["num_lora_restarts"] = self.num_lora_restarts
+        return control
+
+
+class ReLoRAScheduler(LRScheduler):
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        inner_schedule: LRScheduler,
+        relora_steps: int,
+        warmup_steps: int,
+        min_lr_scale: float = 0.001,
+    ) -> None:
+        self.inner_schedule = inner_schedule
+        self.relora_steps = relora_steps
+        self.warmup_steps = warmup_steps
+        self.min_lr_scale = min_lr_scale
+        super().__init__(optimizer, inner_schedule.last_epoch, inner_schedule.verbose)
+
+    def get_lr(self) -> float:
+        self.inner_schedule.last_epoch = self.last_epoch
+
+        original = self.inner_schedule.get_lr()
+        step = self.last_epoch
+        if step < self.relora_steps:
+            scale = 1
+        else:
+            cycle_t = min(1.0, (step % self.relora_steps) / self.warmup_steps)
+            scale = cycle_t * (1 - self.min_lr_scale) + self.min_lr_scale
+        if isinstance(original, Sequence):
+            return [lr * scale for lr in original]
+        else:
+            return original * scale
+
+
+def sharded_paths(path: str, keys: List[str]) -> Dict[str, str]:
+    model_name = "model.safetensors"
+    if not os.path.exists(str(Path(path) / model_name)) and not os.path.exists(
+        str(Path(path) / f"{model_name}.index.json")
+    ):
+        model_name = "pytorch_model.bin"
+
+    index_path = str(Path(path) / f"{model_name}.index.json")
+    if os.path.exists(index_path):
+        data = json.load(open(index_path, "r"))
+        return data["weight_map"]
+    return {key + ".weight": model_name for key in keys}
+
+
+def lora_delta_weight(layer: peft.tuners.lora.LoraLayer) -> torch.Tensor:
+    if isinstance(layer, peft.tuners.lora.Linear8bitLt) or isinstance(
+        layer, peft.tuners.lora.Linear4bit
+    ):
+        adapter = layer.active_adapter
+        return (
+            peft.utils.transpose(
+                layer.lora_B[adapter].weight @ layer.lora_A[adapter].weight,
+                getattr(layer, "fan_in_fan_out", False),
+            )
+            * layer.scaling[adapter]
+        )
+    else:
+        return layer.get_delta_weight()
+
+
+def merge_and_save(
+    model: peft.LoraModel,
+    model_src: str,
+    model_dst: str,
+    reinit: bool = False,
+    quantized: bool = False,
+    cpu_offload: bool = False,
+):
+    key_list = [key for key, _ in model.model.named_modules() if "lora" not in key]
+
+    if not quantized:
+        for key in key_list:
+            try:
+                _parent, target, _target_name = peft.utils._get_submodules(
+                    model.model, key
+                )
+            except AttributeError:
+                continue
+
+            if isinstance(target, peft.tuners.lora.LoraLayer):
+                update = target.get_delta_weight(target.active_adapter).detach()
+                target.weight.data += update
+
+                if reinit:
+                    for adapter_name in target.lora_A:
+                        target.reset_lora_parameters(adapter_name)
+                    for adapter_name in target.lora_embedding_A:
+                        target.reset_lora_parameters(adapter_name)
+        return
+
+    os.makedirs(model_dst, exist_ok=True)
+    shard_paths = sharded_paths(model_src, key_list)
+
+    unique_shards = list(set(shard_paths.values()))
+    for shard_path in unique_shards:
+        out_tensors = {}
+        if shard_path.endswith(".safetensors"):
+            in_tensors = st.load_file(str(Path(model_src) / shard_path))
+        else:
+            in_tensors = torch.load(Path(model_src) / shard_path)
+            if "state_dict" in in_tensors:
+                in_tensors = in_tensors["state_dict"]
+
+        for key in key_list:
+            if (key + ".weight") not in shard_paths or shard_paths[
+                key + ".weight"
+            ] != shard_path:
+                continue
+
+            try:
+                _parent, target, _target_name = peft.utils._get_submodules(
+                    model.model, key
+                )
+            except AttributeError:
+                continue
+
+            if isinstance(target, peft.tuners.lora.LoraLayer):
+                orig_weight = in_tensors[key + ".weight"]
+                old_dev = target.weight.device
+                math_dev = "cpu" if cpu_offload else old_dev
+
+                update = lora_delta_weight(target).detach().to(math_dev)
+                new_weight = orig_weight.to(math_dev) + update
+                out_tensors[key + ".weight"] = new_weight
+
+                if reinit:
+                    for adapter_name in target.lora_A:
+                        target.reset_lora_parameters(adapter_name)
+                    for adapter_name in target.lora_embedding_A:
+                        target.reset_lora_parameters(adapter_name)
+
+                if isinstance(target, peft.tuners.lora.Linear4bit):
+                    target.weight = (
+                        bnb.nn.Params4bit(
+                            new_weight,
+                            requires_grad=False,
+                            compress_statistics=target.weight.compress_statistics,
+                            quant_type=target.weight.quant_type,
+                        )
+                        .cuda(None)
+                        .to(old_dev)
+                    )
+                elif isinstance(target, peft.tuners.lora.Linear8bitLt):
+                    target.weight = (
+                        bnb.nn.Int8Params(new_weight, requires_grad=False)
+                        .cuda(None)
+                        .to(old_dev)
+                    )
+                else:
+                    target.weight.data = new_weight.to(old_dev)
+
+        for key in in_tensors:
+            if key not in out_tensors:
+                out_tensors[key] = in_tensors[key]
+        del in_tensors
+
+        out_shard_name = shard_path
+        if out_shard_name.startswith("pytorch_model"):
+            out_shard_name = (
+                out_shard_name.replace("pytorch_model", "model").rstrip(".bin")
+                + ".safetensors"
+            )
+
+        shard_fn = str(Path(model_dst) / out_shard_name)
+        LOG.info(f"saving tensors to {shard_fn}")
+        st.save_file(out_tensors, shard_fn)
+        del out_tensors
+        torch.cuda.empty_cache()
+
+    if len(unique_shards) > 1:
+        with open(str(Path(model_dst, "model.safetensors.index.json")), "w") as fd:
+            json.dump({"metadata": {}, "weight_map": shard_paths}, fd)
--- a/src/axolotl/monkeypatch/utils.py
+++ b/src/axolotl/monkeypatch/utils.py
@@ -0,0 +1,103 @@
+"""
+Shared utils for the monkeypatches
+"""
+import torch
+
+
+def get_cu_seqlens(attn_mask):
+    """generate a cumulative sequence length mask for flash attention using attn mask"""
+    if len(attn_mask.shape) == 1:
+        attn_mask = attn_mask.unsqueeze(0)
+
+    device = attn_mask.device
+    results = []
+    max_seq_lens = []
+
+    for row in attn_mask:
+        # Exclude zeros to avoid adding their positions to the mask
+        t_non_zeros = row[row != 0]
+        # Find where the sequence number changes (including the first position)
+        seq_change = torch.cat(
+            [
+                torch.tensor([1], dtype=torch.int32, device=device),
+                t_non_zeros[1:] != t_non_zeros[:-1],
+            ]
+        )
+        # Get the indices where the sequence changes
+        change_indices = torch.cat(
+            [
+                (seq_change == 1).nonzero(as_tuple=True)[0],
+                torch.tensor([len(t_non_zeros)], dtype=torch.int32, device=device),
+            ]
+        )
+        # Calculate the sequence lengths
+        seq_lengths = change_indices[1:] - change_indices[:-1]
+        # Calculate the length of the final sequence or padding
+        final_seq_length = len(row) - change_indices[-1]
+        # Append the length of the final sequence or padding to seq_lengths
+        if final_seq_length.item():
+            seq_lengths = torch.cat(
+                [
+                    seq_lengths,
+                    torch.tensor(
+                        [final_seq_length.item()], dtype=torch.int32, device=device
+                    ),
+                ]
+            )
+        # Calculate the cumulative sequence lengths
+        cu_seqlens = torch.cat(
+            [torch.tensor([0], dtype=torch.int32, device=device), seq_lengths.cumsum(0)]
+        )
+        max_seq_len = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+        results.append(cu_seqlens)
+        max_seq_lens.append(max_seq_len)
+
+    return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)
+
+
+def get_cu_seqlens_from_pos_ids(position_ids):
+    """generate a cumulative sequence length mask for flash attention using pos ids"""
+    if len(position_ids.shape) == 1:
+        position_ids = position_ids.unsqueeze(0)
+
+    device = position_ids.device
+    results = []
+    max_seq_lens = []
+
+    for row in position_ids:
+        # Count the number of consecutive zeros from the right side
+        padding_length = (row == 0).int().flip(dims=[0]).cumprod(dim=0).sum().item()
+
+        # Adjust the row to exclude padding
+        adjusted_row = row[:-padding_length] if padding_length else row.clone()
+
+        # Find where the position resets to 0 (indicating a new sequence)
+        seq_starts = torch.cat(
+            [
+                torch.tensor([True], dtype=torch.bool, device=device),
+                adjusted_row[1:] == 0,
+            ]
+        )
+        # Get the indices where the sequence starts
+        start_indices = torch.cat(
+            [
+                (seq_starts).nonzero(as_tuple=True)[0],
+                torch.tensor([len(adjusted_row)], dtype=torch.int32, device=device),
+            ]
+        )
+        # Calculate the sequence lengths
+        seq_lengths = start_indices[1:] - start_indices[:-1]
+        # Calculate the cumulative sequence lengths
+        cu_seqlens = torch.cat(
+            [torch.tensor([0], dtype=torch.int32, device=device), seq_lengths.cumsum(0)]
+        )
+        # Append the padding length to the cumulative sequence lengths
+        if padding_length:
+            cu_seqlens = torch.cat(
+                [cu_seqlens, torch.tensor([len(row)], dtype=torch.int32, device=device)]
+            )
+        max_seq_len = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+        results.append(cu_seqlens)
+        max_seq_lens.append(max_seq_len)
+
+    return torch.stack(results).to(dtype=torch.int32), torch.stack(max_seq_lens)
--- a/src/axolotl/prompt_strategies/alpaca_chat.py
+++ b/src/axolotl/prompt_strategies/alpaca_chat.py
@@ -6,7 +6,7 @@ from axolotl.prompt_tokenizers import (
    AlpacaPromptTokenizingStrategy,
    InstructionPromptTokenizingStrategy,
 )
-from axolotl.prompters import AlpacaPrompter, PromptStyle
+from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter


 def load(tokenizer, cfg):
@@ -20,11 +20,38 @@ def load(tokenizer, cfg):

 class AlpacaConcisePrompter(AlpacaPrompter):
    """
-    Alpaca Prompter extending the system prompt to ask for concise answers
+    Alpaca Prompter extending the system prompt to ask for concise chat-instruct answers
    """

-    system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that concisely and appropriately completes the request.\n\n"
-    system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately and concisely completes the request.\n\n"
+    system_prompt = "Below is an instruction from a USER that describes a task, paired with an input that provides further context. The ASSISTANT writes a response that concisely and appropriately completes the request.\n\n"
+    system_no_input_prompt = "Below is an instruction from a USER that describes a task. The ASSISTANT writes a response that appropriately and concisely completes the request.\n\n"
+
+
+class AlpacaChatPrompter(AlpacaPrompter):
+    """
+    Alpaca Chat Prompter extending the system prompt to for chat-instruct answers
+    """
+
+    system_prompt = "Below is an instruction from a USER that describes a task, paired with an input that provides further context. The ASSISTANT writes a response that concisely and appropriately completes the request.\n\n"
+    system_no_input_prompt = "Below is an instruction from a USER that describes a task. The ASSISTANT writes a response that appropriately and concisely completes the request.\n\n"
+
+    def __init__(self):  # pylint: disable=super-init-not-called
+        self.prompt_style = PromptStyle.CHAT.value
+        self.match_prompt_style()
+
+
+class NoSystemPrompter(AlpacaPrompter):
+    """
+    Null Prompter with no system prompts
+    """
+
+    system_prompt = ""
+    system_no_input_prompt = ""
+    turn_format = "{instruction} {input} "
+    turn_no_input_format = "{instruction} "
+
+    def __init__(self):  # pylint: disable=super-init-not-called
+        pass


 class AlpacaQAPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
@@ -64,7 +91,7 @@ def load_concise(tokenizer, cfg):

 def load_qa(tokenizer, cfg):
    return AlpacaQAPromptTokenizingStrategy(
-        AlpacaPrompter(PromptStyle.CHAT.value),
+        AlpacaChatPrompter(),
        tokenizer,
        cfg.train_on_inputs,
        cfg.sequence_len,
@@ -73,7 +100,16 @@ def load_qa(tokenizer, cfg):

 def load_camel_ai(tokenizer, cfg):
    return CamelAIPromptTokenizingStrategy(
-        AlpacaPrompter(PromptStyle.CHAT.value),
+        AlpacaChatPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_no_prompt(tokenizer, cfg):
+    return AlpacaPromptTokenizingStrategy(
+        UnpromptedPrompter(PromptStyle.CHAT.value),
        tokenizer,
        cfg.train_on_inputs,
        cfg.sequence_len,
--- a/src/axolotl/prompt_strategies/alpaca_instruct.py
+++ b/src/axolotl/prompt_strategies/alpaca_instruct.py
@@ -1,7 +1,7 @@
 """Module loading the AlpacaInstructPromptTokenizingStrategy class"""

 from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter, PromptStyle
+from axolotl.prompters import AlpacaPrompter, PromptStyle, UnpromptedPrompter


 def load(tokenizer, cfg):
@@ -11,3 +11,12 @@ def load(tokenizer, cfg):
        cfg.train_on_inputs,
        cfg.sequence_len,
    )
+
+
+def load_no_prompt(tokenizer, cfg):
+    return AlpacaPromptTokenizingStrategy(
+        UnpromptedPrompter(PromptStyle.INSTRUCT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
--- a/src/axolotl/prompt_strategies/alpaca_w_system.py
+++ b/src/axolotl/prompt_strategies/alpaca_w_system.py
@@ -0,0 +1,160 @@
+"""
+Prompt strategies loader for alpaca instruction datasets with system prompts
+"""
+from typing import Generator, Tuple, Union
+
+from axolotl.prompt_tokenizers import PromptTokenizingStrategy
+from axolotl.prompters import AlpacaPrompter, PromptStyle
+
+
+class InstructionWSystemPromptTokenizingStrategy(PromptTokenizingStrategy):
+    """
+    Tokenizing strategy for instruction-based prompts.
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str]:
+        return (
+            prompt["instruction"],
+            prompt["input"] if "input" in prompt else "",
+            prompt["output"],
+            prompt["system"],
+        )
+
+    def tokenize_prompt(self, prompt):
+        # pylint: disable=duplicate-code
+        (
+            instruction,
+            input,  # pylint: disable=redefined-builtin
+            response,
+            system,
+        ) = self.parse_instruction_fields(prompt)
+        user_prompt = next(
+            iter(
+                self.prompter.build_prompt_w_system(
+                    system,
+                    instruction,
+                    input,
+                )
+            )
+        )
+        tokenized_prompt = self._tokenize(user_prompt, add_eos_token=False)
+        if not self.train_on_inputs:
+            user_prompt_len = len(tokenized_prompt["input_ids"])
+            # TODO this could be sped up using numpy array slicing
+            tokenized_prompt["labels"] = [-100] * user_prompt_len
+        tokenized_res_prompt = self._tokenize(
+            response, strip_bos_token=True, add_eos_token=True
+        )
+        tokenized_prompt["input_ids"] += tokenized_res_prompt["input_ids"]
+        tokenized_prompt["attention_mask"] += tokenized_res_prompt["attention_mask"]
+        tokenized_prompt["labels"] += tokenized_res_prompt["input_ids"]
+
+        return tokenized_prompt
+
+
+class SystemDataPrompter(AlpacaPrompter):
+    """
+    Alpaca Style Prompter that uses system prompts from the dataset
+    """
+
+    def build_prompt_w_system(
+        self,
+        system: str,
+        instruction: str,
+        input: Union[None, str] = None,  # pylint: disable=redefined-builtin
+        output: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        # returns the full prompt from instruction and optional input
+        # if a label (=response, =output) is provided, it's also appended.
+        formatted_sys_prompt = (
+            self.system_format.format(system=system)
+            if system and self.system_format
+            else ""
+        )
+        if input:
+            res = formatted_sys_prompt + self.turn_format.format(
+                instruction=instruction, input=input
+            )
+        else:
+            res = formatted_sys_prompt + self.turn_no_input_format.format(
+                instruction=instruction
+            )
+        if output:
+            res = f"{res}{output}"
+        yield res
+
+
+class OpenOrcaSystemDataPrompter(SystemDataPrompter):
+    """
+    Alpaca Style Prompter that uses system prompts from the dataset, with OpenOrca prompts
+    """
+
+    def match_prompt_style(self):
+        # pylint: disable=duplicate-code
+        if self.prompt_style == PromptStyle.INSTRUCT.value:
+            self.turn_format = "### User:\n{instruction}\n\n### Additional Context:\n{input}\n\n### Assistant:\n"
+            self.turn_no_input_format = "### User:\n{instruction}\n\n### Assistant:\n"
+        if self.prompt_style == PromptStyle.CHAT.value:
+            self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
+            self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
+            self.system_format = "SYSTEM: {system}\n"
+        if self.prompt_style == PromptStyle.CHATML.value:
+            self.turn_format = "<|im_start|>user\n{instruction}\n{input}<|im_end|>\n<|im_start|>assistant\n"
+            self.turn_no_input_format = (
+                "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
+            )
+            self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"
+
+
+class OpenOrcaPromptTokenizingStrategy(InstructionWSystemPromptTokenizingStrategy):
+    """
+    Tokenizing strategy for OpenOrca datasets
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str]:
+        return (
+            prompt["question"],
+            "",
+            prompt["response"],
+            prompt["system_prompt"],
+        )
+
+
+def load(tokenizer, cfg):
+    return load_chat(tokenizer, cfg)
+
+
+def load_instruct(tokenizer, cfg):
+    return InstructionWSystemPromptTokenizingStrategy(
+        SystemDataPrompter(PromptStyle.INSTRUCT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_chat(tokenizer, cfg):
+    return InstructionWSystemPromptTokenizingStrategy(
+        SystemDataPrompter(PromptStyle.CHAT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_open_orca(tokenizer, cfg):
+    return OpenOrcaPromptTokenizingStrategy(
+        OpenOrcaSystemDataPrompter(PromptStyle.INSTRUCT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
+def load_open_orca_chatml(tokenizer, cfg):
+    return OpenOrcaPromptTokenizingStrategy(
+        OpenOrcaSystemDataPrompter(PromptStyle.CHATML.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
--- a/src/axolotl/prompt_strategies/llama2_chat.py
+++ b/src/axolotl/prompt_strategies/llama2_chat.py
@@ -0,0 +1,205 @@
+"""
+Prompt Strategy for finetuning Llama2 chat models
+see also https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L213 for ma reference implementation.
+
+This implementation is based on the Vicuna PR and the fastchat repo, see also:
+https://github.com/lm-sys/FastChat/blob/cdd7730686cb1bf9ae2b768ee171bdf7d1ff04f3/fastchat/conversation.py#L847
+
+Use dataset type: "llama2_chat" in conig.yml to use this prompt style.
+
+E.g. in the config.yml:
+```
+datasets:
+  - path: llama_finetune_train.jsonl
+    type: llama2_chat
+```
+
+The dataset itself should look like this:
+```
+{'conversations':[{"from": "human", "value": "Who are you?"}, {"from": "gpt", "value": "I am Vicuna"},...]}
+```
+in a jsonl file. The first message should be from the human, the second from gpt.
+For a custom system message, the first "from" can be "system" (followed by alternating "human" and "gpt" turns).
+
+Important: Don't use "special_tokens:" in your config.yml if you are not sure what you are doing!
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Generator, List, Sequence
+
+from axolotl.prompt_tokenizers import PromptTokenizingStrategy
+from axolotl.prompters import IGNORE_TOKEN_ID, SHAREGPT_ASSERTION_FAILED_ROLE
+
+
+@dataclass
+class Llama2ChatConversation:
+    """A class that manages prompt templates and keeps all conversation history.
+    copied from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py"""
+
+    name: str = "llama2"
+    # The system prompt
+    system: str = (
+        "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
+        "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
+        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
+        "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
+        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
+    )
+    roles: Sequence[str] = ("[INST]", "[/INST]")
+    messages: List[List[str]] = field(default_factory=list)
+    offset: int = 0
+    sep = " "
+    sep2 = " </s><s>"
+    stop_token_ids = [2]
+
+    def get_prompt(self) -> str:
+        """Get the prompt for generation."""
+        seps = [self.sep, self.sep2]
+        ret = ""
+        for i, (role, message) in enumerate(self.messages):
+            if (i == len(self.messages) - 1) and (role == self.roles[0]):
+                # last message is from user (due to length),
+                #  return prompt without it for training
+                return ret
+            if i == 0:
+                ret += self.system + message.strip()
+            else:
+                ret += role + " " + message.strip() + seps[i % 2]
+        return ret
+
+    def append_message(self, role: str, message: str):
+        """Append a new message."""
+        self.messages.append([role, message])
+
+
+class LLama2ChatTokenizingStrategy(PromptTokenizingStrategy):
+    """
+    Tokenizing strategy for ShareGPT prompts.
+    adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/train/train.py
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.sequence_len = 4096
+        self.tokenizer.add_special_tokens({"pad_token": "<pad>"})
+        # https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/blob/main/added_tokens.json
+
+    def tokenize_prompt(self, prompt):
+        conv = next(self.prompter.build_prompt(prompt))
+        conversation_str = conv.get_prompt()
+
+        # Tokenize conversations
+        input_ids = self.tokenizer(
+            conversation_str,
+            return_tensors="pt",
+            padding="max_length",
+            max_length=self.sequence_len,
+            truncation=True,
+        ).input_ids[0]
+        target = input_ids.clone()
+
+        # Mask targets. Only compute loss on the assistant outputs.
+        sep = conv.roles[1]
+
+        total_len = int(target.ne(self.tokenizer.pad_token_id).sum())
+
+        turns = conversation_str.split(conv.sep2)
+        cur_len = 1
+        target[:cur_len] = IGNORE_TOKEN_ID
+        for turn in turns:
+            if turn == "":
+                break
+            turn_len = len(self.tokenizer(turn).input_ids)
+
+            parts = turn.split(sep)
+            if len(parts) != 2:
+                break
+            parts[0] += sep
+            # "-1" is hardcoded for the LLaMA tokenizer to make the offset correct.
+            instruction_len = len(self.tokenizer(parts[0]).input_ids) - 1
+
+            # Ignore the user instructions
+            target[cur_len - 1 : cur_len + instruction_len] = IGNORE_TOKEN_ID
+            cur_len += turn_len + 2  # due to length of role token
+
+        target[cur_len:] = IGNORE_TOKEN_ID
+
+        if cur_len < self.sequence_len:
+            if cur_len != total_len:
+                target[:] = IGNORE_TOKEN_ID
+                logging.warning(
+                    f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
+                    f" (ignored)"
+                )
+
+        attention_mask = input_ids.ne(self.tokenizer.pad_token_id).tolist()
+        input_ids = input_ids.tolist()
+        target = target.tolist()
+        # this is a fix for the tokenizer which tokenizes [ differently with eos tokens and
+        # follows the original llama implementation
+        for i in range(2, total_len - 2):
+            if input_ids[i] == 29961:
+                input_ids[i] = 518
+            if target[i] == 29961:
+                target[i] = 518
+        return {
+            "input_ids": input_ids,
+            "labels": target,
+            "attention_mask": attention_mask,
+        }
+
+
+class Llama2ChatPrompter:  # pylint: disable=too-few-public-methods
+    """
+    A prompter that generates prompts for Llama2 models.
+    """
+
+    system_prompt = (
+        "[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. "
+        "Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. "
+        "Please ensure that your responses are socially unbiased and positive in nature.\n\n"
+        "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. "
+        "If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n"
+    )
+
+    def build_prompt(self, source) -> Generator[Llama2ChatConversation, None, None]:
+        # see https://github.com/lm-sys/FastChat/blob/da0641e567cf93756b0978ab5a6b092e96f06240/fastchat/train/train.py#L78
+        source = source["conversations"]  # fix data structure for datasets
+
+        # if system prompt provided, use it
+        if source[0]["from"] == "system":
+            system = f"[INST] <<SYS>>\n{source[0]['value']}\n<</SYS>>\n\n"
+            source = source[1:]
+        else:
+            system = self.system_prompt
+
+        conv = Llama2ChatConversation(system=system)
+
+        if len(source) < 2:
+            # If there isn't a back and forth conversation, ignore it
+            # also happens on the data splitting leaving empty conversations
+            raise IndexError
+
+        roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
+
+        if roles[source[0]["from"]] != conv.roles[0]:
+            # Skip the first one if it is not from human
+            source = source[1:]
+
+        conv.messages = []  # pylint: disable=R0801
+        for j, sentence in enumerate(source):
+            role = roles[sentence["from"]]
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
+            if sentence["value"]:
+                conv.append_message(role, sentence["value"])
+        yield conv
+
+
+def load(tokenizer, cfg) -> LLama2ChatTokenizingStrategy:
+    return LLama2ChatTokenizingStrategy(
+        Llama2ChatPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
--- a/src/axolotl/prompt_strategies/orcamini.py
+++ b/src/axolotl/prompt_strategies/orcamini.py
@@ -0,0 +1,46 @@
+"""
+Prompt Strategy for finetuning Orca Mini (v2) models
+see also https://huggingface.co/psmathur/orca_mini_v2_7b for more information
+
+Use dataset type: orcamini in conig.yml to use this prompt style.
+
+Compared to the alpaca_w_system.open_orca dataset type,
+this one specifies the system prompt with "### System:".
+
+Not suited/tested for multiple-turn conversations without further adjustments.
+"""
+from typing import Generator, Union
+
+from axolotl.prompt_strategies.alpaca_w_system import OpenOrcaPromptTokenizingStrategy
+from axolotl.prompters import AlpacaPrompter
+
+
+class OrcaMiniPrompter(AlpacaPrompter):
+    """Adjusted Prompter for Orca Mini (v2) datasets"""
+
+    def match_prompt_style(self):
+        self.turn_no_input_format = (
+            "### System:\n{system}\n\n### User:\n{instruction}\n\n### Response:\n"
+        )
+
+    def build_prompt_w_system(
+        self,
+        system: str,
+        instruction: str,
+        output: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        # returns the full prompt from instruction and optional input
+        # if a label (=response, =output) is provided, it's also appended.
+        res = self.turn_no_input_format.format(system=system, instruction=instruction)
+        if output:
+            res = f"{res}{output}"
+        yield res
+
+
+def load(tokenizer, cfg):
+    return OpenOrcaPromptTokenizingStrategy(
+        OrcaMiniPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
--- a/src/axolotl/prompt_strategies/pygmalion.py
+++ b/src/axolotl/prompt_strategies/pygmalion.py
@@ -11,6 +11,8 @@ from axolotl.prompt_tokenizers import (
    tokenize_prompt_default,
 )

+LOG = logging.getLogger("axolotl")
+
 IGNORE_TOKEN_ID = -100


@@ -64,7 +66,7 @@ class PygmalionPromptTokenizingStrategy(PromptTokenizingStrategy):
                    *copy.deepcopy(res["input_ids"])
                ][len(self.bot_prefix_token_ids) :]
            else:
-                logging.warning(f"unknown role in conversation: {role}")
+                LOG.warning(f"unknown role in conversation: {role}")
                res = defaultdict(lambda: [])

            # pylint: disable=duplicate-code
--- a/src/axolotl/prompt_tokenizers.py
+++ b/src/axolotl/prompt_tokenizers.py
@@ -10,6 +10,8 @@ from transformers import PreTrainedTokenizer

 from axolotl.prompters import IGNORE_TOKEN_ID

+LOG = logging.getLogger("axolotl")
+
 IGNORE_INDEX = -100
 LLAMA_DEFAULT_PAD_TOKEN = "[PAD]"  # nosec
 LLAMA_DEFAULT_EOS_TOKEN = "</s>"  # nosec
@@ -46,16 +48,22 @@ class PromptTokenizingStrategy(abc.ABC):

    @functools.lru_cache(maxsize=128)
    def _get_user_token(self):
-        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|USER|>")
-        if isinstance(id_or_ids, (int,)):
-            return id_or_ids
+        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):
-        id_or_ids = self.tokenizer.convert_tokens_to_ids("<|ASSISTANT|>")
-        if isinstance(id_or_ids, (int,)):
-            return id_or_ids
+        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=True, strip_bos_token=False):
@@ -87,7 +95,9 @@ class InstructionPromptTokenizingStrategy(PromptTokenizingStrategy):
    Tokenizing strategy for instruction-based prompts.
    """

-    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+    def parse_instruction_fields(
+        self, prompt
+    ) -> Union[Tuple[str, str, str], Tuple[str, str, str, str]]:
        raise NotImplementedError

    def tokenize_prompt(self, prompt):
@@ -96,25 +106,27 @@ class InstructionPromptTokenizingStrategy(PromptTokenizingStrategy):
            input,  # pylint: disable=redefined-builtin
            response,
        ) = self.parse_instruction_fields(prompt)
-        full_prompt = self._build_full_prompt(instruction, input, response)
-        tokenized_full_prompt = self._tokenize(full_prompt)
-        if not self.train_on_inputs:
-            user_prompt = next(
-                iter(
-                    self.prompter.build_prompt(
-                        instruction,
-                        input,
-                    )
+        user_prompt = next(
+            iter(
+                self.prompter.build_prompt(
+                    instruction,
+                    input,
                )
            )
-            tokenized_user_prompt = self._tokenize(user_prompt, add_eos_token=False)
-            user_prompt_len = len(tokenized_user_prompt["input_ids"])
+        )
+        tokenized_prompt = self._tokenize(user_prompt, add_eos_token=False)
+        if not self.train_on_inputs:
+            user_prompt_len = len(tokenized_prompt["input_ids"])
            # TODO this could be sped up using numpy array slicing
-            tokenized_full_prompt["labels"] = [
-                -100
-            ] * user_prompt_len + tokenized_full_prompt["labels"][user_prompt_len:]
+            tokenized_prompt["labels"] = [-100] * user_prompt_len
+        tokenized_res_prompt = self._tokenize(
+            response, strip_bos_token=True, add_eos_token=True
+        )
+        tokenized_prompt["input_ids"] += tokenized_res_prompt["input_ids"]
+        tokenized_prompt["attention_mask"] += tokenized_res_prompt["attention_mask"]
+        tokenized_prompt["labels"] += tokenized_res_prompt["input_ids"]

-        return tokenized_full_prompt
+        return tokenized_prompt

    def _build_full_prompt(
        self, instruction, input, response  # pylint: disable=redefined-builtin
@@ -380,7 +392,7 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
                        # everything from this is masked out from the labels
                        labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
                    else:
-                        logging.warning(f"unhandled role: {part[0]}")
+                        LOG.warning(f"unhandled role: {part[0]}")

                # pylint: disable=duplicate-code
                result, current_len = parse_tokenized_to_result(
@@ -436,7 +448,7 @@ def parse_tokenized_to_result(
    result: Dict[str, List[int]],
    current_len: int,
    res: Dict[str, List[int]],
-    labels: list[int],
+    labels: List[int],
    pad_token_id: Union[int, None] = None,
 ) -> Tuple[Dict[str, List[int]], int]:
    """
--- a/src/axolotl/prompters.py
+++ b/src/axolotl/prompters.py
@@ -5,6 +5,7 @@ import logging
 from enum import Enum, auto
 from typing import Generator, List, Optional, Tuple, Union

+LOG = logging.getLogger("axolotl")
 IGNORE_TOKEN_ID = -100


@@ -15,6 +16,7 @@ class PromptStyle(Enum):

    INSTRUCT = "instruct"
    CHAT = "chat"
+    CHATML = "chatml"


 class AlpacaPrompter:
@@ -24,6 +26,9 @@ class AlpacaPrompter:

    system_prompt = "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n"
    system_no_input_prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
+    system_format: str
+    turn_format: str
+    turn_no_input_format: str
    prompt_style: Optional[PromptStyle] = None

    def __init__(self, prompt_style=PromptStyle.INSTRUCT.value):
@@ -31,24 +36,23 @@ class AlpacaPrompter:
        self.match_prompt_style()

    def match_prompt_style(self):
+        # pylint: disable=duplicate-code
        if self.prompt_style == PromptStyle.INSTRUCT.value:
-            self.prompt_input = (
-                self.system_prompt
-                + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
+            self.turn_format = "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
+            self.turn_no_input_format = (
+                "### Instruction:\n{instruction}\n\n### Response:\n"
            )
-            self.prompt_no_input = (
-                self.system_no_input_prompt
-                + "### Instruction:\n{instruction}\n\n### Response:\n"
-            )
-            self.response_split = "### Response:"
+            self.system_format = "### System:\n{system}\n\n"
        if self.prompt_style == PromptStyle.CHAT.value:
-            self.prompt_input = (
-                self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
+            self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
+            self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"
+            self.system_format = "SYSTEM: {system}\n"
+        if self.prompt_style == PromptStyle.CHATML.value:
+            self.turn_format = "<|im_start|>user\n{instruction}\n{input}<|im_end|>\n<|im_start|>assistant\n"
+            self.turn_no_input_format = (
+                "<|im_start|>user\n{instruction}<|im_end|>\n<|im_start|>assistant\n"
            )
-            self.prompt_no_input = (
-                self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
-            )
-            self.response_split = "ASSISTANT:"
+            self.system_format = "<|im_start|>system\n{system}<|im_end|>\n"

    def build_prompt(
        self,
@@ -59,16 +63,17 @@ class AlpacaPrompter:
        # returns the full prompt from instruction and optional input
        # if a label (=response, =output) is provided, it's also appended.
        if input:
-            res = self.prompt_input.format(instruction=instruction, input=input)
+            res = self.system_prompt + self.turn_format.format(
+                instruction=instruction, input=input
+            )
        else:
-            res = self.prompt_no_input.format(instruction=instruction)
+            res = self.system_no_input_prompt + self.turn_no_input_format.format(
+                instruction=instruction
+            )
        if output:
            res = f"{res}{output}"
        yield res

-    def get_response(self, output: str) -> str:
-        return output.split(self.response_split)[1].strip()
-

 class UnpromptedPrompter(AlpacaPrompter):
    """
@@ -93,7 +98,10 @@ class MultipleChoiceExplainPrompter(AlpacaPrompter):
    """

    system_prompt = (
-        "Choose the answer that best answers the question. Explain your reasoning."
+        "Choose the answer that best answers the question. Explain your reasoning.\n"
+    )
+    system_no_input_prompt = (
+        "Choose the answer that best answers the question. Explain your reasoning.\n"
    )


@@ -102,7 +110,12 @@ class MultipleChoiceConcisePrompter(AlpacaPrompter):
    Prompter for multiple choice concise
    """

-    prompt_input = "Choose the answer that best answers the question. Be concise in your response.\n\nUSER: {instruction}\n{input}\nASSISTANT:\n"
+    system_prompt = "Choose the answer that best answers the question. Be concise in your response.\n\n"
+    system_no_input_prompt = "Choose the answer that best answers the question. Be concise in your response.\n\n"
+
+    def match_prompt_style(self):
+        self.turn_format = "USER: {instruction}\n{input}\nASSISTANT:"
+        self.turn_no_input_format = "USER: {instruction}\nASSISTANT:"


 class SummarizeTLDRPrompter(AlpacaPrompter):
@@ -110,9 +123,12 @@ class SummarizeTLDRPrompter(AlpacaPrompter):
    Prompter for summarize TLDR
    """

-    prompt_no_input = (
-        "USER: Summarize the following article as a TL;DR.\n{instruction}\nASSISTANT:"
-    )
+    system_prompt = ""
+    system_no_input_prompt = ""
+
+    def match_prompt_style(self):
+        self.turn_format = "USER: Summarize the following article as a TL;DR.\n{instruction}\n{input}\nASSISTANT:"
+        self.turn_no_input_format = "USER: Summarize the following article as a TL;DR.\n{instruction}\nASSISTANT:"


 class CompletionPrompter:
@@ -128,9 +144,6 @@ class CompletionPrompter:
    ) -> Generator[str, None, None]:
        yield instruction

-    def get_response(self, output: str) -> str:
-        return output.strip()
-

 class GPTeacherPrompter(AlpacaPrompter):
    """
@@ -210,9 +223,6 @@ class ReflectAlpacaPrompter:
            res = f"{res}{label}"
        yield res

-    def get_response(self, output: str) -> str:
-        return output.split(self.response_split)[1].strip()
-

 class SeparatorStyle(Enum):
    """Different separator style."""
@@ -243,7 +253,7 @@ class Conversation:
            if message:
                yield (role + ":", " " + message)
            else:
-                logging.warning(f"role with empty message: {role}")
+                LOG.warning(f"role with empty message: {role}")
                yield (role + ":", "")

    def copy(self):
@@ -261,6 +271,11 @@ class Conversation:
        self.messages.append([role, message])


+SHAREGPT_ASSERTION_FAILED_ROLE = (
+    "Role did not alternate between turns (gpt and human). Please check your data."
+)
+
+
 class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
    """
    A prompter that generates prompts for the ShareGPT
@@ -289,12 +304,6 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
            sep2=" ",
        )

-    # def match_prompt_style(self):
-    #     if self.prompt_style == PromptStyle.chat.value:
-    #         self.prompt_input = self.system_prompt + "USER: {instruction}\n{input}\nASSISTANT:"
-    #         self.prompt_no_input = self.system_no_input_prompt + "USER: {instruction}\nASSISTANT:"
-    #         self.response_split = "ASSISTANT:"
-
    def build_prompt(self, source) -> Generator[str, None, None]:
        # ignore the system prompt if provided
        if source[0]["from"] == "system":
@@ -323,7 +332,7 @@ class ShareGPTPrompter:  # pylint: disable=too-few-public-methods
        conv.messages = []
        for j, sentence in enumerate(source):
            role = roles[sentence["from"]]
-            assert role == conv.roles[j % 2]
+            assert role == conv.roles[j % 2], SHAREGPT_ASSERTION_FAILED_ROLE
            conv.append_message(role, sentence["value"])

        for part in conv.get_prompt():
--- a/src/axolotl/utils/bench.py
+++ b/src/axolotl/utils/bench.py
@@ -0,0 +1,23 @@
+"""Benchmarking and measurement utilities"""
+
+import pynvml
+import torch
+
+
+def gpu_memory_usage(device):
+    if isinstance(device, torch.device):
+        device = device.index
+    if isinstance(device, str) and device.startswith("cuda:"):
+        device = int(device[5:])
+
+    # NB torch.cuda.memory_usage returns zero so we use lower level api
+    pynvml.nvmlInit()
+    handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+    info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+    return info.used / 1024.0**3
+
+
+def log_gpu_memory_usage(log, msg, device):
+    log.info(
+        f"GPU memory usage {msg}: {gpu_memory_usage(device):.03f} GB", stacklevel=2
+    )
--- a/src/axolotl/utils/callbacks.py
+++ b/src/axolotl/utils/callbacks.py
@@ -1,14 +1,20 @@
 """Callbacks for Trainer class"""

+import logging
 import os

+from optimum.bettertransformer import BetterTransformer
 from transformers import (
    TrainerCallback,
    TrainerControl,
    TrainerState,
    TrainingArguments,
 )
-from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, IntervalStrategy
+
+from axolotl.utils.bench import log_gpu_memory_usage
+
+LOG = logging.getLogger("axolotl.callbacks")


 class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-methods
@@ -27,6 +33,66 @@ class SavePeftModelCallback(TrainerCallback):  # pylint: disable=too-few-public-
        )

        peft_model_path = os.path.join(checkpoint_folder, "adapter_model")
-        kwargs["model"].save_pretrained(peft_model_path)
+        kwargs["model"].save_pretrained(
+            peft_model_path, save_safetensors=args.save_safetensors
+        )

        return control
+
+
+class SaveBetterTransformerModelCallback(
+    TrainerCallback
+):  # pylint: disable=too-few-public-methods
+    """Callback to save the BetterTransformer wrapped model"""
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        # Save
+        if (
+            args.save_strategy == IntervalStrategy.STEPS
+            and args.save_steps > 0
+            and state.global_step % args.save_steps == 0
+        ):
+            control.should_save = True
+
+        if control.should_save:
+            checkpoint_folder = os.path.join(
+                args.output_dir,
+                f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}",
+            )
+
+            model = BetterTransformer.reverse(kwargs["model"])
+            model.save_pretrained(checkpoint_folder)
+            # FIXME - need to cleanup old checkpoints
+
+            # since we're saving here, we don't need the trainer loop to attempt to save too b/c
+            # the trainer will raise an exception since it can't save a BetterTransformer wrapped model
+            control.should_save = False
+        return control
+
+
+class PrintGPUStatsCallback(
+    TrainerCallback
+):  # pylint: disable=too-few-public-methods disable=unused-argument
+    """Callback to print GPU utilization"""
+
+    def __init__(self, cfg):
+        self.cfg = cfg
+        self.logged = False
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ):
+        if not self.logged:
+            log_gpu_memory_usage(LOG, "while training", self.cfg.device)
+            self.logged = True
+        return control
--- a/src/axolotl/utils/collators.py
+++ b/src/axolotl/utils/collators.py
@@ -0,0 +1,121 @@
+"""
+DataCollator for axolotl to pad labels and position_ids for packed sequences
+"""
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import numpy as np
+from transformers import PreTrainedTokenizerBase
+from transformers.utils import PaddingStrategy
+
+
+@dataclass
+class DataCollatorForSeq2Seq:
+    """
+    Data collator that will dynamically pad the inputs received, as well as the labels and position_ids
+
+    Args:
+        tokenizer ([`PreTrainedTokenizer`] or [`PreTrainedTokenizerFast`]):
+            The tokenizer used for encoding the data.
+        model ([`PreTrainedModel`]):
+            The model that is being trained. If set and has the *prepare_decoder_input_ids_from_labels*, use it to
+            prepare the *decoder_input_ids*
+
+            This is useful when using *label_smoothing* to avoid calculating loss twice.
+        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
+            among:
+
+            - `True` or `'longest'` (default): Pad to the longest sequence in the batch (or no padding if only a single
+              sequence is provided).
+            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+              acceptable input length for the model if that argument is not provided.
+            - `False` or `'do_not_pad'`: No padding (i.e., can output a batch with sequences of different lengths).
+        max_length (`int`, *optional*):
+            Maximum length of the returned list and optionally padding length (see above).
+        pad_to_multiple_of (`int`, *optional*):
+            If set will pad the sequence to a multiple of the provided value.
+
+            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
+            7.5 (Volta).
+        label_pad_token_id (`int`, *optional*, defaults to -100):
+            The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
+        return_tensors (`str`):
+            The type of Tensor to return. Allowable values are "np", "pt" and "tf".
+    """
+
+    tokenizer: PreTrainedTokenizerBase
+    model: Optional[Any] = None
+    padding: Union[bool, str, PaddingStrategy] = True
+    max_length: Optional[int] = None
+    pad_to_multiple_of: Optional[int] = None
+    label_pad_token_id: int = -100
+    position_pad_token_id: int = 0
+    return_tensors: str = "pt"
+
+    def __call__(self, features, return_tensors=None):
+        labels = None
+        if return_tensors is None:
+            return_tensors = self.return_tensors
+
+        for feature_name, pad_token_id in [
+            ("labels", self.label_pad_token_id),
+            ("position_ids", self.position_pad_token_id),
+        ]:
+            feat = (
+                [feature[feature_name] for feature in features]
+                if feature_name in features[0].keys()
+                else None
+            )
+            labels = feat if feat and feature_name == "labels" else labels
+            # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
+            # same length to return tensors.
+            if feat is not None:
+                max_feature_length = max(len(l) for l in feat)  # noqa: E741
+                if self.pad_to_multiple_of is not None:
+                    max_feature_length = (
+                        (max_feature_length + self.pad_to_multiple_of - 1)
+                        // self.pad_to_multiple_of
+                        * self.pad_to_multiple_of
+                    )
+
+                padding_side = self.tokenizer.padding_side
+                for feature in features:
+                    remainder = [pad_token_id] * (
+                        max_feature_length - len(feature[feature_name])
+                    )
+                    if isinstance(feature[feature_name], list):
+                        feature[feature_name] = (
+                            feature[feature_name] + remainder
+                            if padding_side == "right"
+                            else remainder + feature[feature_name]
+                        )
+                    elif padding_side == "right":
+                        feature[feature_name] = np.concatenate(
+                            [feature[feature_name], remainder]
+                        ).astype(np.int64)
+                    else:
+                        feature[feature_name] = np.concatenate(
+                            [remainder, feature[feature_name]]
+                        ).astype(np.int64)
+
+        features = self.tokenizer.pad(
+            features,
+            padding=self.padding,
+            max_length=self.max_length,
+            pad_to_multiple_of=self.pad_to_multiple_of,
+            return_tensors=return_tensors,
+        )
+
+        # prepare decoder_input_ids
+        if (
+            labels is not None
+            and self.model is not None
+            and hasattr(self.model, "prepare_decoder_input_ids_from_labels")
+        ):
+            decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(
+                labels=features["labels"]
+            )
+            features["decoder_input_ids"] = decoder_input_ids
+
+        return features
--- a/src/axolotl/utils/data.py
+++ b/src/axolotl/utils/data.py
@@ -1,11 +1,19 @@
 """Module containing data utilities"""
-
+import functools
+import hashlib
 import logging
 from hashlib import md5
 from pathlib import Path
-from typing import List, Tuple, Union
+from typing import Tuple, Union

-from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
+import torch
+from datasets import (
+    Dataset,
+    DatasetDict,
+    concatenate_datasets,
+    load_dataset,
+    load_from_disk,
+)
 from huggingface_hub import hf_hub_download
 from transformers import PreTrainedTokenizerBase

@@ -33,6 +41,9 @@ from axolotl.prompters import (
    ShareGPTPrompter,
    SummarizeTLDRPrompter,
 )
+from axolotl.utils.distributed import barrier, is_main_process
+
+LOG = logging.getLogger("axolotl")


 def load_tokenized_prepared_datasets(
@@ -72,17 +83,17 @@ def load_tokenized_prepared_datasets(
    if dataset:
        ...
    elif any(prepared_ds_path.glob("*")):
-        logging.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
+        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
        dataset = load_from_disk(str(prepared_ds_path))
-        logging.info("Prepared dataset loaded from disk...")
+        LOG.info("Prepared dataset loaded from disk...")
    else:
-        logging.info(f"Unable to find prepared dataset in {prepared_ds_path}")
-        logging.info("Loading raw datasets...")
+        LOG.info(f"Unable to find prepared dataset in {prepared_ds_path}")
+        LOG.info("Loading raw datasets...")

        if cfg.seed:
            seed = cfg.seed
        else:
-            logging.info("No seed provided, using default seed of 42")
+            LOG.info("No seed provided, using default seed of 42")
            seed = 42

        datasets = []
@@ -93,6 +104,7 @@ def load_tokenized_prepared_datasets(
            try:
                load_dataset(
                    d.path,
+                    name=d.name,
                    streaming=True,
                    use_auth_token=use_auth_token,
                )
@@ -101,34 +113,46 @@ def load_tokenized_prepared_datasets(
                pass

            # prefer local dataset, even if hub exists
-            if Path(d.path).exists():
-                ds = load_dataset(
-                    "json",
-                    data_files=d.path,
-                    streaming=False,
-                    split=None,
-                )
-            elif ds_from_hub:
-                if d.data_files:
+            local_path = Path(d.path)
+            if local_path.exists():
+                if local_path.is_dir():
+                    # TODO dirs with arrow or parquet files could be loaded with `load_from_disk`
                    ds = load_dataset(
                        d.path,
-                        streaming=False,
+                        name=d.name,
                        data_files=d.data_files,
-                        use_auth_token=use_auth_token,
+                        streaming=False,
+                        split=None,
+                    )
+                elif local_path.is_file():
+                    ds = load_dataset(
+                        "json",
+                        name=d.name,
+                        data_files=d.path,
+                        streaming=False,
+                        split=None,
                    )
                else:
-                    ds = load_dataset(
-                        d.path,
-                        streaming=False,
-                        use_auth_token=use_auth_token,
+                    raise ValueError(
+                        "unhandled dataset load: local path exists, but is neither a directory or a file"
                    )
+            elif ds_from_hub:
+                ds = load_dataset(
+                    d.path,
+                    name=d.name,
+                    streaming=False,
+                    data_files=d.data_files,
+                    use_auth_token=use_auth_token,
+                )
            else:
                fp = hf_hub_download(
                    repo_id=d.path,
                    repo_type="dataset",
                    filename=d.data_files,
                )
-                ds = load_dataset("json", data_files=fp, streaming=False, split=None)
+                ds = load_dataset(
+                    "json", name=d.name, data_files=fp, streaming=False, split=None
+                )
            if not ds:
                raise ValueError("unhandled dataset load")
            # support for using a subset of the data
@@ -242,25 +266,21 @@ def load_tokenized_prepared_datasets(
                suffix = ""
                if ":load_" in d.type:
                    suffix = f" Did you mean {d.type.replace(':load_', '.load_')}?"
-                logging.error(
-                    f"unhandled prompt tokenization strategy: {d.type}. {suffix}"
-                )
+                LOG.error(f"unhandled prompt tokenization strategy: {d.type}. {suffix}")
                raise ValueError(
                    f"unhandled prompt tokenization strategy: {d.type} {suffix}"
                )
-        logging.info("tokenizing, merging, and shuffling master dataset")
+        LOG.info("merging datasets")
+        dataset = concatenate_datasets(datasets)

-        samples: List[int] = []
-        for d in datasets:
-            samples = samples + list(d)
-        dataset = Dataset.from_list(samples).shuffle(seed=seed)
+        if len(datasets) > 1:
+            LOG.info("shuffle merged datasets")
+            dataset = dataset.shuffle(seed=seed)
        if cfg.local_rank == 0:
-            logging.info(
-                f"Saving merged prepared dataset to disk... {prepared_ds_path}"
-            )
+            LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
            dataset.save_to_disk(prepared_ds_path)
            if cfg.push_dataset_to_hub:
-                logging.info(
+                LOG.info(
                    f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                )
                dataset.push_to_hub(
@@ -311,7 +331,7 @@ def load_prepare_datasets(
        use_auth_token = cfg.hf_use_auth_token
        try:
            if cfg.push_dataset_to_hub:
-                logging.info(
+                LOG.info(
                    f"Checking for packed prepared dataset from hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                )
                dataset = load_dataset(
@@ -325,13 +345,13 @@ def load_prepare_datasets(
        if dataset:
            ...
        elif any(prepared_ds_path.glob("*")):
-            logging.info(
+            LOG.info(
                f"Loading prepared packed dataset from disk at {prepared_ds_path}..."
            )
            dataset = load_from_disk(str(prepared_ds_path))
-            logging.info("Prepared packed dataset loaded from disk...")
+            LOG.info("Prepared packed dataset loaded from disk...")
            if cfg.push_dataset_to_hub:
-                logging.info(
+                LOG.info(
                    f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                )
                dataset.push_to_hub(
@@ -350,17 +370,16 @@ def load_prepare_datasets(
                [dataset],
                seq_length=max_packed_sequence_len,
            )
-            logging.info(
-                f"packing master dataset to len: {cfg.max_packed_sequence_len}"
-            )
+            LOG.info(f"packing master dataset to len: {cfg.max_packed_sequence_len}")
            dataset = Dataset.from_list(list(constant_len_dataset))

            # filter out bad data
+            # TODO convert to dataset.filter(...)
            dataset = Dataset.from_list(
                [
                    d
                    for d in dataset
-                    if len(d["input_ids"]) < cfg.sequence_len
+                    if len(d["input_ids"]) <= cfg.sequence_len
                    and len(d["input_ids"]) > 0
                    and len(d["input_ids"]) == len(d["attention_mask"])
                    and len(d["input_ids"]) == len(d["labels"])
@@ -368,12 +387,12 @@ def load_prepare_datasets(
            )

            if cfg.local_rank == 0:
-                logging.info(
+                LOG.info(
                    f"Saving packed prepared dataset to disk... {prepared_ds_path}"
                )
                dataset.save_to_disk(prepared_ds_path)
                if cfg.push_dataset_to_hub:
-                    logging.info(
+                    LOG.info(
                        f"Saving packed prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"
                    )
                    dataset.push_to_hub(
@@ -386,7 +405,7 @@ def load_prepare_datasets(
        )

    if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None:
-        logging.info(
+        LOG.info(
            f"Using index #{cfg.dataset_shard_idx} of {cfg.dataset_shard_num} shards"
        )
        dataset = dataset.shard(
@@ -394,8 +413,171 @@ def load_prepare_datasets(
            index=cfg.dataset_shard_idx,
        )

-    dataset = dataset.train_test_split(test_size=cfg.val_set_size, shuffle=False)
-    train_dataset = dataset["train"]
-    eval_dataset = dataset["test"]
+    if cfg.val_set_size:
+        # ensure we end up with the same fingerprint by doing rank0 first and being able to cache
+        to_hash_train = (
+            dataset._fingerprint  # pylint: disable=protected-access
+            + "|"
+            + str(cfg.val_set_size)
+            + "|"
+            + "train"
+            + "|"
+            + str(cfg.seed or 42)
+        )
+        to_hash_test = (
+            dataset._fingerprint  # pylint: disable=protected-access
+            + "|"
+            + str(cfg.val_set_size)
+            + "|"
+            + "test"
+            + "|"
+            + str(cfg.seed or 42)
+        )
+        train_fingerprint = hashlib.md5(
+            to_hash_train.encode(), usedforsecurity=False
+        ).hexdigest()
+        test_fingerprint = hashlib.md5(
+            to_hash_test.encode(), usedforsecurity=False
+        ).hexdigest()
+
+        if is_main_process():
+            dataset = dataset.train_test_split(
+                test_size=cfg.val_set_size,
+                shuffle=False,
+                seed=cfg.seed or 42,
+                train_new_fingerprint=train_fingerprint,
+                test_new_fingerprint=test_fingerprint,
+            )
+        barrier()
+        if not is_main_process():
+            dataset = dataset.train_test_split(
+                test_size=cfg.val_set_size,
+                shuffle=False,
+                seed=cfg.seed or 42,
+                train_new_fingerprint=train_fingerprint,
+                test_new_fingerprint=test_fingerprint,
+            )
+        barrier()
+
+        train_dataset = dataset["train"]
+        eval_dataset = dataset["test"]
+    else:
+        train_dataset = dataset
+        eval_dataset = None

    return train_dataset, eval_dataset
+
+
+def encode_pretraining(tokenizer, max_tokens, examples):
+    res = tokenizer(
+        examples["text"],
+        truncation=True,
+        max_length=max_tokens - 2,
+        add_special_tokens=True,
+    )
+    # Convert to PyTorch tensors
+    input_ids = [torch.tensor(seq) for seq in res["input_ids"]]
+    attention_mask = [torch.tensor(seq) for seq in res["attention_mask"]]
+    new_input_ids = []
+    new_attention_mask = []
+    # Append EOS and PAD tokens to input_ids, and correct attention_mask
+    for i, _ in enumerate(input_ids):
+        input_ids[i] = torch.cat(
+            (
+                input_ids[i],
+                torch.tensor([tokenizer.eos_token_id, tokenizer.pad_token_id]),
+            ),
+            dim=0,
+        )
+        attention_mask[i] = torch.cat((attention_mask[i], torch.tensor([1, 0])), dim=0)
+
+    # Concatenate tokens so that their lengths are less than max_tokens
+    buffer_input_ids = torch.tensor([], dtype=torch.long)
+    buffer_attention_mask = torch.tensor([], dtype=torch.long)
+
+    for ids, mask in zip(input_ids, attention_mask):
+        if buffer_input_ids.numel() == max_tokens:
+            new_input_ids.append(buffer_input_ids)
+            new_attention_mask.append(buffer_attention_mask)
+            buffer_input_ids = torch.tensor([], dtype=torch.long)
+            buffer_attention_mask = torch.tensor([], dtype=torch.long)
+            buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
+            buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
+        elif buffer_input_ids.numel() + ids.numel() <= max_tokens:
+            buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
+            buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
+        else:
+            buffer_input_ids = torch.cat(
+                (
+                    buffer_input_ids,
+                    torch.full(
+                        (max_tokens - buffer_input_ids.numel(),),
+                        tokenizer.pad_token_id,
+                        dtype=torch.long,
+                    ),
+                ),
+                dim=0,
+            )
+            buffer_attention_mask = torch.cat(
+                (
+                    buffer_attention_mask,
+                    torch.full(
+                        (max_tokens - buffer_attention_mask.numel(),),
+                        0,
+                        dtype=torch.long,
+                    ),
+                ),
+                dim=0,
+            )
+            new_input_ids.append(buffer_input_ids)
+            new_attention_mask.append(buffer_attention_mask)
+            buffer_input_ids = torch.tensor([], dtype=torch.long)
+            buffer_attention_mask = torch.tensor([], dtype=torch.long)
+
+            buffer_input_ids = torch.cat((buffer_input_ids, ids), dim=0)
+            buffer_attention_mask = torch.cat((buffer_attention_mask, mask), dim=0)
+
+    if buffer_input_ids.numel() > 0:  # for any leftover tokens
+        while buffer_input_ids.numel() < max_tokens:  # make all sequences equal in size
+            buffer_input_ids = torch.cat(
+                (
+                    buffer_input_ids,
+                    torch.full(
+                        (max_tokens - buffer_input_ids.numel(),),
+                        tokenizer.pad_token_id,
+                        dtype=torch.long,
+                    ),
+                ),
+                dim=0,
+            )
+            buffer_attention_mask = torch.cat(
+                (
+                    buffer_attention_mask,
+                    torch.full(
+                        (max_tokens - buffer_attention_mask.numel(),),
+                        0,
+                        dtype=torch.long,
+                    ),
+                ),
+                dim=0,
+            )
+        new_input_ids.append(buffer_input_ids)
+        new_attention_mask.append(buffer_attention_mask)
+
+    ret = {
+        "input_ids": [seq.tolist() for seq in new_input_ids],
+        "labels": [seq.tolist() for seq in new_input_ids],
+        "attention_mask": [seq.tolist() for seq in new_attention_mask],
+    }
+
+    LOG.debug(len(ret["input_ids"]))
+    return ret
+
+
+def load_pretraining_dataset(path, tokenizer, max_tokens=2048, seed=42):
+    encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
+    dataset = load_dataset(path, streaming=True, split="train")
+    dataset = dataset.shuffle(seed=seed, buffer_size=10_000)
+    # TODO dynamically figure out which columns/features to remove
+    dataset = dataset.map(encode, batched=True, remove_columns=["text", "meta"])
+    return dataset
--- a/src/axolotl/utils/dataloader.py
+++ b/src/axolotl/utils/dataloader.py
@@ -0,0 +1,288 @@
+# pylint: skip-file
+import hashlib
+import itertools
+import logging
+import math
+from typing import Any, Callable, List, Union
+
+import numba
+import numpy as np
+from torch.utils.data import DistributedSampler, Sampler
+
+LOG = logging.getLogger("axolotl.utils.dataloader")
+
+
+@numba.njit
+def ffd_check(a: np.ndarray, c: int, n: int):
+    # First-fit-decreasing bin packing
+    # Check if a[] could fit in n bins with capacity c
+    # https://en.wikipedia.org/wiki/First-fit-decreasing_bin_packing
+
+    a = np.sort(a)[::-1]
+    bins = np.full((n,), c, dtype=a.dtype)
+    for size in a:
+        not_found = True
+        for idx in range(n):
+            if bins[idx] >= size:
+                bins[idx] -= size
+                not_found = False
+                break
+
+        if not_found:
+            return False
+
+    return True
+
+
+@numba.njit
+def ffd_with_result(a: np.ndarray, c: int, start_index: int):
+    # First-fit-decreasing bin packing (with result return)
+
+    indices = np.argsort(a)[::-1]
+    a = a[indices]
+
+    bins: List[Any] = []
+    bins_result: List[Any] = []
+    for a_id, size in enumerate(a):
+        add_new = True
+        for idx in range(len(bins)):
+            if bins[idx] >= size:
+                bins[idx] -= size
+                bins_result[idx].append(indices[a_id] + start_index)
+                add_new = False
+                break
+
+        if add_new:
+            bins.append(c - size)
+            bins_result.append([indices[a_id] + start_index])
+
+    return bins_result, len(a)
+
+
+@numba.njit
+def allocate(
+    lengths: np.ndarray, lengths_cumsum: np.ndarray, rank: int, c: int, n: int
+):
+    """
+    :param lengths: array of lengths of each sample
+    :param lengths_cumsum: cumulative sum of consecutive lengths
+    :param rank: rank for this process
+    :param c: length of tokens per batch
+    :param n: number of ranks
+    :return:
+    """
+    # Dynamic batch allocator, similar to Multifit
+    # https://en.wikipedia.org/wiki/Multifit_algorithm
+    # ~99.5% efficiency on OpenChat training set (12 * 2048 ctx len)
+
+    s = 0
+    start_index = 0
+    result = []
+    result_totseqs = []
+
+    while True:
+        # binary search [left, right)
+        left = 1
+        right = 1 + np.searchsorted(lengths_cumsum[start_index:], s + c * n, "right")
+
+        while right - left > 1:
+            mid = (left + right) // 2
+            if ffd_check(lengths[start_index : start_index + mid], c, n):
+                left = mid
+            else:
+                right = mid
+
+        # use length left
+        batch, tot_seqs = ffd_with_result(
+            lengths[start_index : start_index + left], c, start_index
+        )
+        if len(batch) < n:
+            break
+
+        start_index += left
+        s = lengths_cumsum[start_index - 1]
+
+        # add local rank
+        result.append(batch[rank])
+        # add total seqs for all ranks
+        result_totseqs.append(tot_seqs)
+        # yield batch[rank], tot_seqs, s, len(result) * c * n
+    return result, result_totseqs, s, len(result) * c * n
+
+
+def chunk(iterable, n):
+    """
+    Chunk data into tuples of length n
+    """
+    # batched('ABCDEFG', 3) --> ABC DEF G
+    if n < 1:
+        raise ValueError("n must be at least one")
+    it = iter(iterable)
+    while batch := tuple(itertools.islice(it, n)):
+        yield batch
+
+
+def hash_indices(lst: List[int]) -> str:
+    # Convert the list of integers to a string representation
+    concatenated = ",".join(map(str, lst))
+
+    # Generate the hash
+    sha256 = hashlib.sha256()
+    sha256.update(concatenated.encode())
+
+    return sha256.hexdigest()
+
+
+class MultipackDistributedDataloader:
+    """Unpadded data loading using Multipack.
+    Adapted from https://github.com/imoneoi/openchat/blob/v3_fix_mle_loss/ochat/training_deepspeed/multipack_dataloader.py
+    Approximate (at most ~1.22x) the optimal solution of the identical-machines scheduling problem, which is NP-hard.
+    """
+
+    def __init__(
+        self,
+        dataset: Any,
+        collate_fn: Callable,
+        seq_max_length: int = 2048,
+        batch_size: int = 1,
+        sampler: Union[Sampler, DistributedSampler] = None,
+        packing_efficiency_estimate: float = 1.0,
+        sample_packing_seq_len_multiplier: int = 1,
+        device_count: int = 1,
+    ):
+        # Dataset
+        self.dataset = dataset
+        self.lengths = (
+            dataset.data.column("position_ids")
+            .to_pandas()
+            .apply(lambda x: x[-1] + 1)
+            .values
+        )
+        assert isinstance(self.lengths, np.ndarray)
+        assert batch_size % sample_packing_seq_len_multiplier == 0
+        assert batch_size >= sample_packing_seq_len_multiplier
+        self.sampler = sampler
+        self.batch_size = batch_size
+        self.sample_packing_seq_len_multiplier = sample_packing_seq_len_multiplier
+        self.seq_max_length = seq_max_length
+        self.batch_max_length = batch_size * seq_max_length
+        self.collate_fn = collate_fn
+
+        self.num_replicas = 1
+        self.rank = 0
+
+        # statistics
+        self.eff_total_used = 0
+        self.eff_total_slots = 0
+        self.packing_efficiency_estimate = packing_efficiency_estimate or 1.0
+        self.device_count = device_count
+
+    def generate_batches(self, set_stats=False):
+        LOG.info("generating packed batches")
+        if self.sampler:
+            indices = [idx for idx in self.sampler]
+        else:
+            indices = range(0, len(self.dataset))
+
+        LOG.info(hash_indices(indices))
+        lengths = self.lengths[indices]
+        lengths_cumsum = np.cumsum(lengths)
+
+        batches, totseqs, total_used, total_slots = allocate(
+            lengths=lengths,
+            lengths_cumsum=lengths_cumsum,
+            rank=self.rank,
+            # c=self.batch_max_length,
+            c=self.seq_max_length * self.sample_packing_seq_len_multiplier,
+            n=self.num_replicas,
+        )
+
+        batches = [[indices[b_idx] for b_idx in batch] for batch in batches]
+
+        # statistics
+        if set_stats:
+            self.eff_total_used += total_used
+            self.eff_total_slots += total_slots
+
+        return batches, totseqs
+
+    def __iter__(self):
+        if hasattr(self.sampler, "set_epoch"):
+            new_epoch = self.sampler.epoch + 1
+            self.sampler.set_epoch(new_epoch)
+            LOG.info(f"calling sampler.set_epoch({new_epoch})")
+        all_batches, _ = self.generate_batches(set_stats=True)
+        features = self.dataset.features.keys()
+        len_remaining = self._len_est()
+        for batches in chunk(
+            all_batches, self.batch_size // self.sample_packing_seq_len_multiplier
+        ):
+            chunked_data = []
+            attn_mask_cum_idx = 0
+            for batch in batches:
+                concatenated = {}
+                batched_data = [self.dataset[batch_idx] for batch_idx in batch]
+                for feature in features:
+                    if feature == "attention_mask":
+                        arrays = [
+                            (attn_mask_cum_idx + idx + 1) * np.array(item[feature])
+                            for idx, item in enumerate(batched_data)
+                            if feature in item
+                        ]
+                        attn_mask_cum_idx += len(batched_data)
+                        concatenated[feature] = np.concatenate(arrays)
+                    else:
+                        arrays = [
+                            np.array(item[feature])
+                            for item in batched_data
+                            if feature in item
+                        ]
+                        concatenated[feature] = np.concatenate(arrays)
+                chunked_data.append(concatenated)
+            yield self.collate_fn(chunked_data)
+            len_remaining -= 1
+            if not len_remaining:
+                return
+
+    def _len_est(self):
+        lengths_sum = np.sum(self.lengths)
+        lengths_sum_per_device = lengths_sum // self.device_count
+        LOG.info(
+            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
+            f"total_num_tokens per device: {lengths_sum_per_device}"
+        )
+
+        # shave off 1% + 1 for dealing with variance in packing from random sampler to sampler
+        return (
+            math.floor(
+                0.99
+                * lengths_sum_per_device
+                / self.packing_efficiency_estimate
+                // self.seq_max_length
+                // self.batch_size
+            )
+            - 1
+        )
+
+    def __len__(self):
+        # this doesn't return the actual length b/c with distributed samplers, not all dataloaders get
+        # the same share of total tokens
+        # if not self.eff_total_used:
+        #     batches, _ = self.generate_batches(set_stats=True)
+        # LOG.info(
+        #     f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
+        #     f"actual packing efficiency: {self.efficiency()}"
+        # )
+        return max(1, self._len_est())
+
+    def len_w_stats(self):
+        if not self.eff_total_used:
+            batches, _ = self.generate_batches(set_stats=True)
+        LOG.info(
+            f"packing_efficiency_estimate: {self.packing_efficiency_estimate} "
+            f"actual packing efficiency: {self.efficiency()}"
+        )
+        return max(1, self._len_est())
+
+    def efficiency(self):
+        return self.eff_total_used / self.eff_total_slots
--- a/src/axolotl/utils/distributed.py
+++ b/src/axolotl/utils/distributed.py
@@ -0,0 +1,41 @@
+"""
+utility helpers for distributed checks
+"""
+import torch.distributed as dist
+from accelerate import Accelerator
+
+accelerate = None  # pylint: disable=invalid-name
+
+
+def load_accelerate():
+    global accelerate  # pylint: disable=global-statement
+    accelerate = Accelerator()
+
+
+def is_distributed():
+    """
+    Check if distributed training is initialized.
+    """
+    global accelerate  # pylint: disable=global-statement
+    if not accelerate:
+        accelerate = Accelerator()
+    return dist.is_available() and dist.is_initialized()
+
+
+def barrier():
+    """
+    Acts as a barrier to wait for all processes. This ensures that all processes
+    reach the barrier before proceeding further.
+    """
+    if is_distributed():
+        dist.barrier()
+
+
+def is_main_process():
+    """
+    Check if the current process is the main process.
+    If not in distributed mode, always return True.
+    """
+    if not is_distributed():
+        return True
+    return dist.get_rank() == 0
--- a/src/axolotl/utils/models.py
+++ b/src/axolotl/utils/models.py
@@ -10,16 +10,21 @@ from typing import TYPE_CHECKING, Optional, Tuple  # noqa: F401
 import bitsandbytes as bnb
 import torch
 import transformers
-from transformers import PreTrainedModel  # noqa: F401
+from optimum.bettertransformer import BetterTransformer
 from transformers import (  # noqa: F401
    AutoConfig,
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    LlamaConfig,
+    PreTrainedModel,
+    PreTrainedTokenizerBase,
 )

 from axolotl.prompt_tokenizers import LLAMA_DEFAULT_PAD_TOKEN
+from axolotl.utils.bench import log_gpu_memory_usage
+
+LOG = logging.getLogger("axolotl")

 if TYPE_CHECKING:
    from peft import PeftConfig  # noqa: F401
@@ -32,21 +37,32 @@ def load_tokenizer(
    tokenizer_type,
    cfg,
 ):
+    tokenizer_kwargs = {}
+    use_fast = True  # this is the default
+    if cfg.tokenizer_use_fast is not None:
+        use_fast = cfg.tokenizer_use_fast
+    if cfg.tokenizer_legacy is not None:
+        # True is the default w/ https://github.com/huggingface/transformers/pull/25224
+        tokenizer_kwargs["legacy"] = cfg.tokenizer_legacy
    if tokenizer_type:
        tokenizer = getattr(transformers, tokenizer_type).from_pretrained(
            tokenizer_config,
            trust_remote_code=cfg.trust_remote_code or False,
+            use_fast=use_fast,
+            **tokenizer_kwargs,
        )
    else:
        tokenizer = AutoTokenizer.from_pretrained(
            tokenizer_config,
            trust_remote_code=cfg.trust_remote_code or False,
+            use_fast=use_fast,
+            **tokenizer_kwargs,
        )

-    logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
-    logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
-    logging.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
-    logging.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
+    LOG.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
+    LOG.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
+    LOG.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
+    LOG.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")

    if tokenizer.__class__.__name__ in [
        "LlamaTokenizer",
@@ -68,38 +84,45 @@ def load_tokenizer(


 def load_model(
-    base_model, base_model_config, model_type, tokenizer, cfg, adapter="lora"
-):
-    # type: (str, str, str, AutoTokenizer, DictDefault, Optional[str]) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
+    cfg, tokenizer
+):  # type: (DictDefault, PreTrainedTokenizerBase) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
    """
-    Load a model from a base model and a model type.
+    Load a model for a given configuration and tokenizer.
    """
+    base_model = cfg.base_model
+    base_model_config = cfg.base_model_config
+    model_type = cfg.model_type
+    adapter = cfg.adapter

    # TODO refactor as a kwarg
    load_in_8bit = cfg.load_in_8bit
-    cfg.is_llama_derived_model = "llama" in base_model or (
-        cfg.model_type and "llama" in cfg.model_type.lower()
+    cfg.is_llama_derived_model = (
+        "llama" in base_model
+        or (cfg.model_type and "llama" in cfg.model_type.lower())
+        or cfg.is_llama_derived_model
    )

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

-            logging.info("patching with flash attention")
+            LOG.info("patching with flash attention")
            replace_llama_attn_with_flash_attn()
    elif cfg.is_llama_derived_model and cfg.xformers_attention:
        from axolotl.monkeypatch.llama_attn_hijack_xformers import (
            hijack_llama_attention,
        )

-        logging.info("patching with xformers attention")
+        LOG.info("patching with xformers attention")
        hijack_llama_attention()
    elif cfg.is_llama_derived_model and cfg.sdp_attention:
        from axolotl.monkeypatch.llama_attn_hijack_xformers import (
            hijack_llama_sdp_attention,
        )

-        logging.info("patching with sdp attention")
+        LOG.info("patching with sdp attention")
        hijack_llama_sdp_attention()
    elif cfg.is_llama_derived_model and cfg.landmark_attention:
        from axolotl.monkeypatch.llama_landmark_attn import (
@@ -107,7 +130,7 @@ def load_model(
            patch_llama_with_landmark_attn,
        )

-        logging.info("patching with landmark attention")
+        LOG.info("patching with landmark attention")
        patch_llama_with_landmark_attn()

        # Note: This might overwrite previous additional_special_tokens
@@ -118,12 +141,20 @@ def load_model(
            replace_llama_rope_with_xpos_rope,
        )

-        logging.info("patching with xpos rope")
+        LOG.info("patching with xpos rope")
        replace_llama_rope_with_xpos_rope()

-    if cfg.bf16:
+    if cfg.is_llama_derived_model and (
+        cfg.max_packed_sequence_len or cfg.sample_packing
+    ):
+        from axolotl.monkeypatch.llama_expand_mask import hijack_expand_mask
+
+        LOG.info("patching _expand_mask")
+        hijack_expand_mask()
+
+    if cfg.bf16 or cfg.bfloat16:
        torch_dtype = torch.bfloat16
-    elif cfg.load_in_8bit or cfg.fp16:
+    elif cfg.load_in_8bit or cfg.fp16 or cfg.float16:
        torch_dtype = torch.float16
    else:
        torch_dtype = torch.float32
@@ -135,18 +166,24 @@ def load_model(

            replace_peft_model_with_int4_lora_model()
    except Exception as err:
-        logging.exception(err)
+        LOG.exception(err)
        raise err

-    try:
-        from peft import prepare_model_for_kbit_training
-    except ImportError:
-        # For backward compatibility
-        from peft import (
-            prepare_model_for_int8_training as prepare_model_for_kbit_training,
-        )
+    if not cfg.gptq and (
+        (cfg.adapter == "lora" and load_in_8bit)
+        or (cfg.adapter == "qlora" and cfg.load_in_4bit)
+    ):
+        try:
+            from peft import prepare_model_for_kbit_training
+        except ImportError:
+            # For backward compatibility
+            from peft import (
+                prepare_model_for_int8_training as prepare_model_for_kbit_training,
+            )

    model_kwargs = {}
+    if cfg.model_revision:
+        model_kwargs["revision"] = cfg.model_revision
    if cfg.adapter == "qlora" and cfg.load_in_4bit:
        model_kwargs["quantization_config"] = BitsAndBytesConfig(
            load_in_4bit=True,
@@ -178,7 +215,7 @@ def load_model(
                if len(files) > 0:
                    model_path = str(files[0])
                else:
-                    logging.warning(
+                    LOG.warning(
                        "unable to find a cached model file, this will likely fail..."
                    )
                    model_path = str(cache_model_path)
@@ -195,17 +232,18 @@ def load_model(
                else True,
            )
            load_in_8bit = False
-        elif cfg.is_llama_derived_model:
+        elif cfg.is_llama_derived_model and not cfg.trust_remote_code:
            from transformers import LlamaForCausalLM

-            config = LlamaConfig.from_pretrained(base_model_config)
+            config = LlamaConfig.from_pretrained(
+                base_model_config, rope_scaling=cfg.rope_scaling
+            )
            model = LlamaForCausalLM.from_pretrained(
                base_model,
                config=config,
                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=torch_dtype,
-                device_map="auto" if cfg.world_size == 1 else cfg.device_map,
                **model_kwargs,
            )
        # elif model_type == "GPTNeoXForCausalLM" and cfg.flash_attention:
@@ -234,13 +272,12 @@ def load_model(
        #         device=cfg.device,
        #     )
        #     model.train() # sets to train instead of eval mode
-        elif model_type:
+        elif model_type and not cfg.trust_remote_code:
            model = getattr(transformers, model_type).from_pretrained(
                base_model,
                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=torch_dtype,
-                device_map=cfg.device_map,
                trust_remote_code=cfg.trust_remote_code or False,
                **model_kwargs,
            )
@@ -251,51 +288,82 @@ def load_model(
            )
            # Shouldn't be a problem most of the time. will obviously error if the model doesn't support this
            # when training starts
-            if hasattr(config, "max_seq_len") and cfg.sequence_len > config.max_seq_len:
+            if (
+                hasattr(config, "max_seq_len")
+                and config.max_seq_len
+                and cfg.sequence_len > config.max_seq_len
+            ):
                config.max_seq_len = cfg.sequence_len
-                logging.warning(f"increasing context length to {cfg.sequence_len}")
+                LOG.warning(f"increasing context length to {cfg.sequence_len}")
            elif (
                hasattr(config, "max_sequence_length")
+                and config.max_sequence_length
                and cfg.sequence_len > config.max_sequence_length
            ):
                config.max_sequence_length = cfg.sequence_len
-                logging.warning(f"increasing context length to {cfg.sequence_len}")
+                LOG.warning(f"increasing context length to {cfg.sequence_len}")
            model = AutoModelForCausalLM.from_pretrained(
                base_model,
                config=config,
                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=torch_dtype,
-                device_map=cfg.device_map,
                trust_remote_code=cfg.trust_remote_code or False,
                **model_kwargs,
            )
    except Exception as err:  # pylint: disable=broad-exception-caught
-        logging.error(
+        LOG.error(
            "Exception raised attempting to load model, retrying with AutoModelForCausalLM"
        )
-        logging.exception(err)
+        LOG.exception(err)
        model = AutoModelForCausalLM.from_pretrained(
            base_model,
            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=torch_dtype,
-            device_map=cfg.device_map,
            trust_remote_code=cfg.trust_remote_code or False,
            **model_kwargs,
        )

-    embeddings_len = math.ceil(len(tokenizer) / 32) * 32
+    embeddings_len = (
+        math.ceil(len(tokenizer) / 32) * 32
+        if cfg.resize_token_embeddings_to_32x
+        else len(tokenizer)
+    )
    model.resize_token_embeddings(embeddings_len)

+    if (
+        hasattr(model.config, "max_position_embeddings")
+        and model.config.max_position_embeddings
+        and cfg.sequence_len >= model.config.max_position_embeddings
+    ):
+        LOG.warning(
+            f"increasing model.config.max_position_embeddings to {cfg.sequence_len}"
+        )
+        model.config.max_position_embeddings = cfg.sequence_len
+
+    if model.device.type == "cuda":
+        log_gpu_memory_usage(LOG, "after model load", model.device)
+
    if not cfg.gptq and (
        (cfg.adapter == "lora" and load_in_8bit)
        or (cfg.adapter == "qlora" and cfg.load_in_4bit)
    ):
-        logging.info("converting PEFT model w/ prepare_model_for_kbit_training")
+        LOG.info("converting PEFT model w/ prepare_model_for_kbit_training")
        model = prepare_model_for_kbit_training(
            model, use_gradient_checkpointing=cfg.gradient_checkpointing
        )

+        # LlamaRMSNorm layers are in fp32 after kbit_training, so we need to
+        # convert them back to fp16/bf16 for flash-attn compatibility.
+        if cfg.flash_attention and cfg.is_llama_derived_model:
+            for name, module in model.named_modules():
+                if "norm" in name:
+                    module.to(torch_dtype)
+                if "lm_head" in name or "embed_tokens" in name:
+                    if hasattr(module, "weight"):
+                        module.to(torch_dtype)
+
    model, lora_config = load_adapter(model, cfg, adapter)

    if cfg.ddp and not load_in_8bit:
@@ -303,7 +371,7 @@ def load_model(

    if cfg.gptq:
        # Scales to half
-        logging.info("Fitting 4bit scales and zeros to half")
+        LOG.info("Fitting 4bit scales and zeros to half")
        for _, module in model.named_modules():
            if "Autograd4bitQuantLinear" in str(type(module)) or "Linear4bitLt" in str(
                type(module)
@@ -313,6 +381,9 @@ def load_model(
                module.scales = module.scales.half()
                module.bias = module.bias.half()

+    if model.device.type == "cuda":
+        log_gpu_memory_usage(LOG, "after adapters", model.device)
+
    if (
        torch.cuda.device_count() > 1
        and int(os.getenv("WORLD_SIZE", "1")) > 1
@@ -329,9 +400,12 @@ def load_model(
        if param.requires_grad:
            requires_grad.append(f"{name}: {param.requires_grad}")
    if len(requires_grad) == 0:
-        logging.warning("there are no parameters that require gradient updates")
+        LOG.warning("there are no parameters that require gradient updates")
    model.config.use_cache = False

+    if cfg.flash_optimum:
+        model = BetterTransformer.transform(model)
+
    # TODO resume_from_checkpoint handling
    return model, lora_config

@@ -341,6 +415,8 @@ def load_adapter(model, cfg, adapter):

    if adapter is None:
        return model, None
+    if hasattr(model, "enable_input_require_grads"):
+        model.enable_input_require_grads()
    if adapter in ["lora", "qlora"]:
        return load_lora(model, cfg)
    if adapter == "llama-adapter":
@@ -360,7 +436,7 @@ def load_llama_adapter(model, cfg):
    )

    if cfg.lora_model_dir:
-        logging.info("Loading pretained LORA")
+        LOG.info("Loading pretained LORA")
        model = PeftModel.from_pretrained(
            model,
            cfg.lora_model_dir,
@@ -407,7 +483,7 @@ def load_lora(model, cfg):
            bits = 8

        linear_names = find_all_linear_names(bits, model)
-        logging.info(f"found linear modules: {repr(linear_names)}")
+        LOG.info(f"found linear modules: {repr(linear_names)}")
        lora_target_modules = list(set(lora_target_modules + linear_names))

    lora_config = LoraConfig(
--- a/src/axolotl/utils/schedulers.py
+++ b/src/axolotl/utils/schedulers.py
@@ -1,6 +1,9 @@
 """Module for custom LRScheduler class"""
+import math
+from functools import partial

-from torch.optim.lr_scheduler import LRScheduler
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LambdaLR, LRScheduler


 class InterpolatingLogScheduler(LRScheduler):
@@ -42,3 +45,58 @@ class InterpolatingLogScheduler(LRScheduler):
            lrs = [self.max_lr for base_lr in self.base_lrs]

        return lrs
+
+
+def _get_cosine_schedule_with_quadratic_warmup_lr_lambda(
+    current_step: int,
+    *,
+    num_warmup_steps: int,
+    num_training_steps: int,
+    num_cycles: float
+):
+    if current_step < num_warmup_steps:
+        return (float(current_step) / float(max(1, num_warmup_steps))) ** 2
+    progress = float(current_step - num_warmup_steps) / float(
+        max(1, num_training_steps - num_warmup_steps)
+    )
+    return max(
+        0.0, 0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress))
+    )
+
+
+def get_cosine_schedule_with_quadratic_warmup(
+    optimizer: Optimizer,
+    num_warmup_steps: int,
+    num_training_steps: int,
+    num_cycles: float = 0.5,
+    last_epoch: int = -1,
+):
+    """
+    Create a schedule with a learning rate that decreases following the values of the cosine function between the
+    initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
+    initial lr set in the optimizer.
+
+    Args:
+        optimizer ([`~torch.optim.Optimizer`]):
+            The optimizer for which to schedule the learning rate.
+        num_warmup_steps (`int`):
+            The number of steps for the warmup phase.
+        num_training_steps (`int`):
+            The total number of training steps.
+        num_cycles (`float`, *optional*, defaults to 0.5):
+            The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
+            following a half-cosine).
+        last_epoch (`int`, *optional*, defaults to -1):
+            The index of the last epoch when resuming training.
+
+    Return:
+        `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
+    """
+
+    lr_lambda = partial(
+        _get_cosine_schedule_with_quadratic_warmup_lr_lambda,
+        num_warmup_steps=num_warmup_steps,
+        num_training_steps=num_training_steps,
+        num_cycles=num_cycles,
+    )
+    return LambdaLR(optimizer, lr_lambda, last_epoch)
--- a/src/axolotl/utils/tokenization.py
+++ b/src/axolotl/utils/tokenization.py
@@ -5,6 +5,8 @@ import logging

 from termcolor import colored

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

 def check_dataset_labels(dataset, tokenizer):
    # the dataset is already shuffled, so let's just check the first 5 elements
@@ -32,5 +34,7 @@ def check_example_labels(example, tokenizer):
        )
        colored_tokens.append(colored_token)

-    logging.info(" ".join(colored_tokens))
-    logging.info("\n\n\n")
+    LOG.info(" ".join(colored_tokens))
+    LOG.info("\n\n\n")
+
+    return " ".join(colored_tokens)
--- a/src/axolotl/utils/trainer.py
+++ b/src/axolotl/utils/trainer.py
@@ -1,26 +1,227 @@
 """Module containing the Trainer class and related functions"""
-
 import importlib
 import logging
 import math
 import os
 import sys
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from functools import partial
 from pathlib import Path
-from typing import Optional
+from typing import Optional, Union

 import bitsandbytes as bnb
+import numpy as np
 import torch.cuda
 import transformers
+from datasets import Dataset, set_caching_enabled
 from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
-from transformers import EarlyStoppingCallback, Trainer
+from torch.utils.data import DataLoader, DistributedSampler, RandomSampler
+from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
 from transformers.trainer_pt_utils import get_parameter_names

-from axolotl.utils.callbacks import SavePeftModelCallback
-from axolotl.utils.schedulers import InterpolatingLogScheduler
+from axolotl.monkeypatch.relora import ReLoRACallback, ReLoRAScheduler
+from axolotl.utils.callbacks import (
+    PrintGPUStatsCallback,
+    SaveBetterTransformerModelCallback,
+    SavePeftModelCallback,
+)
+from axolotl.utils.collators import DataCollatorForSeq2Seq
+from axolotl.utils.dataloader import MultipackDistributedDataloader
+from axolotl.utils.schedulers import (
+    InterpolatingLogScheduler,
+    get_cosine_schedule_with_quadratic_warmup,
+)
+
+LOG = logging.getLogger("axolotl")


-class OneCycleLRSchedulerTrainer(Trainer):
+@torch.jit.script
+def weighted_cross_entropy(
+    logits: torch.Tensor, labels: torch.Tensor, weights: torch.Tensor
+):
+    # Flatten the logits, labels, and weights tensors
+    logits = logits.view(
+        -1, logits.size(-1)
+    )  # logits becomes of shape [batch_size*sequence_length, vocab_size]
+    labels = labels.view(-1)  # labels becomes of shape [batch_size*sequence_length]
+    weights = weights.view(-1)  # weights becomes of shape [batch_size*sequence_length]
+
+    # Compute the unweighted cross entropy loss
+    losses = torch.nn.functional.cross_entropy(logits, labels, reduction="none")
+
+    # Apply the weights to the losses and compute their sum
+    return (weights * losses).sum()
+
+
+@torch.jit.script
+def create_weighted_mask(labels: torch.Tensor):
+    # Check if the tensor is 2D. If not, unsqueeze it to make it 2D
+    if len(labels.shape) == 1:
+        labels = labels.unsqueeze(0)
+
+    weights = torch.zeros_like(labels).float()
+    for i in range(labels.shape[0]):
+        mask = labels[i] != -100
+
+        # Create a tensor to track group ids
+        group_ids = torch.zeros_like(labels[i]).int()
+        curr_group_id = 0
+
+        for j in range(1, len(labels[i])):
+            if mask[j] and not mask[j - 1]:  # switch from masked to unmasked label
+                curr_group_id += 1  # start new group
+            group_ids[j] = (
+                curr_group_id if mask[j] else 0
+            )  # assign group id if unmasked label
+
+        # Count only unmasked labels in each group
+        group_counts = torch.bincount(group_ids[mask])
+
+        mask_weights = torch.zeros_like(labels[i]).float()
+        mask_weights[mask] = 1.0 / group_counts[group_ids[mask]]
+
+        weights[i] = mask_weights
+
+    return weights.squeeze()  # squeeze the output to match the input dimension
+
+
+def trainer_weighted_loss(model_output, labels, shift_labels=True):
+    logits = (
+        model_output["logits"] if isinstance(model_output, dict) else model_output[0]
+    )
+    if shift_labels:
+        logits = logits[..., :-1, :].contiguous()
+        labels = labels[..., 1:].contiguous()
+
+    weights = create_weighted_mask(labels)
+    return weighted_cross_entropy(logits, labels, weights)
+
+
+@dataclass
+class AxolotlTrainingArguments(TrainingArguments):
+    """
+    Extend the base TrainingArguments for axolotl helpers
+    """
+
+    lr_quadratic_warmup: bool = field(
+        default=False,
+        metadata={"help": "Use quadratic warmup for cosine scheduling."},
+    )
+    sample_packing: bool = field(
+        default=False,
+        metadata={"help": "Use sample packing for efficient training."},
+    )
+    sample_packing_efficiency: float = field(
+        default=1.0,
+        metadata={"help": "Sample packing efficiency for calculating batch length."},
+    )
+    max_seq_length: int = field(
+        default=2048,
+        metadata={"help": "The maximum sequence length the model can handle"},
+    )
+    sample_packing_seq_len_multiplier: int = field(
+        default=1,
+        metadata={"help": "the multiplier for the max len for packed sequences"},
+    )
+
+
+class AxolotlTrainer(Trainer):
+    """
+    Extend the base Trainer for axolotl helpers
+    """
+
+    args = None  # type: AxolotlTrainingArguments
+
+    def create_scheduler(
+        self, num_training_steps: int, optimizer: torch.optim.Optimizer = None
+    ):
+        """
+        Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
+        passed as an argument.
+
+        Args:
+            num_training_steps (int): The number of training steps to do.
+            optimizer (torch.optim.Optimizer): The training optimizer
+        """
+
+        # fmt: off
+        if self.lr_scheduler is None:  # type: ignore  # pylint: disable=access-member-before-definition
+            # fmt: on
+            if (
+                self.args.lr_scheduler_type == "cosine"
+                and self.args.lr_quadratic_warmup is True
+            ):
+                self.lr_scheduler = get_cosine_schedule_with_quadratic_warmup(  # pylint: disable=attribute-defined-outside-init
+                    optimizer,
+                    num_warmup_steps=self.args.get_warmup_steps(num_training_steps),
+                    num_training_steps=num_training_steps,
+                )
+            else:
+                return super().create_scheduler(num_training_steps, optimizer)
+        return self.lr_scheduler
+
+    def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
+        if self.args.world_size > 1 and self.args.sample_packing:
+            return DistributedSampler(
+                self.train_dataset,
+                num_replicas=self.args.world_size,
+                rank=self.args.process_index,
+                seed=self.args.seed,
+            )
+        return super()._get_train_sampler()
+
+    def get_train_dataloader(self) -> Union[DataLoader, MultipackDistributedDataloader]:
+        if self.args.sample_packing:
+            train_sampler = self._get_train_sampler()
+            return self.accelerator.prepare(
+                MultipackDistributedDataloader(
+                    self.train_dataset,
+                    batch_size=self._train_batch_size,
+                    seq_max_length=self.args.max_seq_length,
+                    collate_fn=self.data_collator,
+                    sampler=train_sampler,
+                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                    sample_packing_seq_len_multiplier=self.args.sample_packing_seq_len_multiplier,
+                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                )
+            )
+        return super().get_train_dataloader()
+
+    def get_eval_dataloader(
+        self, eval_dataset: Optional[Dataset] = None
+    ) -> Union[DataLoader, MultipackDistributedDataloader]:
+        if self.args.sample_packing:
+            eval_dataset = (
+                eval_dataset if eval_dataset is not None else self.eval_dataset
+            )
+            eval_sampler = self._get_eval_sampler(eval_dataset)
+            return self.accelerator.prepare(
+                MultipackDistributedDataloader(
+                    eval_dataset,
+                    batch_size=self.args.eval_batch_size,
+                    seq_max_length=self.args.max_seq_length,
+                    collate_fn=self.data_collator,
+                    sampler=eval_sampler,
+                    packing_efficiency_estimate=self.args.sample_packing_efficiency,
+                    sample_packing_seq_len_multiplier=self.args.eval_batch_size,
+                    device_count=int(os.environ.get("WORLD_SIZE", 1)),
+                )
+            )
+        return super().get_eval_dataloader(eval_dataset)
+
+    def compute_loss(self, model, inputs, return_outputs=False):
+        # use one's weighted cross entropy loss calc
+        # if self.args.sample_packing:
+        #     labels = inputs.pop("labels")
+        #     outputs = model(**inputs)
+        #     loss = trainer_weighted_loss(outputs, labels, shift_labels=True)
+        #     return (loss, outputs) if return_outputs else loss
+        return super().compute_loss(model, inputs, return_outputs=return_outputs)
+
+
+class OneCycleLRSchedulerTrainer(AxolotlTrainer):
    """
    Trainer subclass that uses the OneCycleLR scheduler
    """
@@ -49,10 +250,121 @@ class OneCycleLRSchedulerTrainer(Trainer):
        return self.lr_scheduler


-def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
-    total_num_steps = int(
-        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
-    )
+def add_position_ids(sample):
+    sample["position_ids"] = torch.arange(len(sample["input_ids"]))
+    return sample
+
+
+def drop_long_seq(sample, sequence_len=2048):
+    return len(sample["input_ids"]) <= sequence_len
+
+
+@contextmanager
+def disable_datasets_caching():
+    try:
+        set_caching_enabled(False)
+        yield
+    finally:
+        set_caching_enabled(True)
+
+
+def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
+    if cfg.sample_packing:
+        drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
+        train_dataset = train_dataset.filter(drop_long, num_proc=os.cpu_count()).map(
+            add_position_ids, num_proc=os.cpu_count()
+        )
+        if eval_dataset:
+            eval_dataset = eval_dataset.filter(drop_long, num_proc=os.cpu_count()).map(
+                add_position_ids, num_proc=os.cpu_count()
+            )
+    return train_dataset, eval_dataset
+
+
+def calculate_total_num_steps(cfg, train_dataset, tokenizer):
+    if cfg.sample_packing:
+        # we have to drop anything longer then sequence len otherwise
+        # flash attention with position ids fails
+        if not cfg.total_num_tokens:
+            LOG.info("calculating total_num_tokens")
+            total_num_tokens = np.sum(
+                train_dataset.data.column("input_ids")
+                .to_pandas()
+                .apply(lambda x: len(x))  # pylint: disable=unnecessary-lambda
+                .values
+            )
+            LOG.info(f"📝 UPDATE CONFIG WITH: `total_num_tokens: {total_num_tokens}`")
+            cfg.total_num_tokens = total_num_tokens
+
+        if cfg.sample_packing_eff_est:
+            total_num_steps = (
+                # match count to len est in dataloader
+                (
+                    math.floor(
+                        0.99
+                        * cfg.total_num_tokens
+                        / cfg.sample_packing_eff_est
+                        / cfg.sequence_len
+                        // cfg.batch_size
+                        // int(os.environ.get("WORLD_SIZE", 1))
+                    )
+                    - 1
+                )
+                * cfg.num_epochs
+            )
+            LOG.info(
+                f"total_num_tokens: {cfg.total_num_tokens}, total_num_steps: {total_num_steps}"
+            )
+        else:
+            sampler = RandomSampler(train_dataset)
+            data_loader = MultipackDistributedDataloader(
+                train_dataset,
+                batch_size=cfg.micro_batch_size,
+                seq_max_length=cfg.max_packed_sequence_len or cfg.sequence_len,
+                collate_fn=DataCollatorForSeq2Seq(
+                    tokenizer,
+                    return_tensors="pt",
+                    padding="longest",
+                ),
+                sampler=sampler,
+                packing_efficiency_estimate=cfg.sample_packing_eff_est,
+                sample_packing_seq_len_multiplier=cfg.micro_batch_size,
+                device_count=int(os.environ.get("WORLD_SIZE", 1)),
+            )
+            data_loader_len = data_loader.len_w_stats()
+            actual_eff = data_loader.efficiency()
+            LOG.info(f"data_loader_len: {data_loader_len}")
+            total_num_steps = int(
+                math.floor(
+                    data_loader_len
+                    * cfg.micro_batch_size
+                    * cfg.num_epochs
+                    // cfg.batch_size
+                )
+            )
+            LOG.info(
+                f"📝 UPDATE CONFIG WITH: `sample_packing_eff_est: {math.ceil(actual_eff * 100.0) / 100.0}`"
+            )
+            cfg.sample_packing_eff_est = math.ceil(actual_eff * 100.0) / 100.0
+    else:
+        total_num_steps = int(
+            math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+        )
+    LOG.info(f"total_num_steps: {total_num_steps}")
+    return total_num_steps
+
+
+def setup_fsdp_envs(cfg):
+    os.environ["ACCELERATE_USE_FSDP"] = "true"
+    if cfg.fsdp_config.fsdp_sync_module_states:
+        os.environ["FSDP_SYNC_MODULE_STATES"] = "true"
+    if cfg.fsdp_config.fsdp_state_dict_type:
+        os.environ["FSDP_STATE_DICT_TYPE"] = cfg.fsdp_config.fsdp_state_dict_type
+
+
+def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
+    if cfg.fsdp:
+        setup_fsdp_envs(cfg)
    warmup_steps = (
        cfg.warmup_steps
        if cfg.warmup_steps is not None
@@ -100,6 +412,9 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
        if cfg.fsdp_config:
            training_arguments_kwargs["fsdp_config"] = dict(cfg.fsdp_config)

+    if cfg.lr_quadratic_warmup is not None:
+        training_arguments_kwargs["lr_quadratic_warmup"] = cfg.lr_quadratic_warmup
+
    # deepspeed
    if (
        os.environ.get("ACCELERATE_USE_DEEPSPEED") == "true"
@@ -112,7 +427,31 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
            # TODO search Path("./") for one
            training_arguments_kwargs["deepspeed"] = "./ds_config.json"

-    training_args = transformers.TrainingArguments(
+    if cfg.adam_beta1:
+        training_arguments_kwargs["adam_beta1"] = cfg.adam_beta1
+    if cfg.adam_beta2:
+        training_arguments_kwargs["adam_beta2"] = cfg.adam_beta2
+    if cfg.adam_epsilon:
+        training_arguments_kwargs["adam_epsilon"] = cfg.adam_epsilon
+    if cfg.max_grad_norm:
+        training_arguments_kwargs["max_grad_norm"] = cfg.max_grad_norm
+
+    if cfg.hub_model_id:
+        training_arguments_kwargs["hub_model_id"] = cfg.hub_model_id
+        training_arguments_kwargs["push_to_hub"] = True
+        training_arguments_kwargs["hub_private_repo"] = True
+
+    if cfg.save_safetensors:
+        training_arguments_kwargs["save_safetensors"] = cfg.save_safetensors
+
+    if cfg.sample_packing_eff_est:
+        training_arguments_kwargs[
+            "sample_packing_efficiency"
+        ] = cfg.sample_packing_eff_est
+
+    training_args = AxolotlTrainingArguments(  # pylint: disable=unexpected-keyword-arg
+        # max_steps=total_num_steps,  # this is helpful in case we don't actually know total # of steps
+        max_seq_length=cfg.sequence_len,
        per_device_train_batch_size=cfg.micro_batch_size,
        per_device_eval_batch_size=cfg.eval_batch_size
        if cfg.eval_batch_size is not None
@@ -126,7 +465,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
        eval_steps=cfg.eval_steps if cfg.val_set_size > 0 else None,
        save_steps=cfg.save_steps,
        output_dir=cfg.output_dir,
-        save_total_limit=3,
+        save_total_limit=cfg.save_total_limit if cfg.save_total_limit else 4,
        load_best_model_at_end=(
            cfg.load_best_model_at_end is not False
            and cfg.val_set_size > 0
@@ -144,6 +483,8 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
        if cfg.lr_scheduler and cfg.lr_scheduler not in ("one_cycle", "log_sweep")
        else "cosine",
        weight_decay=cfg.weight_decay if cfg.weight_decay is not None else 0.0,
+        sample_packing=cfg.sample_packing if cfg.sample_packing else False,
+        sample_packing_seq_len_multiplier=cfg.micro_batch_size,
        **training_arguments_kwargs,
    )

@@ -215,6 +556,19 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
        trainer_kwargs["optimizers"] = (optimizer, lr_scheduler)

    callbacks = []
+    callbacks.append(PrintGPUStatsCallback(cfg))
+
+    if cfg.relora_steps:
+        relora_steps = int(cfg.relora_steps)
+        relora_warmup_steps = int(cfg.relora_warmup_steps)
+        callbacks.append(ReLoRACallback(cfg))
+
+        (optimizer, lr_scheduler) = trainer_kwargs["optimizers"]
+        trainer_kwargs["optimizers"] = (
+            optimizer,
+            ReLoRAScheduler(optimizer, lr_scheduler, relora_steps, relora_warmup_steps),
+        )
+
    # TODO on_save callback to sync checkpoints to GCP/AWS in background
    if cfg.early_stopping_patience:
        early_stop_cb = EarlyStoppingCallback(
@@ -228,17 +582,20 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
    ]:  # only save in rank 0
        callbacks.append(SavePeftModelCallback)

+    if hasattr(model, "use_bettertransformer") and model.use_bettertransformer is True:
+        callbacks.append(SaveBetterTransformerModelCallback)
+
    data_collator_kwargs = {
        "padding": True,
    }
    if cfg.collator_pad_to_longest:
        data_collator_kwargs["padding"] = "longest"
    else:
-        data_collator_kwargs["pad_to_multiple_of"] = 8
+        # A100 is best at 64, while others at 8. Let's use the larger so we don't have to check
+        # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html
+        data_collator_kwargs["pad_to_multiple_of"] = 64

    if cfg.is_llama_derived_model and cfg.landmark_attention:
-        from functools import partial
-
        from axolotl.monkeypatch.llama_landmark_attn import (
            add_mem_tokens,
            get_mem_id,
@@ -247,7 +604,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):

        set_model_mem_id(model, tokenizer)

-        logging.info("Adding landmark attention tokens to dataset")
+        LOG.info("Adding landmark attention tokens to dataset")

        for dataset in [train_dataset, eval_dataset]:
            dataset = dataset.map(
@@ -259,14 +616,14 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
    trainer_cls = (
        OneCycleLRSchedulerTrainer
        if cfg.lr_scheduler == "one_cycle" and (cfg.fsdp or cfg.adapter == "qlora")
-        else transformers.Trainer
+        else AxolotlTrainer
    )
    trainer = trainer_cls(
        model=model,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        args=training_args,
-        data_collator=transformers.DataCollatorForSeq2Seq(
+        data_collator=DataCollatorForSeq2Seq(
            tokenizer,
            return_tensors="pt",
            **data_collator_kwargs,
--- a/src/axolotl/utils/validation.py
+++ b/src/axolotl/utils/validation.py
@@ -2,14 +2,31 @@

 import logging

+import torch
+
+LOG = logging.getLogger("axolotl")
+

 def validate_config(cfg):
+    if cfg.max_packed_sequence_len and cfg.sample_packing:
+        raise ValueError(
+            "please set only one of max_packed_sequence_len (deprecated soon) or sample_packing"
+        )
+    if cfg.max_packed_sequence_len:
+        LOG.warning(
+            str(
+                PendingDeprecationWarning(
+                    "max_packed_sequence_len will be deprecated in favor of sample_packing"
+                )
+            )
+        )
+
    if cfg.gradient_accumulation_steps and cfg.batch_size:
        raise ValueError(
            "please set only one of gradient_accumulation_steps or batch_size"
        )
    if cfg.batch_size:
-        logging.warning(
+        LOG.warning(
            "%s\n%s",
            "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.",
@@ -42,10 +59,13 @@ def validate_config(cfg):
                raise ValueError("Require cfg.load_in_4bit to be True for qlora")

    if not cfg.load_in_8bit and cfg.adapter == "lora":
-        logging.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
+        LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning")
+
+    if cfg.relora_steps and cfg.adapter not in ("lora", "qlora"):
+        raise ValueError("cfg.adapter must be lora or qlora to use ReLoRA")

    if cfg.trust_remote_code:
-        logging.warning(
+        LOG.warning(
            "`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model."
        )

@@ -62,7 +82,63 @@ def validate_config(cfg):
    ) and cfg.gradient_checkpointing:
        raise ValueError("gradient_checkpointing is not supported for MPT models")

+    if cfg.flash_optimum is True:
+        if cfg.adapter:
+            LOG.warning("BetterTransformers probably doesn't work with PEFT adapters")
+        if cfg.fp16 or cfg.bf16:
+            raise ValueError("AMP is not supported with BetterTransformer")
+        if cfg.float16 is not True and cfg.bloat16 is not True:
+            LOG.warning(
+                "You should probably set bfloat16 or float16 to true to "
+                "load the model in float16 for BetterTransformers"
+            )
+        if int(torch.__version__.split(".")[0]) < 2:
+            LOG.warning("torch>=2.0.0 required")
+            raise ValueError(
+                f"flash_optimum for BetterTransformers may not be used with {torch.__version__}"
+            )
+
+    if cfg.pretraining_dataset and cfg.group_by_length:
+        LOG.warning(
+            "You probably want to disable group_by_length as it will force a streamed dataset to download completely."
+        )
+
+    if any([cfg.adam_beta1, cfg.adam_beta2, cfg.adam_epsilon]) and (
+        not cfg.optimizer or "adamw" not in cfg.optimizer
+    ):
+        LOG.warning("adamw hyperparameters found, but no adamw optimizer set")
+
+    if cfg.push_to_hub_model_id:
+        raise ValueError(
+            "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
+        )
+
+    if cfg.gptq and cfg.model_revision:
+        raise ValueError(
+            "model_revision is not supported for GPTQ models. "
+            + "Please download the model from HuggingFace Hub manually for correct branch, "
+            + "point to its path, and remove model_revision from the config."
+        )
+
+    if cfg.sample_packing and cfg.sdp_attention:
+        # incompatible due to bug w/ accelerate causing 0.0 loss when using llama2
+        raise ValueError(
+            "sample_packing not compatible with sdp_attention. Use flash_attention"
+        )
+
+    if cfg.sample_packing and cfg.xformers_attention:
+        raise ValueError(
+            "sample_packing not compatible with xformers_attention. Use flash_attention"
+        )
+
    # TODO
    # MPT 7b
    # https://github.com/facebookresearch/bitsandbytes/issues/25
-    # no 8bit adamw w bf16
+    # no 8bit adaAmw w bf16
+
+    # GPT-NeoX
+    # evals broken when extending context len
+    # File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/models/gpt_neox/modeling_gpt_neox.py", line 162, in forward                        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
+    # File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/optimum/bettertransformer/models/attention.py", line 74, in gpt2_wrapped_scaled_dot_product
+    # attention_mask = causal_mask + attention_mask
+    # RuntimeError: The size of tensor a (2048) must match the size of tensor b (8132) at non-singleton dimension 3
--- a/src/axolotl/utils/wandb.py
+++ b/src/axolotl/utils/wandb.py
@@ -9,6 +9,8 @@ def setup_wandb_env_vars(cfg):
    elif cfg.wandb_project and len(cfg.wandb_project) > 0:
        os.environ["WANDB_PROJECT"] = cfg.wandb_project
        cfg.use_wandb = True
+        if cfg.wandb_entity and len(cfg.wandb_entity) > 0:
+            os.environ["WANDB_ENTITY"] = cfg.wandb_entity
        if cfg.wandb_watch and len(cfg.wandb_watch) > 0:
            os.environ["WANDB_WATCH"] = cfg.wandb_watch
        if cfg.wandb_log_model and len(cfg.wandb_log_model) > 0:
--- a/tests/fixtures/conversation.tokenized_llama2chat.json
+++ b/tests/fixtures/conversation.tokenized_llama2chat.json
--- a/tests/monkeypatch/test_llama_attn_hijack_flash.py
+++ b/tests/monkeypatch/test_llama_attn_hijack_flash.py
@@ -0,0 +1,30 @@
+"""
+Unit tests for the monkeypatch utils
+"""
+import unittest
+
+import torch
+
+from axolotl.monkeypatch.utils import get_cu_seqlens, get_cu_seqlens_from_pos_ids
+
+
+class TestMonkeyPatchUtils(unittest.TestCase):
+    """
+    Unit test class for monkeypatch utils
+    """
+
+    def test_get_cu_seqlens_1d(self):
+        attn_mask = torch.tensor([[1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 0, 0]])
+        target_res = torch.tensor([0, 4, 7, 12, 14, 16], dtype=torch.int32)
+        self.assertTrue(torch.allclose(get_cu_seqlens(attn_mask)[0], target_res))
+
+    def test_get_cu_seqlens_from_pos_ids_1d(self):
+        position_ids = torch.tensor([[0, 1, 2, 3, 0, 1, 2, 0, 1, 2, 3, 4, 0, 1, 0, 0]])
+        target_res = torch.tensor([0, 4, 7, 12, 14, 16], dtype=torch.int32)
+        self.assertTrue(
+            torch.allclose(get_cu_seqlens_from_pos_ids(position_ids)[0], target_res)
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/tests/test_expand_mask.py
+++ b/tests/test_expand_mask.py
@@ -0,0 +1,44 @@
+"""
+Unit tests for the monkey patch for expand mask to handle packed sequences
+"""
+import unittest
+
+import torch
+
+from axolotl.monkeypatch.llama_expand_mask import _expand_mask
+
+
+class TestExpandMask(unittest.TestCase):
+    """
+    Test class for attention mask expansion for packed sequences
+    """
+
+    def test_output(self):
+        mask = torch.tensor([[1, 1, 1, 2], [2, 3, 3, 0]])
+        dtype = torch.float32
+        expected_output = torch.tensor(
+            [
+                [
+                    [
+                        [0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
+                        [0.0000e00, 0.0000e00, -3.4028e38, -3.4028e38],
+                        [0.0000e00, 0.0000e00, 0.0000e00, -3.4028e38],
+                        [-3.4028e38, -3.4028e38, -3.4028e38, 0.0000e00],
+                    ]
+                ],
+                [
+                    [
+                        [0.0000e00, -3.4028e38, -3.4028e38, -3.4028e38],
+                        [-3.4028e38, 0.0000e00, -3.4028e38, -3.4028e38],
+                        [-3.4028e38, 0.0000e00, 0.0000e00, -3.4028e38],
+                        [-3.4028e38, -3.4028e38, -3.4028e38, -3.4028e38],
+                    ]
+                ],
+            ]
+        )
+        # Check that the output matches the expected output
+        self.assertTrue(torch.allclose(_expand_mask(mask, dtype), expected_output))
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/tests/test_packed_dataset.py
+++ b/tests/test_packed_dataset.py
@@ -27,7 +27,7 @@ class TestPacking(unittest.TestCase):
            }
        )

-    def test_resets_attention(self):
+    def test_increments_attention(self):
        prompter = AlpacaPrompter("chat")
        strat = AlpacaPromptTokenizingStrategy(
            prompter,
@@ -55,10 +55,14 @@ class TestPacking(unittest.TestCase):
        # first example doesn't have mask reset
        assert example["input_ids"][0] == self.tokenizer.bos_token_id
        assert example["attention_mask"][0] == 1
+        assert example["position_ids"][0] == 0
+        assert example["position_ids"][1] == 1

        # but subsequent one does
        assert example["input_ids"][next_bos_index] == self.tokenizer.bos_token_id
-        assert example["attention_mask"][next_bos_index] == 0
+        assert example["attention_mask"][next_bos_index] == 2
+        assert example["position_ids"][next_bos_index] == 0
+        assert example["position_ids"][next_bos_index + 1] == 1


 if __name__ == "__main__":
--- a/tests/test_prompt_tokenizers.py
+++ b/tests/test_prompt_tokenizers.py
@@ -4,12 +4,24 @@ import logging
 import unittest
 from pathlib import Path

-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, LlamaTokenizer

-from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
-from axolotl.prompters import ShareGPTPrompter
+from axolotl.prompt_strategies.alpaca_chat import NoSystemPrompter
+from axolotl.prompt_strategies.alpaca_w_system import (
+    InstructionWSystemPromptTokenizingStrategy,
+    SystemDataPrompter,
+)
+from axolotl.prompt_strategies.llama2_chat import (
+    Llama2ChatPrompter,
+    LLama2ChatTokenizingStrategy,
+)
+from axolotl.prompt_tokenizers import (
+    AlpacaPromptTokenizingStrategy,
+    ShareGPTPromptTokenizingStrategy,
+)
+from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompter

-logging.basicConfig(level="INFO")
+LOG = logging.getLogger("axolotl")


 class TestPromptTokenizationStrategies(unittest.TestCase):
@@ -29,7 +41,6 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
        )

    def test_sharegpt_integration(self):
-        print(Path(__file__).parent)
        with open(
            Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
        ) as fin:
@@ -53,6 +64,166 @@ class TestPromptTokenizationStrategies(unittest.TestCase):
            self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
            self.assertEqual(example[fields], tokenized_conversation[fields])

+    def test_no_sys_prompt(self):
+        """
+        tests the interface between the user and assistant parts
+        """
+        prompter = NoSystemPrompter()
+        # pylint: disable=duplicate-code
+        strat = AlpacaPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        sample = {
+            "instruction": "hello cruel. lorem ipsum dolor sit amet.",
+            "output": "world!",
+        }
+        example = strat.tokenize_prompt(sample)
+        world_idx = example["input_ids"].index(3186)
+        assert example["labels"][world_idx] == 3186
+        assert example["labels"][world_idx - 1] == -100
+
+    def test_alpaca(self):
+        """
+        tests the interface between the user and assistant parts
+        """
+        # pylint: disable=duplicate-code
+        prompter = AlpacaPrompter()
+        strat = AlpacaPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        sample = {"instruction": "hello!", "output": "Hi! How can I help?"}
+        example = strat.tokenize_prompt(sample)
+        world_idx = example["input_ids"].index(6324)
+        assert example["labels"][world_idx] == 6324
+        assert example["labels"][world_idx - 1] == -100
+
+
+class InstructionWSystemPromptTokenizingStrategyTest(unittest.TestCase):
+    """
+    Test class for prompt tokenization strategies with sys prompt from the dataset
+    """
+
+    def setUp(self) -> None:
+        # pylint: disable=duplicate-code
+        self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
+        self.tokenizer.add_special_tokens(
+            {
+                "bos_token": "<s>",
+                "eos_token": "</s>",
+                "unk_token": "<unk>",
+            }
+        )
+
+    def test_system_alpaca(self):
+        prompter = SystemDataPrompter(PromptStyle.CHAT.value)
+        strat = InstructionWSystemPromptTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            2048,
+        )
+        sample = {
+            "system": "use cot",
+            "instruction": "hello!",
+            "output": "Hi! How can I help?",
+        }
+        example = strat.tokenize_prompt(sample)
+        assert example["input_ids"][0:5] == [
+            1,
+            28962,
+            1254,
+            12665,
+            29901,
+        ]  # "<s>SYSTEM:"
+        assert example["input_ids"][5:7] == [671, 20118]  # " use cot"
+        assert example["input_ids"][8] == 11889  # USER
+
+
+class Llama2ChatTokenizationTest(unittest.TestCase):
+    """
+    Test class for prompt tokenization strategies with sys prompt from the dataset
+    """
+
+    def setUp(self) -> None:
+        # pylint: disable=duplicate-code
+        self.tokenizer = LlamaTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
+        # woraround because official Meta repos are not open
+
+    def test_llama2_chat_integration(self):
+        with open(
+            Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
+        ) as fin:
+            data = fin.read()
+            conversation = json.loads(data)
+        with open(
+            Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
+            encoding="utf-8",
+        ) as fin:
+            data = fin.read()
+            tokenized_conversation = json.loads(data)
+        prompter = Llama2ChatPrompter()
+        strat = LLama2ChatTokenizingStrategy(
+            prompter,
+            self.tokenizer,
+            False,
+            4096,
+        )
+        example = strat.tokenize_prompt(conversation)
+        for fields in ["input_ids", "attention_mask", "labels"]:
+            self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
+            self.assertEqual(example[fields], tokenized_conversation[fields])
+
+    def compare_with_transformers_integration(self):
+        # this needs transformers >= v4.31.0
+        from transformers.models.llama.tokenization_llama import B_SYS, E_SYS
+        from transformers.pipelines.conversational import Conversation
+
+        # from transformers.models.llama.tokenization_llama import DEFAULT_SYSTEM_PROMPT
+        # broken as of 23/7/20
+        # see https://github.com/huggingface/transformers/pull/24935
+        # pylint: disable=C0103
+        DEFAULT_SYSTEM_PROMPT = """\
+You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
+
+If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
+        with open(
+            Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
+        ) as fin:
+            data = fin.read()
+            conversation = json.loads(data)
+        with open(
+            Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
+            encoding="utf-8",
+        ) as fin:
+            data = fin.read()
+            tokenized_conversation = json.loads(data)
+
+        user_input = []
+        answers = []
+        for msg in conversation["conversations"]:
+            if msg["from"] == "human":
+                user_input.append(msg["value"])
+            else:
+                answers.append(msg["value"])
+        hf_conf = Conversation(
+            text=user_input[-1],
+            past_user_inputs=[B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + user_input[0]]
+            + user_input[1:-1],
+            generated_responses=answers,
+        )
+        # pylint: disable=W0212
+        hf_tokens = self.tokenizer._build_conversation_input_ids(hf_conf)
+
+        self.assertEqual(
+            hf_tokens, tokenized_conversation["input_ids"][: len(hf_tokens)]
+        )
+

 if __name__ == "__main__":
    unittest.main()
--- a/tests/test_prompters.py
+++ b/tests/test_prompters.py
@@ -2,7 +2,13 @@

 import unittest

-from axolotl.prompters import AlpacaPrompter, PromptStyle
+from axolotl.prompt_strategies.alpaca_w_system import SystemDataPrompter
+from axolotl.prompters import (
+    AlpacaPrompter,
+    MultipleChoiceExplainPrompter,
+    PromptStyle,
+    UnpromptedPrompter,
+)


 class AlpacaPrompterTest(unittest.TestCase):
@@ -55,3 +61,64 @@ class AlpacaPrompterTest(unittest.TestCase):
        assert "### Response:" not in res
        assert "USER:" in res
        assert "ASSISTANT:" in res
+
+    def test_system_prompt(self):
+        prompter = SystemDataPrompter(prompt_style=PromptStyle.CHAT.value)
+        res = next(
+            prompter.build_prompt_w_system(
+                "use cot", "tell me a joke about the following", "alpacas"
+            )
+        )
+        assert "use cot" in res
+        assert res.startswith("SYSTEM:")
+        assert "### Instruction:" not in res
+        assert "### Input:" not in res
+        assert "alpacas" in res
+        assert "### Response:" not in res
+        assert "USER:" in res
+        assert "ASSISTANT:" in res
+
+
+class UnpromptedPrompterTest(unittest.TestCase):
+    """
+    Test class for UnpromptedPrompter with no system prompts
+    """
+
+    def test_prompt_style_w_none(self):
+        prompter = UnpromptedPrompter(prompt_style=None)
+        res = next(prompter.build_prompt("tell me a joke"))
+        assert "### Instruction:" in res
+        assert "tell me a joke" in res
+        assert res.startswith("###")
+
+    def test_prompt_style_w_instruct(self):
+        prompter = UnpromptedPrompter(prompt_style=PromptStyle.INSTRUCT.value)
+        res = next(
+            prompter.build_prompt("tell me a joke about the following", "alpacas")
+        )
+        assert "### Instruction:" in res
+        assert "tell me a joke" in res
+        assert res.startswith("###")
+
+    def test_prompt_style_w_chat(self):
+        prompter = UnpromptedPrompter(prompt_style=PromptStyle.CHAT.value)
+        res = next(
+            prompter.build_prompt("tell me a joke about the following", "alpacas")
+        )
+        assert "USER:" in res
+        assert "tell me a joke" in res
+        assert res.startswith("USER:")
+
+
+class MultipleChoiceExplainPrompterTest(unittest.TestCase):
+    """
+    Test class for MultipleChoiceExplainPrompter
+    """
+
+    def test_prompt_style_w_chat(self):
+        prompter = MultipleChoiceExplainPrompter(prompt_style=PromptStyle.CHAT.value)
+        res = next(prompter.build_prompt("choose one", "- A\n- B\n- C", "C"))
+        assert "USER:" in res
+        assert "choose one" in res
+        assert "Choose the answer that best answers the question." in res
+        assert "- A\n- B\n- C" in res
--- a/tests/test_tokenizers.py
+++ b/tests/test_tokenizers.py
@@ -0,0 +1,31 @@
+"""
+Test cases for the tokenizer loading
+"""
+import unittest
+
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.models import load_tokenizer
+
+
+class TestTokenizers(unittest.TestCase):
+    """
+    test class for the load_tokenizer fn
+    """
+
+    def test_default_use_fast(self):
+        cfg = DictDefault({})
+        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
+        assert "Fast" in tokenizer.__class__.__name__
+
+    def test_dont_use_fast(self):
+        cfg = DictDefault(
+            {
+                "tokenizer_use_fast": False,
+            }
+        )
+        tokenizer = load_tokenizer("huggyllama/llama-7b", None, cfg)
+        assert "Fast" not in tokenizer.__class__.__name__
+
+
+if __name__ == "__main__":
+    unittest.main()
--- a/tests/test_validation.py
+++ b/tests/test_validation.py
@@ -212,3 +212,128 @@ class ValidationTest(unittest.TestCase):

        with pytest.raises(ValueError, match=regex_exp):
            validate_config(cfg)
+
+    def test_flash_optimum(self):
+        cfg = DictDefault(
+            {
+                "flash_optimum": True,
+                "adapter": "lora",
+            }
+        )
+
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "BetterTransformers probably doesn't work with PEFT adapters"
+                in record.message
+                for record in self._caplog.records
+            )
+
+        cfg = DictDefault(
+            {
+                "flash_optimum": True,
+            }
+        )
+
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "probably set bfloat16 or float16" in record.message
+                for record in self._caplog.records
+            )
+
+        cfg = DictDefault(
+            {
+                "flash_optimum": True,
+                "fp16": True,
+            }
+        )
+        regex_exp = r".*AMP is not supported.*"
+
+        with pytest.raises(ValueError, match=regex_exp):
+            validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "flash_optimum": True,
+                "bf16": True,
+            }
+        )
+        regex_exp = r".*AMP is not supported.*"
+
+        with pytest.raises(ValueError, match=regex_exp):
+            validate_config(cfg)
+
+    def test_adamw_hyperparams(self):
+        cfg = DictDefault(
+            {
+                "optimizer": None,
+                "adam_epsilon": 0.0001,
+            }
+        )
+
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "adamw hyperparameters found, but no adamw optimizer set"
+                in record.message
+                for record in self._caplog.records
+            )
+
+        cfg = DictDefault(
+            {
+                "optimizer": "adafactor",
+                "adam_beta1": 0.0001,
+            }
+        )
+
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "adamw hyperparameters found, but no adamw optimizer set"
+                in record.message
+                for record in self._caplog.records
+            )
+
+        cfg = DictDefault(
+            {
+                "optimizer": "adamw_bnb_8bit",
+                "adam_beta1": 0.9,
+                "adam_beta2": 0.99,
+                "adam_epsilon": 0.0001,
+            }
+        )
+
+        validate_config(cfg)
+
+        cfg = DictDefault(
+            {
+                "optimizer": "adafactor",
+            }
+        )
+
+        validate_config(cfg)
+
+    def test_packing(self):
+        cfg = DictDefault(
+            {
+                "max_packed_sequence_len": 2048,
+            }
+        )
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert any(
+                "max_packed_sequence_len will be deprecated in favor of sample_packing"
+                in record.message
+                for record in self._caplog.records
+            )
+
+        cfg = DictDefault(
+            {
+                "max_packed_sequence_len": 2048,
+                "sample_packing": True,
+            }
+        )
+        regex_exp = r".*set only one of max_packed_sequence_len \(deprecated soon\) or sample_packing.*"
+        with pytest.raises(ValueError, match=regex_exp):
+            validate_config(cfg)