bump transformers to 4.51.3

* [ci] make e2e tests a bit faster by reducing test split size

* use 10% split of alpaca dataset to speed up dataset loading/tokenization

* reduce gas 4->2 for most e2e tests

* increase val set size for packing

CNAME

@@ -0,0 +1 @@
+docs.axolotl.ai

README.md

@@ -63,7 +63,7 @@ axolotl fetch examples
 axolotl fetch deepspeed_configs  # OPTIONAL
 ```
 
-Other installation approaches are described [here](https://axolotl-ai-cloud.github.io/axolotl/docs/installation.html).
+Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).
 
 ### Your First Fine-tune
 
@@ -78,7 +78,7 @@ axolotl fetch examples --dest path/to/folder
 axolotl train examples/llama-3/lora-1b.yml
 ```
 
-That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/getting-started.html) for a more detailed walkthrough.
+That's it! Check out our [Getting Started Guide](https://docs.axolotl.ai/docs/getting-started.html) for a more detailed walkthrough.
 
 ## ✨ Key Features
 
@@ -91,20 +91,20 @@ That's it! Check out our [Getting Started Guide](https://axolotl-ai-cloud.github
 
 ## 📚 Documentation
 
-- [Installation Options](https://axolotl-ai-cloud.github.io/axolotl/docs/installation.html) - Detailed setup instructions for different environments
-- [Configuration Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html) - Full configuration options and examples
-- [Dataset Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/) - Supported formats and how to use them
-- [Multi-GPU Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-gpu.html)
-- [Multi-Node Training](https://axolotl-ai-cloud.github.io/axolotl/docs/multi-node.html)
-- [Multipacking](https://axolotl-ai-cloud.github.io/axolotl/docs/multipack.html)
-- [API Reference](https://axolotl-ai-cloud.github.io/axolotl/docs/api/) - Auto-generated code documentation
-- [FAQ](https://axolotl-ai-cloud.github.io/axolotl/docs/faq.html) - Frequently asked questions
+- [Installation Options](https://docs.axolotl.ai/docs/installation.html) - Detailed setup instructions for different environments
+- [Configuration Guide](https://docs.axolotl.ai/docs/config.html) - Full configuration options and examples
+- [Dataset Guide](https://docs.axolotl.ai/docs/dataset-formats/) - Supported formats and how to use them
+- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
+- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
+- [Multipacking](https://docs.axolotl.ai/docs/multipack.html)
+- [API Reference](https://docs.axolotl.ai/docs/api/) - Auto-generated code documentation
+- [FAQ](https://docs.axolotl.ai/docs/faq.html) - Frequently asked questions
 
 ## 🤝 Getting Help
 
 - Join our [Discord community](https://discord.gg/HhrNrHJPRb) for support
 - Check out our [Examples](https://github.com/axolotl-ai-cloud/axolotl/tree/main/examples/) directory
-- Read our [Debugging Guide](https://axolotl-ai-cloud.github.io/axolotl/docs/debugging.html)
+- Read our [Debugging Guide](https://docs.axolotl.ai/docs/debugging.html)
 - Need dedicated support? Please contact [✉️wing@axolotl.ai](mailto:wing@axolotl.ai) for options
 
 ## 🌟 Contributing

_quarto.yml

@@ -231,6 +231,7 @@ website:
             - docs/reward_modelling.qmd
             - docs/lr_groups.qmd
             - docs/lora_optims.qmd
+            - docs/dataset_loading.qmd
 
         - section: "Core Concepts"
           contents:

cicd/multigpu.py

@@ -68,7 +68,7 @@ def run_cmd(cmd: str, run_folder: str):
 @app.function(
     image=cicd_image,
     gpu=GPU_CONFIG,
-    timeout=60 * 60,
+    timeout=90 * 60,
     cpu=8.0,
     memory=131072 * N_GPUS,
     volumes=VOLUME_CONFIG,

docker/Dockerfile-base

@@ -29,7 +29,7 @@ ENV PATH="/root/miniconda3/envs/py${PYTHON_VERSION}/bin:${PATH}"
 WORKDIR /workspace
 
 RUN python3 -m pip install --upgrade pip && pip3 install -U packaging==23.2 setuptools==75.8.0 wheel && \
-    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
+    python3 -m pip install --no-cache-dir -U torch==${PYTORCH_VERSION}+cu${CUDA} torchvision --extra-index-url https://download.pytorch.org/whl/cu$CUDA && \
     python3 -m pip install --no-cache-dir "causal_conv1d @ git+https://github.com/Dao-AILab/causal-conv1d.git@main" && \
     python3 -m pip install --no-cache-dir "mamba_ssm @ git+https://github.com/state-spaces/mamba.git@main"
 

docs/config.qmd

@@ -90,7 +90,7 @@ lora_on_cpu: true
 
 # List[str]. Add plugins to extend the pipeline.
 # See `src/axolotl/integrations` for the available plugins or doc below for more details.
-# https://axolotl-ai-cloud.github.io/axolotl/docs/custom_integrations.html
+# https://docs.axolotl.ai/docs/custom_integrations.html
 plugins:
   # - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
 
@@ -109,7 +109,7 @@ datasets:
     preprocess_shards: # Optional[int] process dataset in N sequential chunks for memory efficiency (exclusive with `shards`)
 
     name: # Optional[str] name of dataset configuration to load
-    train_on_split: train # Optional[str] name of dataset split to load from
+    split: train # Optional[str] name of dataset split to load from
     revision: # Optional[str] The specific revision of the dataset to use when loading from the Hugging Face Hub. This can be a commit hash, tag, or branch name. If not specified, the latest version will be used. This parameter is ignored for local datasets.
     trust_remote_code: # Optional[bool] Trust remote code for untrusted source
 
@@ -165,7 +165,9 @@ datasets:
       content: value
       # ...
 
-    # Optional[Dict[str, List]]. Roles mapping in the messages. The default is:
+    # Optional[Dict[str, List]]. Roles mapping in the messages.
+    # The format is {target_role: [source_roles]}. All source roles will be mapped to the target role.
+    # The default is:
     roles:
       user: ["human", "user"]
       assistant: ["gpt", "assistant"]
@@ -392,7 +394,7 @@ lora_fan_in_fan_out: false
 
 # Apply custom LoRA autograd functions and activation function Triton kernels for
 # speed and memory savings
-# See: https://axolotl-ai-cloud.github.io/axolotl/docs/lora_optims.html
+# See: https://docs.axolotl.ai/docs/lora_optims.html
 lora_mlp_kernel: true
 lora_qkv_kernel: true
 lora_o_kernel: true
@@ -686,7 +688,7 @@ ddp_broadcast_buffers:
 # Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
 # E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
 # subsequences, or set to 4 to split into four equal-sized subsequences.
-# See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
+# See https://docs.axolotl.ai/docs/sequence_parallelism.html for more details.
 sequence_parallel_degree:
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 # Must evenly divide the number of KV heads in your model.

docs/dataset-formats/index.qmd

@@ -13,6 +13,13 @@ As there are a lot of available options in Axolotl, this guide aims to provide a
 
 Axolotl supports 3 kinds of training methods: pre-training, supervised fine-tuning, and preference-based post-training (e.g. DPO, ORPO, PRMs). Each method has their own dataset format which are described below.
 
+::: {.callout-tip}
+
+This guide will mainly use JSONL as an introduction. Please refer to the [dataset loading docs](../dataset_loading.qmd) to understand how to load datasets from other sources.
+
+For `pretraining_dataset:` specifically, please refer to the [Pre-training section](#pre-training).
+:::
+
 ## Pre-training
 
 When aiming to train on large corpora of text datasets, pre-training is your go-to choice. Due to the size of these datasets, downloading the entire-datasets before beginning training would be prohibitively time-consuming. Axolotl supports [streaming](https://huggingface.co/docs/datasets/en/stream) to only load batches into memory at a time.
@@ -450,10 +457,7 @@ datasets:
     type: alpaca
 ```
 
-Axolotl supports many kinds of instruction dataset. All of them can be found here (https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/inst_tune.html) with their respective type and sample row format.
-
-
-Reference: [Instruction Dataset Documentation](inst_tune.qmd).
+Axolotl supports many kinds of instruction dataset. All of them can be found in the [Instruction Dataset Documentation](inst_tune.qmd) with their respective type and sample row format.
 
 #### Custom Instruct Prompt Format
 

docs/dataset_loading.qmd

@@ -0,0 +1,276 @@
+---
+title: Dataset Loading
+description: Understanding how to load datasets from different sources
+back-to-top-navigation: true
+toc: true
+toc-depth: 5
+---
+
+## Overview
+
+Datasets can be loaded in a number of different ways depending on the how it is saved (the extension of the file) and where it is stored.
+
+## Loading Datasets
+
+We use the `datasets` library to load datasets and a mix of `load_dataset` and `load_from_disk` to load them.
+
+You may recognize the similar named configs between `load_dataset` and the `datasets` section of the config file.
+
+```yaml
+datasets:
+  - path:
+    name:
+    data_files:
+    split:
+    revision:
+    trust_remote_code:
+```
+
+::: {.callout-tip}
+
+Do not feel overwhelmed by the number of options here. A lot of them are optional. In fact, the most common config to use would be `path` and sometimes `data_files`.
+
+:::
+
+This matches the API of [`datasets.load_dataset`](https://github.com/huggingface/datasets/blob/0b5998ac62f08e358f8dcc17ec6e2f2a5e9450b6/src/datasets/load.py#L1838-L1858), so if you're familiar with that, you will feel right at home.
+
+For HuggingFace's guide to load different dataset types, see [here](https://huggingface.co/docs/datasets/loading).
+
+For full details on the config, see [config.qmd](config.qmd).
+
+::: {.callout-note}
+
+You can set multiple datasets in the config file by more than one entry under `datasets`.
+
+```yaml
+datasets:
+  - path: /path/to/your/dataset
+  - path: /path/to/your/other/dataset
+```
+
+:::
+
+### Local dataset
+
+#### Files
+
+Usually, to load a JSON file, you would do something like this:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("json", data_files="data.json")
+```
+
+Which translates to the following config:
+
+```yaml
+datasets:
+  - path: json
+    data_files: /path/to/your/file.jsonl
+```
+
+However, to make things easier, we have added a few shortcuts for loading local dataset files.
+
+You can just point the `path` to the file or directory along with the `ds_type` to load the dataset. The below example shows for a JSON file:
+
+```yaml
+datasets:
+  - path: /path/to/your/file.jsonl
+    ds_type: json
+```
+
+This works for CSV, JSON, Parquet, and Arrow files.
+
+::: {.callout-tip}
+
+If `path` points to a file and `ds_type` is not specified, we will automatically infer the dataset type from the file extension, so you could omit `ds_type` if you'd like.
+
+:::
+
+#### Directory
+
+If you're loading a directory, you can point the `path` to the directory.
+
+Then, you have two options:
+
+##### Loading entire directory
+
+You do not need any additional configs.
+
+We will attempt to load in the following order:
+- datasets saved with `datasets.save_to_disk`
+- loading entire directory of files (such as with parquet/arrow files)
+
+```yaml
+datasets:
+  - path: /path/to/your/directory
+```
+
+##### Loading specific files in directory
+
+Provide `data_files` with a list of files to load.
+
+```yaml
+datasets:
+    # single file
+  - path: /path/to/your/directory
+    ds_type: csv
+    data_files: file1.csv
+
+    # multiple files
+  - path: /path/to/your/directory
+    ds_type: json
+    data_files:
+      - file1.jsonl
+      - file2.jsonl
+
+    # multiple files for parquet
+  - path: /path/to/your/directory
+    ds_type: parquet
+    data_files:
+      - file1.parquet
+      - file2.parquet
+
+```
+
+### HuggingFace Hub
+
+The method you use to load the dataset depends on how the dataset was created, whether a folder was uploaded directly or a HuggingFace Dataset was pushed.
+
+::: {.callout-note}
+
+If you're using a private dataset, you will need to enable the `hf_use_auth_token` flag in the root-level of the config file.
+
+:::
+
+#### Folder uploaded
+
+This would mean that the dataset is a single file or file(s) uploaded to the Hub.
+
+```yaml
+datasets:
+  - path: org/dataset-name
+    data_files:
+      - file1.jsonl
+      - file2.jsonl
+```
+
+#### HuggingFace Dataset
+
+This means that the dataset is created as a HuggingFace Dataset and pushed to the Hub via `datasets.push_to_hub`.
+
+```yaml
+datasets:
+  - path: org/dataset-name
+```
+
+::: {.callout-note}
+
+There are some other configs which may be required like `name`, `split`, `revision`, `trust_remote_code`, etc depending on the dataset.
+
+:::
+
+### Remote Filesystems
+
+Via the `storage_options` config under `load_dataset`, you can load datasets from remote filesystems like S3, GCS, Azure, and OCI.
+
+::: {.callout-warning}
+
+This is currently experimental. Please let us know if you run into any issues!
+
+:::
+
+The only difference between the providers is that you need to prepend the path with the respective protocols.
+
+```yaml
+datasets:
+    # Single file
+  - path: s3://bucket-name/path/to/your/file.jsonl
+
+    # Directory
+  - path: s3://bucket-name/path/to/your/directory
+```
+
+For directory, we load via `load_from_disk`.
+
+#### S3
+
+Prepend the path with `s3://`.
+
+The credentials are pulled in the following order:
+
+- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables
+- from the `~/.aws/credentials` file
+- for nodes on EC2, the IAM metadata provider
+
+::: {.callout-note}
+
+We assume you have credentials setup and not using anonymous access. If you want to use anonymous access, let us know! We may have to open a config option for this.
+
+:::
+
+Other environment variables that can be set can be found in [boto3 docs](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html#using-environment-variables)
+
+#### GCS
+
+Prepend the path with `gs://` or `gcs://`.
+
+The credentials are loaded in the following order:
+
+- gcloud credentials
+- for nodes on GCP, the google metadata service
+- anonymous access
+
+#### Azure
+
+##### Gen 1
+
+Prepend the path with `adl://`.
+
+Ensure you have the following environment variables set:
+
+- `AZURE_STORAGE_TENANT_ID`
+- `AZURE_STORAGE_CLIENT_ID`
+- `AZURE_STORAGE_CLIENT_SECRET`
+
+##### Gen 2
+
+Prepend the path with `abfs://` or `az://`.
+
+Ensure you have the following environment variables set:
+
+- `AZURE_STORAGE_ACCOUNT_NAME`
+- `AZURE_STORAGE_ACCOUNT_KEY`
+
+Other environment variables that can be set can be found in [adlfs docs](https://github.com/fsspec/adlfs?tab=readme-ov-file#setting-credentials)
+
+#### OCI
+
+Prepend the path with `oci://`.
+
+It would attempt to read in the following order:
+
+- `OCIFS_IAM_TYPE`, `OCIFS_CONFIG_LOCATION`, and `OCIFS_CONFIG_PROFILE` environment variables
+- when on OCI resource, resource principal
+
+Other environment variables:
+
+- `OCI_REGION_METADATA`
+
+Please see the [ocifs docs](https://ocifs.readthedocs.io/en/latest/getting-connected.html#Using-Environment-Variables).
+
+### HTTPS
+
+The path should start with `https://`.
+
+```yaml
+datasets:
+  - path: https://path/to/your/dataset/file.jsonl
+```
+
+This must be publically accessible.
+
+## Next steps
+
+Now that you know how to load datasets, you can learn more on how to load your specific dataset format into your target output format [dataset formats docs](dataset-formats).

docs/multi-gpu.qmd

@@ -36,6 +36,9 @@ deepspeed: deepspeed_configs/zero1.json
 ### Usage {#sec-deepspeed-usage}
 
 ```{.bash}
+# Fetch deepspeed configs (if not already present)
+axolotl fetch deepspeed_configs
+
 # Passing arg via config
 axolotl train config.yml
 
@@ -48,10 +51,20 @@ axolotl train config.yml --deepspeed deepspeed_configs/zero1.json
 We provide default configurations for:
 
 - ZeRO Stage 1 (`zero1.json`)
+- ZeRO Stage 1 with torch compile (`zero1_torch_compile.json`)
 - ZeRO Stage 2 (`zero2.json`)
 - ZeRO Stage 3 (`zero3.json`)
+- ZeRO Stage 3 with bf16 (`zero3_bf16.json`)
+- ZeRO Stage 3 with bf16 and CPU offload params(`zero3_bf16_cpuoffload_params.json`)
+- ZeRO Stage 3 with bf16 and CPU offload params and optimizer (`zero3_bf16_cpuoffload_all.json`)
 
-Choose based on your memory requirements and performance needs.
+::: {.callout-tip}
+
+Choose the configuration that offloads the least amount to memory while still being able to fit on VRAM for best performance.
+
+Start from Stage 1 -> Stage 2 -> Stage 3.
+
+:::
 
 ## FSDP {#sec-fsdp}
 

docs/multimodal.qmd

@@ -9,6 +9,7 @@ format:
 ## Supported Models
 
 - [Mllama](#sec-mllama)
+- [Llama4](#sec-llama4)
 - [Pixtral](#sec-pixtral)
 - [Llava-1.5](#sec-llava-15)
 - [Mistral-Small-3.1](#sec-mistral-small-31)
@@ -63,6 +64,14 @@ base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
 chat_template: llama3_2_vision
 ```
 
+### Llama4 {#sec-llama4}
+
+```yaml
+base_model: meta-llama/Llama-4-Scout-17B-16E-Instruct
+
+chat_template: llama4
+```
+
 ### Pixtral {#sec-pixtral}
 
 ```yaml

docs/rlhf.qmd

@@ -530,7 +530,7 @@ trl:
 ```
 
 ```bash
-CUDA_VISIBLE_DEVICES=2,3 axolotl vllm_serve grpo.yaml
+CUDA_VISIBLE_DEVICES=2,3 axolotl vllm-serve grpo.yaml
 ```
 
 Your `vLLM` instance will now attempt to spin up, and it's time to kick off training utilizing our remaining two GPUs. In another terminal, execute:

examples/cerebras/btlm-ft.yml

@@ -8,7 +8,6 @@ tokenizer_type: GPT2Tokenizer
 trust_remote_code: true
 tokenizer_use_fast: true
 tokenizer_legacy: true
-strict: false
 push_dataset_to_hub:
 hf_use_auth_token: true
 datasets:

examples/cerebras/qlora.yml

@@ -4,7 +4,6 @@ base_model: cerebras/Cerebras-GPT-1.3B
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/code-llama/13b/lora.yml

@@ -7,7 +7,6 @@ tokenizer_type: CodeLlamaTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/code-llama/13b/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: CodeLlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/code-llama/34b/lora.yml

@@ -7,7 +7,6 @@ tokenizer_type: CodeLlamaTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/code-llama/34b/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: CodeLlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/code-llama/7b/lora.yml

@@ -7,7 +7,6 @@ tokenizer_type: CodeLlamaTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/code-llama/7b/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: CodeLlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/cohere/command-r-7b-qlora.yml

@@ -4,7 +4,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 # huggingface repo
 chat_template: cohere

examples/dbrx/16bit-lora.yaml

@@ -3,7 +3,6 @@ base_model: LnL-AI/dbrx-base-converted-v2
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/dbrx/8bit-lora.yaml

@@ -6,7 +6,6 @@ trust_remote_code: true
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/dbrx/fft-ds-zero3.yaml

@@ -3,7 +3,6 @@ base_model: LnL-AI/dbrx-base-converted-v2
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/deepcoder/deepcoder-14B-preview-lora.yml

@@ -0,0 +1,58 @@
+base_model: agentica-org/DeepCoder-14B-Preview
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+    field_messages: messages
+    message_property_mappings:
+      role: role
+      content: content
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./outputs/lora-out
+
+sequence_len: 4096
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 4
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/deepcogito/cogito-v1-preview-llama-3B-lora.yml

@@ -0,0 +1,58 @@
+base_model: deepcogito/cogito-v1-preview-llama-3B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+    field_messages: messages
+    message_property_mappings:
+      role: role
+      content: content
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./outputs/lora-out
+
+sequence_len: 4096
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/deepcogito/cogito-v1-preview-qwen-14B-lora.yml

@@ -0,0 +1,58 @@
+base_model: deepcogito/cogito-v1-preview-qwen-14B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: true
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: fozziethebeat/alpaca_messages_2k_test
+    type: chat_template
+    field_messages: messages
+    message_property_mappings:
+      role: role
+      content: content
+
+dataset_prepared_path:
+val_set_size: 0.05
+output_dir: ./outputs/lora-out
+
+sequence_len: 4096
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 32
+lora_alpha: 16
+lora_dropout: 0.05
+lora_target_linear: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 2
+micro_batch_size: 2
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: true
+resume_from_checkpoint:
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 10
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:

examples/deepseek-v2/fft-fsdp-16b.yaml

@@ -2,7 +2,6 @@ base_model: deepseek-ai/DeepSeek-V2-Lite
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 trust_remote_code: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/deepseek-v2/qlora-fsdp-2_5.yaml

@@ -6,7 +6,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 
 plugins:

examples/falcon/config-7b-lora.yml

@@ -11,7 +11,6 @@ trust_remote_code: true
 load_in_8bit: true
 load_in_4bit: false
 gptq: false
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/falcon/config-7b-qlora.yml

@@ -15,7 +15,6 @@ load_in_8bit: false
 # enable 4bit for QLoRA
 load_in_4bit: true
 gptq: false
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: QingyiSi/Alpaca-CoT

examples/falcon/config-7b.yml

@@ -8,7 +8,6 @@ tokenizer_type: AutoTokenizer
 # required by falcon custom model code: https://huggingface.co/tiiuae/falcon-7b/tree/main
 trust_remote_code: true
 gptq: false
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/gemma/qlora.yml

@@ -8,7 +8,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 # huggingface repo
 datasets:

examples/gemma2/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 # huggingface repo
 chat_template: gemma

examples/gemma2/reward-model.yaml

@@ -5,7 +5,6 @@ num_labels: 1
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 reward_model: true
 chat_template: gemma

examples/gemma3/gemma-3-1b-qlora.yml

@@ -10,7 +10,6 @@ ddp_find_unused_parameters: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 # huggingface repo
 chat_template: gemma3

examples/gemma3/gemma-3-4b-qlora.yml

@@ -1,5 +1,4 @@
 base_model: google/gemma-3-4b-it
-strict: false
 
 load_in_4bit: true
 

examples/gemma3/gemma-3-4b-vision-qlora.yml

@@ -1,6 +1,5 @@
 base_model: google/gemma-3-4b-it
 processor_type: AutoProcessor
-strict: false
 
 load_in_4bit: true
 

examples/gptj/qlora.yml

@@ -4,7 +4,6 @@ base_model: EleutherAI/gpt-j-6b
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/jamba/qlora.yaml

@@ -6,7 +6,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/jamba/qlora_deepspeed.yaml

@@ -5,7 +5,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/jamba/qlora_fsdp_large.yaml

@@ -5,7 +5,6 @@ tokenizer_type: AutoTokenizer
 # hub_model_id: username/custom_model_name
 
 load_in_4bit: true
-strict: false
 use_tensorboard: true
 chat_template: jamba
 datasets:

examples/llama-2/fft_optimized.yml

@@ -4,7 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/llama-2/gptq-lora.yml

@@ -10,7 +10,6 @@ gptq_disable_exllama: true
 
 tokenizer_use_fast: true
 tokenizer_legacy: true
-strict: false
 push_dataset_to_hub:
 hf_use_auth_token: true
 datasets:

examples/llama-2/lisa.yml

@@ -4,7 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/llama-2/loftq.yml

@@ -4,7 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/llama-2/lora.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/llama-2/qlora-fsdp.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: yahma/alpaca-cleaned

examples/llama-2/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/llama-2/relora.yml

@@ -5,7 +5,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/llama-3-vision/lora-11b.yaml

@@ -4,7 +4,6 @@ processor_type: AutoProcessor
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
 
-strict: false
 
 # these 3 lines are needed for now to handle vision chat templates w images
 skip_prepare_dataset: true

examples/llama-3/fft-8b-liger-fsdp.yaml

@@ -9,7 +9,6 @@ liger_rms_norm: true
 liger_glu_activation: true
 liger_fused_linear_cross_entropy: true
 
-strict: false
 
 chat_template: llama3
 datasets:

examples/llama-3/fft-8b.yaml

@@ -1,7 +1,6 @@
 base_model: NousResearch/Meta-Llama-3.1-8B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/llama-3/instruct-dpo-lora-8b.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 chat_template: llama3
 rl: dpo

examples/llama-3/instruct-lora-8b.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 chat_template: llama3
 datasets:

examples/llama-3/lora-1b-deduplicate-dpo.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 chat_template: llama3
 rl: dpo

examples/llama-3/lora-1b-deduplicate-sft.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/llama-3/lora-1b-kernels.yml

@@ -1,7 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/llama-3/lora-1b-ray.yml

@@ -1,7 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/llama-3/lora-1b-sample-packing-sequentially.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/llama-3/lora-1b.yml

@@ -1,7 +1,6 @@
 base_model: NousResearch/Llama-3.2-1B
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/llama-3/lora-8b.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/llama-3/qlora-1b-kto.yaml

@@ -4,7 +4,6 @@ base_model: meta-llama/Llama-3.2-1B
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 rl: kto
 rl_beta: 0.5

examples/llama-3/qlora-1b.yml

@@ -4,7 +4,6 @@ base_model: NousResearch/Llama-3.2-1B
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/llama-3/qlora-fsdp-405b.yaml

@@ -5,7 +5,6 @@ tokenizer_type: AutoTokenizer
 # hub_model_id: username/custom_model_name
 
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/llama-3/qlora-fsdp-70b.yaml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/llama-3/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: aaditya/alpaca_subset_1

examples/llama-4/README.md

@@ -0,0 +1,16 @@
+# Llama 4 by Meta AI
+
+## Available Examples
+
+### Llama 4 Scout 17Bx16Experts (109B)
+- [Multi-Modal/Vision QLoRA w/ FSDP1](./scout-vision-qlora-fsdp.yaml)
+- [Text Single GPU (H100) QLoRA](./scout-qlora-single-h100.yaml)
+- [Text Multi GPU QLoRA w/ FSDP1](./scout-qlora-fsdp1.yaml)
+
+Our Single H100 implementation for Llama 4 Scout uses only 68.5GB VRAM for post-training with 4k context length @ 546 tokens/second. [WandB logs here](https://wandb.ai/axolotl-ai/llama4-sft/runs/zic56rhd)
+
+### Llama 4 Maverick 17Bx128Experts (400B)
+
+- [Text Multi GPU QLoRA w/FSDP1](./maverick-qlora-fsdp1.yaml)
+
+Our 4xH100 implementation for Llama 4 Maverick uses 79.5GB VRAM/GPU for post-training with 4k context length @ 206 tokens/second. [WandB logs here.](https://wandb.ai/axolotl-ai/llama-sft/runs/siyvwuxc?nw=nwuserwinglian)

examples/llama-4/maverick-qlora-fsdp1.yaml

@@ -0,0 +1,88 @@
+base_model: axolotl-quants/Llama-4-Maverick-17B-128E-Linearized-bnb-nf4-bf16
+model_type: Llama4ForConditionalGeneration
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+
+llama4_linearized_experts: true
+load_in_4bit: true
+adapter: qlora
+lora_r: 32
+lora_alpha: 64
+lora_target_modules:
+  - self_attn.q_proj
+  - self_attn.k_proj
+  - self_attn.v_proj
+  - self_attn.o_proj
+  - shared_expert.gate_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+  # - experts.gate_projs.[0-9]+$
+  # - experts.up_projs.[0-9]+$
+  # - experts.down_projs.[0-9]+$
+lora_modules_to_save:
+# - lm_head
+# - embed_tokens
+
+chat_template: llama4
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 1e-4
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+gradient_checkpointing: offload
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+
+warmup_steps: 20
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - auto_wrap
+  - full_shard
+fsdp_config:
+  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot|>

examples/llama-4/scout-qlora-fsdp1.yaml

@@ -1,13 +1,20 @@
-base_model: meta-llama/Llama-4-Scout-17B-16E
+base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
 model_type: Llama4ForConditionalGeneration
   # Automatically upload checkpoint and final model to HF
   # hub_model_id: username/custom_model_name
 
-strict: false
 
-  # torch_compile: true
+# torch_compile: true
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
 
-adapter: lora
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+
+llama4_linearized_experts: true
+load_in_4bit: true
+adapter: qlora
 lora_r: 32
 lora_alpha: 64
 lora_target_modules:
@@ -15,6 +22,12 @@ lora_target_modules:
   - self_attn.k_proj
   - self_attn.v_proj
   - self_attn.o_proj
+  - shared_expert.gate_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+    # - experts.gate_projs.[0-9]+$
+    # - experts.up_projs.[0-9]+$
+    # - experts.down_projs.[0-9]+$
 lora_modules_to_save:
   - lm_head
   - embed_tokens
@@ -37,38 +50,42 @@ sequence_len: 4096
 sample_packing: true
 pad_to_sequence_len: true
 
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
 gradient_accumulation_steps: 1
 micro_batch_size: 1
 num_epochs: 1
-optimizer: adamw_torch_8bit
+optimizer: adamw_torch_fused
 lr_scheduler: cosine
 learning_rate: 2e-5
 
 bf16: true
 tf32: true
 
-# gradient_checkpointing: true
-# gradient_checkpointing_kwargs:
-#   use_reentrant: false
 logging_steps: 1
 flash_attention: true
 
 warmup_steps: 100
-evals_per_epoch: 2
+evals_per_epoch: 1
 saves_per_epoch: 1
 weight_decay: 0.0
 fsdp:
   - auto_wrap
   - full_shard
 fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
+  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
   fsdp_cpu_ram_efficient_loading: true
   fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
-  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_state_dict_type: FULL_STATE_DICT
   fsdp_sharding_strategy: FULL_SHARD
-  fsdp_reshard_after_forward: true
   fsdp_activation_checkpointing: true
 special_tokens:
   pad_token: <|finetune_right_pad_id|>

examples/llama-4/scout-qlora-single-h100.yaml

@@ -0,0 +1,85 @@
+base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
+model_type: Llama4ForConditionalGeneration
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+
+llama4_linearized_experts: true
+load_in_4bit: true
+adapter: qlora
+lora_r: 32
+lora_alpha: 64
+lora_target_modules:
+  - self_attn.q_proj
+  - self_attn.k_proj
+  - self_attn.v_proj
+  - self_attn.o_proj
+  - shared_expert.gate_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+  # - experts.gate_projs.[0-9]+$
+  # - experts.up_projs.[0-9]+$
+  # - experts.down_projs.[0-9]+$
+lora_modules_to_save:
+  # - lm_head
+  # - embed_tokens
+
+lora_mlp_kernel: true
+lora_qkv_kernel: true
+lora_o_kernel: true
+
+chat_template: llama4
+datasets:
+  - path: mlabonne/FineTome-100k
+    type: chat_template
+    split: train[:20%]
+    field_messages: conversations
+    message_property_mappings:
+      role: from
+      content: value
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+sequence_len: 4096  # up to 8k will work on a single H100
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 1e-4
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+gradient_checkpointing: offload
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+
+warmup_steps: 20
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot|>

examples/llama-4/scout-vision-qlora-fsdp.yaml

@@ -0,0 +1,88 @@
+base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
+model_type: Llama4ForConditionalGeneration
+processor_type: Llama4Processor
+  # Automatically upload checkpoint and final model to HF
+  # hub_model_id: username/custom_model_name
+
+
+# these 3 lines are needed for now to handle vision chat templates w images
+skip_prepare_dataset: true
+remove_unused_columns: false
+sample_packing: false
+
+sequence_len: 4096
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_glu_activation: true
+liger_rms_norm: true
+liger_layer_norm: true
+
+llama4_linearized_experts: true  # use Axolotl's customized model
+load_in_4bit: true
+adapter: qlora
+lora_r: 32
+lora_alpha: 64
+lora_target_modules:
+  - self_attn.q_proj
+  - self_attn.k_proj
+  - self_attn.v_proj
+  - self_attn.o_proj
+  - shared_expert.gate_proj
+  - shared_expert.up_proj
+  - shared_expert.down_proj
+  - vision_adapter.mlp.fc1
+  - vision_adapter.mlp.fc2
+  # - experts.gate_projs.[0-9]+$
+  # - experts.up_projs.[0-9]+$
+  # - experts.down_projs.[0-9]+$
+lora_modules_to_save:
+  - lm_head
+  - embed_tokens
+
+chat_template: llama4
+datasets:
+  - path: HuggingFaceH4/llava-instruct-mix-vsft
+    type: chat_template
+    split: train[:1%]
+    field_messages: messages
+
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.0
+output_dir: ./outputs/out
+
+gradient_accumulation_steps: 1
+micro_batch_size: 1
+num_epochs: 1
+optimizer: adamw_torch_4bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+bf16: true
+tf32: true
+
+logging_steps: 1
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 1
+saves_per_epoch: 1
+weight_decay: 0.0
+fsdp:
+  - auto_wrap
+  - full_shard
+fsdp_config:
+  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: true
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_activation_checkpointing: true
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+  eos_token: <|eot|>

examples/llava/lora-7b.yaml

@@ -1,6 +1,5 @@
 base_model: llava-hf/llava-1.5-7b-hf
 processor_type: AutoProcessor
-strict: false
 
 # these 3 lines are needed for now to handle vision chat templates w images
 skip_prepare_dataset: true

examples/mamba/config.yml

@@ -5,7 +5,6 @@ tokenizer_type: AutoTokenizer
 tokenizer_config: EleutherAI/gpt-neox-20b
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/mistral/bigstral-ds-zero3.yaml

@@ -6,7 +6,6 @@ tokenizer_type: LlamaTokenizer
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-strict: false
 
 unfrozen_parameters:
   - ^lm_head.weight$

examples/mistral/config.yml

@@ -4,7 +4,6 @@ model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/mistral/lora-mps.yml

@@ -4,7 +4,6 @@ model_type: MistralForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/mistral/lora.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/mistral/mistral-dpo-qlora.yml

@@ -12,7 +12,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 chat_template: chatml
 rl: dpo

examples/mistral/mistral-qlora-fsdp.yml

@@ -9,7 +9,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/mistral/mistral-qlora-orpo.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 rl: orpo
 orpo_alpha: 0.1

examples/mistral/mistral-small-3.1-24B-lora.yml

@@ -1,6 +1,5 @@
 base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
 processor_type: AutoProcessor
-strict: false
 
 load_in_8bit: true
 

examples/mistral/mixtral-8x22b-qlora-fsdp.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/mistral/mixtral-qlora-fsdp.yml

@@ -9,7 +9,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/mistral/mixtral.yml

@@ -9,7 +9,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: tatsu-lab/alpaca

examples/mistral/mixtral_22.yml

@@ -6,7 +6,6 @@ tokenizer_type: LlamaTokenizer
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-strict: false
 
 unfrozen_parameters:
   - ^lm_head.weight$

examples/mistral/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/openllama-3b/config.yml

@@ -4,7 +4,6 @@ model_type: LlamaForCausalLM
 tokenizer_type: LlamaTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/openllama-3b/lora.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/openllama-3b/qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: LlamaTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 push_dataset_to_hub:
 datasets:
   - path: teknium/GPT4-LLM-Cleaned

examples/phi/lora-3.5.yaml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 chat_template: phi_3
 datasets:

examples/phi/phi-ft.yml

@@ -4,7 +4,6 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: garage-bAInd/Open-Platypus

examples/phi/phi-qlora.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: garage-bAInd/Open-Platypus

examples/phi/phi2-ft.yml

@@ -4,7 +4,6 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: garage-bAInd/Open-Platypus

examples/phi/phi3-ft-fsdp.yml

@@ -4,7 +4,6 @@ model_type: AutoModelForCausalLM
 tokenizer_type: AutoTokenizer
 # Automatically upload checkpoint and final model to HF
 # hub_model_id: username/custom_model_name
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/phi/phi3-ft.yml

@@ -7,7 +7,6 @@ tokenizer_type: AutoTokenizer
 # hub_model_id: username/custom_model_name
 
 chat_template: phi_3
-strict: false
 
 datasets:
   - path: garage-bAInd/Open-Platypus

examples/pixtral/lora-12b.yml

@@ -1,6 +1,5 @@
 base_model: mistral-community/pixtral-12b
 processor_type: AutoProcessor
-strict: false
 
 # these 3 lines are needed for now to handle vision chat templates w images
 skip_prepare_dataset: true

examples/qwen/lora.yml

@@ -9,7 +9,6 @@ trust_remote_code: true
 
 load_in_8bit: true
 load_in_4bit: false
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/qwen/qlora.yml

@@ -9,7 +9,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/qwen/qwen2-moe-lora.yaml

@@ -3,7 +3,6 @@ base_model: Qwen/Qwen1.5-MoE-A2.7B
 # hub_model_id: username/custom_model_name
 
 trust_remote_code: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test

examples/qwen/qwen2-moe-qlora.yaml

@@ -6,7 +6,6 @@ trust_remote_code: true
 
 load_in_8bit: false
 load_in_4bit: true
-strict: false
 
 datasets:
   - path: mhenrichsen/alpaca_2k_test