Merge pull request #3367 from AI-Hypercomputer:anisha-tokenizer_path

PiperOrigin-RevId: 883376052

Author: Google-ML-Automation

docs/guides/checkpointing_solutions/convert_checkpoint.md

@@ -221,7 +221,7 @@ To extend conversion support to a new model architecture, you must define its sp
 - In [`utils/param_mapping.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/MaxText/checkpoint_conversion/utils/param_mapping.py), add the `hook_fn` logic (`def {MODEL}_MAXTEXT_TO_HF_PARAM_HOOK_FN`). This is the transformation needed per layer.
 
 2. **Add Hugging Face weights Shape**: In [`utils/hf_shape.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/MaxText/checkpoint_conversion/utils/hf_shape.py), define the tensor shape of Hugging Face format (`def {MODEL}_HF_WEIGHTS_TO_SHAPE`). This is used to ensure the tensor shape is matched after to_huggingface conversion.
-3. **Register model key**: In [`utils/utils.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/MaxText/checkpoint_conversion/utils/utils.py), add the new model key in `HF_IDS`.
+3. **Register model key**: In [`utils/utils.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/maxtext/utils/globals.py), add the new model key in `HF_IDS`.
 4. **Add transformer config**: In [`utils/hf_model_configs.py`](https://github.com/AI-Hypercomputer/maxtext/blob/main/src/MaxText/checkpoint_conversion/utils/hf_model_configs.py), add the `transformers.Config` object, describing the Hugging Face model configuration (defined in [`src/maxtext/configs/models`](https://github.com/AI-Hypercomputer/maxtext/tree/main/src/maxtext/configs/models)). **Note**: This configuration must precisely match the MaxText model's architecture.
 
 Here is an example [PR to add support for gemma3 multi-modal model](https://github.com/AI-Hypercomputer/maxtext/pull/1983)

docs/tutorials/posttraining/rl.md

@@ -86,14 +86,17 @@ install_maxtext_tpu_post_train_extra_deps
 
 ## Setup environment variables
 
+Follow the instructions [here](https://huggingface.co/docs/huggingface_hub/v0.21.2/guides/cli) to login to Hugging Face using your access token using
+
+```bash
+huggingface-cli login
+```
+
 Setup following environment variables before running GRPO/GSPO:
 
 ```bash
 # -- Model configuration --
-export HF_MODEL=<Hugging Face Model> # e.g. 'llama3.1-8b-Instruct'
-export MODEL=<MaxText Model> # e.g. 'llama3.1-8b'
-export TOKENIZER=<Tokenizer> # e.g. 'meta-llama/Llama-3.1-8B-Instruct'
-export HF_TOKEN=<Hugging Face access token>
+export MODEL=<MaxText Model> # e.g. 'llama3.1-8b-Instruct'
 
 # -- MaxText configuration --
 export BASE_OUTPUT_DIRECTORY=<output directory to store run logs> # e.g., gs://my-bucket/my-output-directory
@@ -135,11 +138,9 @@ Run the following command for GRPO:
 ```
 python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
-  tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \
   run_name=${RUN_NAME?} \
   base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
-  hf_access_token=${HF_TOKEN?} \
   chips_per_vm=${CHIPS_PER_VM?}
 ```
 
@@ -159,11 +160,9 @@ Run the following command for GSPO:
 ```
 python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
-  tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \
   run_name=${RUN_NAME?} \
   base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
-  hf_access_token=${HF_TOKEN?} \
   loss_algo=gspo-token \
   chips_per_vm=${CHIPS_PER_VM?}
 ```

docs/tutorials/posttraining/rl_on_multi_host.md

@@ -69,9 +69,7 @@ actual values.
 
 ```bash
 # -- Model configuration --
-export HF_MODEL=<Hugging Face Model> # e.g. 'llama3.1-70b-Instruct'
-export MODEL=<MaxText Model> # e.g. 'llama3.1-70b'
-export TOKENIZER=<Tokenizer> # e.g. 'meta-llama/Llama-3.1-70B-Instruct'
+export MODEL=<MaxText Model> # e.g. 'llama3.1-70b-Instruct'
 export HF_TOKEN=<Hugging Face access token>
 
 # -- MaxText configuration --
@@ -200,7 +198,6 @@ xpk workload create-pathways --workload ${WORKLOAD?} \
 --command "HF_TOKEN=${HF_TOKEN?} TF_CPP_MIN_LOG_LEVEL=0 JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE='1' \
 python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
-  tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \
   run_name=${WORKLOAD?} \
   base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
@@ -218,7 +215,6 @@ xpk workload create-pathways --workload ${WORKLOAD?} \
 --command "HF_TOKEN=${HF_TOKEN?} TF_CPP_MIN_LOG_LEVEL=0 JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE='1' \
 python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
-  tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \
   run_name=${WORKLOAD?} \
   base_output_directory=${BASE_OUTPUT_DIRECTORY?} \

docs/tutorials/posttraining/sft.md

@@ -43,13 +43,17 @@ install_maxtext_tpu_post_train_extra_deps
 
 ## Setup environment variables
 
+Follow the instructions [here](https://huggingface.co/docs/huggingface_hub/v0.21.2/guides/cli) to login to Hugging Face using your access token using
+
+```bash
+huggingface-cli login
+```
+
 Set the following environment variables before running SFT.
 
 ```sh
 # -- Model configuration --
-export PRE_TRAINED_MODEL=<model name> # e.g., 'llama3.1-8b'
-export PRE_TRAINED_MODEL_TOKENIZER=<tokenizer path> # e.g., 'meta-llama/Llama-3.1-8B-Instruct'
-export HF_TOKEN=<Hugging Face access token>
+export PRE_TRAINED_MODEL=<model name> # e.g., 'llama3.1-8b-Instruct'
 
 # -- MaxText configuration --
 export BASE_OUTPUT_DIRECTORY=<output directory to store run logs> # e.g., gs://my-bucket/my-output-directory
@@ -93,8 +97,6 @@ python3 -m maxtext.trainers.post_train.sft.train_sft \
     base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
     model_name=${PRE_TRAINED_MODEL?} \
     load_parameters_path=${PRE_TRAINED_MODEL_CKPT_PATH?} \
-    hf_access_token=${HF_TOKEN?} \
-    tokenizer_path=${PRE_TRAINED_MODEL_TOKENIZER?} \
     per_device_batch_size=${PER_DEVICE_BATCH_SIZE?} \
     steps=${STEPS?} \
     hf_path=${DATASET_NAME?} \

docs/tutorials/posttraining/sft_on_multi_host.md

@@ -95,7 +95,6 @@ export HF_TOKEN=<Hugging Face Access Token>
 
 # -- Model Configuration --
 export MODEL_NAME=<Model Name> # e.g., deepseek3-671b
-export TOKENIZER_PATH=<Model Tokenizer> # e.g., deepseek-ai/DeepSeek-V3
 
 # -- Dataset configuration --
 export DATASET_NAME=<Hugging Face Dataset Name> # e.g., HuggingFaceH4/ultrachat_200k
@@ -143,7 +142,7 @@ xpk workload create \
 --workload=${WORKLOAD_NAME?} \
 --tpu-type=${TPU_TYPE?} \
 --num-slices=${TPU_SLICE?} \
---command "python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL_NAME?} load_parameters_path=${MODEL_CHECKPOINT_PATH?} hf_access_token=${HF_TOKEN?} tokenizer_path=${TOKENIZER_PATH?} per_device_batch_size=1 steps=${STEPS?} profiler=xplane hf_path=${DATASET_NAME?} train_split=${TRAIN_SPLIT?} train_data_columns=${TRAIN_DATA_COLUMNS?}"
+--command "python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL_NAME?} load_parameters_path=${MODEL_CHECKPOINT_PATH?} hf_access_token=${HF_TOKEN?}  per_device_batch_size=1 steps=${STEPS?} profiler=xplane hf_path=${DATASET_NAME?} train_split=${TRAIN_SPLIT?} train_data_columns=${TRAIN_DATA_COLUMNS?}"
 ```
 
 Once the fine-tuning is completed, you can access your model checkpoints at `$OUTPUT_PATH/$WORKLOAD_NAME/checkpoints`.
@@ -159,7 +158,7 @@ xpk workload create-pathways \
 --workload=${WORKLOAD_NAME?} \
 --tpu-type=${TPU_TYPE?} \
 --num-slices=${TPU_SLICE?} \
---command="JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE=1 python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL_NAME?} load_parameters_path=${MODEL_CHECKPOINT_PATH?} hf_access_token=${HF_TOKEN?} tokenizer_path=${TOKENIZER_PATH?} per_device_batch_size=1 steps=${STEPS?} profiler=xplane checkpoint_storage_use_zarr3=False checkpoint_storage_use_ocdbt=False enable_single_controller=True"
+--command="JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE=1 python3 -m maxtext.trainers.post_train.sft.train_sft run_name=${WORKLOAD_NAME?} base_output_directory=${OUTPUT_PATH?} model_name=${MODEL_NAME?} load_parameters_path=${MODEL_CHECKPOINT_PATH?} hf_access_token=${HF_TOKEN?} per_device_batch_size=1 steps=${STEPS?} profiler=xplane checkpoint_storage_use_zarr3=False checkpoint_storage_use_ocdbt=False enable_single_controller=True"
 ```
 
 Once the fine-tuning is completed, you can access your model checkpoints at `$OUTPUT_PATH/$WORKLOAD_NAME/checkpoints`.

src/maxtext/checkpoint_conversion/compare_hf_ckpt.py

@@ -48,8 +48,9 @@
 from safetensors import safe_open
 
 from maxtext.configs import pyconfig
-from maxtext.checkpoint_conversion.utils.utils import HF_IDS, print_ram_usage, get_hf_model
+from maxtext.checkpoint_conversion.utils.utils import print_ram_usage, get_hf_model
 from maxtext.utils import max_logging
+from maxtext.utils.globals import HF_IDS
 
 
 jax.config.update("jax_platform_name", "cpu")

src/maxtext/checkpoint_conversion/to_huggingface.py

@@ -19,7 +19,7 @@
 
 Key Parameters (to be set in the config file or as command-line overrides):
   model_name: (Required) The name of the model to convert (e.g., "gemma2-2b").
-              Must be a key in `maxtext.checkpoint_conversion.utils.utils.HF_IDS`.
+              Must be a key in `maxtext.utils.globals.HF_IDS`.
   load_parameters_path: (Required) Path to the MaxText checkpoint directory
                         containing the parameter-only checkpoint.
   base_output_directory: (Optional) The directory where the converted HuggingFace
@@ -79,12 +79,13 @@
     save_model_files,
     load_orbax_checkpoint,
     detect_and_extract_checkpoint,
-    HF_IDS,
     MemoryMonitorTqdm,
     print_peak_memory,
 )
 from maxtext.utils import max_logging
 from maxtext.utils import max_utils
+from maxtext.utils.globals import HF_IDS
+
 
 flags.DEFINE_bool(
     "override_model_architecture",

src/maxtext/checkpoint_conversion/to_maxtext.py

@@ -18,7 +18,7 @@
 
 Key Parameters (to be set in the config file or as command-line overrides):
   model_name: (Required) The name of the model to convert (e.g., "gemma2-2b").
-              Must be a key in `maxtext.checkpoint_conversion.utils.utils.HF_IDS`.
+              Must be a key in `maxtext.utils.globals.HF_IDS`.
   base_output_directory: (Optional) The directory where the converted HuggingFace
                          checkpoint will be saved. Can be a local path, a GCS
                          path (gs://...), or a HuggingFace Hub repo ID (hf://...).
@@ -30,7 +30,7 @@
              Defaults to False.
   --hf_model_path: (Optional) Specifies a local or remote directory containing the model weights.
       If unspecified, we use the default Hugging Face repository ID
-      (e.g., openai/gpt-oss-20b; see `HF_IDS[model_name]` in `utils/ckpt_conversion/utils`).
+      (e.g., openai/gpt-oss-20b; see `HF_IDS[model_name]` in `maxtext.utils.globals`).
       This is necessary for locally dequantized models like GPT-OSS or DeepSeek.
 
 Environment Variables:
@@ -74,11 +74,12 @@
 from maxtext.common.common_types import MODEL_MODE_TRAIN
 from maxtext.checkpoint_conversion.standalone_scripts.llama_or_mistral_ckpt import save_weights_to_checkpoint
 from maxtext.checkpoint_conversion.utils.param_mapping import HOOK_FNS, PARAM_MAPPING
-from maxtext.checkpoint_conversion.utils.utils import HF_IDS, MemoryMonitorTqdm, apply_hook_fns, get_hf_model, print_peak_memory, print_ram_usage, validate_and_filter_param_map_keys
+from maxtext.checkpoint_conversion.utils.utils import MemoryMonitorTqdm, apply_hook_fns, get_hf_model, print_peak_memory, print_ram_usage, validate_and_filter_param_map_keys
 from maxtext.inference.inference_utils import str2bool
 from maxtext.layers import quantizations
 from maxtext.models import models
 from maxtext.utils import max_logging, max_utils, maxtext_utils
+from maxtext.utils.globals import HF_IDS
 import numpy as np
 from orbax.checkpoint import type_handlers
 from safetensors import safe_open
@@ -155,7 +156,12 @@ def _initialize_index(self):
     if self.is_local:
       index_path = os.path.join(self.model_id, index_file)
     else:
-      index_path = hf_hub_download(repo_id=self.model_id, filename=index_file, token=self.token, revision=self.revision)
+      index_path = hf_hub_download(
+          repo_id=self.model_id,
+          filename=index_file,
+          token=self.token,
+          revision=self.revision,
+      )
     with open(index_path, "r", encoding="utf-8") as f:
       index_data = json.load(f)
     self.shard_map = index_data["weight_map"]
@@ -185,7 +191,12 @@ def get_tensor(self, key: str) -> np.ndarray:
     else:
       # STEP 1: Download outside the lock.
       # multiple threads can download different shards at the same time.
-      local_path = hf_hub_download(repo_id=self.model_id, filename=shard_name, token=self.token, revision=self.revision)
+      local_path = hf_hub_download(
+          repo_id=self.model_id,
+          filename=shard_name,
+          token=self.token,
+          revision=self.revision,
+      )
 
     # STEP 2: Lock ONLY the reading into RAM.
     # This prevents multiple threads from simultaneously allocating large chunks of RAM.
@@ -200,7 +211,13 @@ class LazyTensor:
   and transformation until __array__ is called (e.g., by Orbax during save).
   """
 
-  def __init__(self, load_fn: Callable[[], np.ndarray], shape: tuple, dtype, name: str = "unknown"):
+  def __init__(
+      self,
+      load_fn: Callable[[], np.ndarray],
+      shape: tuple,
+      dtype,
+      name: str = "unknown",
+  ):
     self._load_fn = load_fn
     self.shape = shape
     self.dtype = np.dtype(dtype)
@@ -421,7 +438,13 @@ def _get_hf_loading_function(hf_source_keys_or_key, tensor_getter, hook_fn, mt_t
     def _loader(getter, key, shape, hook):
       return apply_hook_fns(getter(key), shape, hook)
 
-    load_fn = partial(_loader, tensor_getter, hf_source_keys_or_key, mt_target_shape_or_shapes, hook_fn)
+    load_fn = partial(
+        _loader,
+        tensor_getter,
+        hf_source_keys_or_key,
+        mt_target_shape_or_shapes,
+        hook_fn,
+    )
   # Stacked mapping
   elif not isinstance(hf_source_keys_or_key[0], list):
     # Case 2 or 3: Single-Axis Stacked hf keys (un-nested list)
@@ -516,7 +539,12 @@ def _get_maxtext_weight(
     # to load the tensor later (the `load_fn`, shape, dtype).
     # The actual data will only be loaded when Orbax calls `__array__`
     # on this object during the saving process.
-    final_mt_tensor_numpy = LazyTensor(load_fn, mt_target_shape_or_shapes, config.weight_dtype, name=mt_param_key_or_keys)
+    final_mt_tensor_numpy = LazyTensor(
+        load_fn,
+        mt_target_shape_or_shapes,
+        config.weight_dtype,
+        name=mt_param_key_or_keys,
+    )
     if not is_composite_mt_key:
       # Case 2.1: Lazy mode, `atomic_mt_key`
       final_mt_weights[mt_target_idx_or_indices] = final_mt_tensor_numpy
@@ -562,7 +590,10 @@ def main(
 
   # check the supported model ids
   if model_name_original not in HF_IDS:
-    raise ValueError(f"Unsupported model name: {model_name_original}. Supported models are: {list(HF_IDS.keys())}")
+    raise ValueError(
+        f"Unsupported model name: {model_name_original}.\
+                      Supported models are: {list(HF_IDS.keys())}"
+    )
 
   model_id = hf_model_path or HF_IDS[model_name_original]
 
@@ -633,7 +664,11 @@ def _eager_getter(key):
   filtered_map_keys = validate_and_filter_param_map_keys(param_map_mt_to_hf.keys(), maxtext_abstract_dict.keys())
 
   for mt_param_key_or_keys in MemoryMonitorTqdm(
-      filtered_map_keys, desc="Transforming weights", unit="param", leave=True, dynamic_ncols=True
+      filtered_map_keys,
+      desc="Transforming weights",
+      unit="param",
+      leave=True,
+      dynamic_ncols=True,
   ):
     if not lazy_load_tensors:
       max_logging.log(f"maxtext param: {mt_param_key_or_keys}")
@@ -651,7 +686,13 @@ def _eager_getter(key):
 
     # Step 2: Determine the loading function for hf key
     # based on hf_key form (unscanned, scanned, unscanned with expert stacking, or scanned with expert stacking)
-    load_fn = _get_hf_loading_function(hf_source_keys_or_key, tensor_getter, hook_fn, mt_target_shape_or_shapes, config)
+    load_fn = _get_hf_loading_function(
+        hf_source_keys_or_key,
+        tensor_getter,
+        hook_fn,
+        mt_target_shape_or_shapes,
+        config,
+    )
 
     # Step 3: Load hf keys and convert to maxtext keys
     # based on tensor load mode (lazy, eager) and MaxText key form (`atomic_mt_key` or `composite_mt_key`)
@@ -710,9 +751,13 @@ def _eager_getter(key):
       default=False,
       help="Whether to use lazy loading of HF tensors.",
   )
-  # If not specified, default to maxtext.checkpoint_conversion.utils.utils.HF_IDS[model_name]
+  # If not specified, default to maxtext.utils.globals.HF_IDS[model_name]
   parser.add_argument(
-      "--hf_model_path", type=str, required=False, default="", help="local path to hf model, or custom remote hf repo"
+      "--hf_model_path",
+      type=str,
+      required=False,
+      default="",
+      help="local path to hf model, or custom remote hf repo",
   )
   # Determines the logical sharding of the output checkpoint by partitioning
   # weights across virtual XLA devices.
@@ -730,7 +775,11 @@ def _eager_getter(key):
   parser.add_argument("--simulated_cpu_devices_count", type=int, required=False, default=16)
 
   parser.add_argument(
-      "--revision", type=str, required=False, default=None, help="Specific Hugging Face revision (branch/tag/commit)"
+      "--revision",
+      type=str,
+      required=False,
+      default=None,
+      help="Specific Hugging Face revision (branch/tag/commit)",
   )
 
   # Parse local arguments

src/maxtext/checkpoint_conversion/utils/utils.py

@@ -56,41 +56,6 @@
 DEFAULT_MAX_SHARD_SIZE = 1024 * 1024 * 1024 * 3  # 3GB default
 
 
-# Mapping from MaxText model key to Hugging Face tokenizer identifiers
-HF_IDS = {
-    "gemma2-2b": "google/gemma-2-2b",
-    "gemma2-9b": "google/gemma-2-9b",
-    "gemma2-27b": "google/gemma-2-27b",
-    "gemma3-4b": "google/gemma-3-4b-it",  # hf multi-modal should also support the pure-text
-    "gemma3-12b": "google/gemma-3-12b-it",
-    "gemma3-27b": "google/gemma-3-27b-it",
-    "qwen3-0.6b": "Qwen/Qwen3-0.6B",
-    "qwen3-4b": "Qwen/Qwen3-4B",
-    "qwen3-4b-thinking-2507": "Qwen/Qwen3-4B-Thinking-2507",
-    "qwen3-8b": "Qwen/Qwen3-8B",
-    "qwen3-14b": "Qwen/Qwen3-14B",
-    "qwen3-32b": "Qwen/Qwen3-32B",
-    "llama3.1-8b": "meta-llama/Llama-3.1-8B",
-    "llama3.1-8b-Instruct": "meta-llama/Llama-3.1-8B-Instruct",
-    "llama3.1-70b-Instruct": "meta-llama/Llama-3.1-70B-Instruct",
-    "llama3.1-70b": "meta-llama/Llama-3.1-70B",
-    "llama3.1-405b": "meta-llama/Llama-3.1-405B",
-    "qwen3-30b-a3b": "Qwen/Qwen3-30B-A3B-Thinking-2507",
-    "qwen3-235b-a22b": "Qwen/Qwen3-235B-A22B-Thinking-2507",
-    "qwen3-480b-a35b": "Qwen/Qwen3-Coder-480B-A35B-Instruct",
-    "deepseek3-671b": "deepseek-ai/DeepSeek-V3",
-    "gpt-oss-20b": "openai/gpt-oss-20b",
-    "gpt-oss-120b": "openai/gpt-oss-120b",
-    "qwen3-omni-30b-a3b": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
-    "qwen3-next-80b-a3b": "Qwen/Qwen3-Next-80B-A3B-Instruct",
-    "mixtral-8x7b": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "mixtral-8x22b": "mistralai/Mixtral-8x22B-Instruct-v0.1",
-    "olmo3-7b": "allenai/Olmo-3-7B-Instruct",
-    "olmo3-7b-pt": "allenai/Olmo-3-1025-7B",
-    "olmo3-32b": "allenai/Olmo-3-32B-Think",
-}
-
-
 def _get_local_directory(output_dir: str) -> str:
   """Determines the local directory for saving files."""
   if output_dir.startswith("gs://") or output_dir.startswith("hf://"):

src/maxtext/configs/base.yml

@@ -573,7 +573,7 @@ num_vocab_tiling: 1
 
 # Tokenizer
 vocab_size: 32_000 # powers of 2 for sharding
-tokenizer_path: "src/maxtext/assets/tokenizers/tokenizer.llama2"
+tokenizer_path: ""
 # tfds pipeline supports tokenizer_type: sentencepiece, huggingface, tiktoken
 # grain pipeline supports tokenizer_type: sentencepiece, huggingface
 # hf pipeline only supports huggingface type, and will ignore tokenizer_type flag