Update documentation to not pass config file paths

PREFLIGHT.md

@@ -7,12 +7,12 @@ Before you run ML workload on Multihost with GCE or GKE, simply apply `bash pref
 
 Here is an example for GCE:
 ```
-bash preflight.sh PLATFORM=GCE && python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GCE && python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 Here is an example for GKE:
 ```
-bash preflight.sh PLATFORM=GKE && python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GKE && python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 # Optimization 2: Numa binding (You can only apply this to v4 and v5p)
@@ -22,14 +22,14 @@ For GCE,
 [preflight.sh](https://github.com/google/maxtext/blob/main/preflight.sh) will help you install `numactl` dependency, so you can use it directly, here is an example:
 
 ```
-bash preflight.sh PLATFORM=GCE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GCE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 For GKE,
 `numactl` should be built into your docker image from [maxtext_tpu_dependencies.Dockerfile](https://github.com/google/maxtext/blob/main/src/dependencies/dockerfiles/maxtext_tpu_dependencies.Dockerfile), so you can use it directly if you built the maxtext docker image. Here is an example
 
 ```
-bash preflight.sh PLATFORM=GKE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${YOUR_JOB_NAME?}
+bash preflight.sh PLATFORM=GKE && numactl --membind 0 --cpunodebind=0 python3 -m maxtext.trainers.pre_train.train run_name=${YOUR_JOB_NAME?}
 ```
 
 1. `numactl`: This is the command-line tool used for controlling NUMA policy for processes or shared memory. It's particularly useful on multi-socket systems where memory locality can impact performance.

docs/guides/checkpointing_solutions/convert_checkpoint.md

@@ -70,7 +70,7 @@ Finally, run below command to complete the conversion
 # Optional: If run out of disk space when downloading HuggingFace safetensors,
 # customize your "HF_HOME" to redirect the cache to a larger or mounted disk (e.g., on a TPU VM).
 # export HF_HOME="/dev/shm/huggingface_tmp"
-python3 -m maxtext.checkpoint_conversion.to_maxtext maxtext/configs/base.yml \
+python3 -m maxtext.checkpoint_conversion.to_maxtext \
     model_name=${MODEL_NAME?} \
     hf_access_token=${HF_TOKEN?} \
     base_output_directory=${MODEL_CHECKPOINT_DIRECTORY?} \
@@ -108,7 +108,7 @@ Use the `to_huggingface.py` script to convert a MaxText checkpoint into the Hugg
 The following command converts a MaxText checkpoint and saves it locally, to GCS, or uploads it directly to the Hugging Face Hub.
 
 ```bash
-python3 -m maxtext.checkpoint_conversion.to_huggingface src/maxtext/configs/base.yml \
+python3 -m maxtext.checkpoint_conversion.to_huggingface \
     model_name=<MODEL_NAME> \
     load_parameters_path=<path-to-maxtext-checkpoint> \
     base_output_directory=<path-to-save-converted-checkpoint> \

docs/run_maxtext/run_maxtext_localhost.md

@@ -58,7 +58,7 @@ bash tools/setup/setup.sh DEVICE={tpu|gpu}
 After the installation is complete, run a short training job using synthetic data to confirm everything is working correctly. This command trains a model for just 10 steps. Remember to replace `$YOUR_JOB_NAME` with a unique name for your run and `gs://<my-bucket>` with the path to the GCS bucket you configured in the prerequisites.
 
 ```bash
-python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \
   dataset_type=synthetic \
@@ -72,7 +72,7 @@ python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
 To demonstrate model output, run the following command:
 
 ```bash
-python3 -m maxtext.inference.decode src/maxtext/configs/base.yml \
+python3 -m maxtext.inference.decode \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \
   per_device_batch_size=1
@@ -92,7 +92,7 @@ To use a pre-configured model for TPUs, you override the `model_name` parameter,
 <summary><strong>llama3-8b (TPU)</strong></summary>
 
 ```bash
-python3 -m maxtext.trainers.pre_train.train maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
   model_name=llama3-8b \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \
@@ -106,7 +106,7 @@ python3 -m maxtext.trainers.pre_train.train maxtext/configs/base.yml \
 <summary><strong>qwen3-4b (TPU)</strong></summary>
 
 ```bash
-python3 -m maxtext.trainers.pre_train.train maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
   model_name=qwen3-4b \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \

docs/run_maxtext/run_maxtext_single_host_gpu.md

@@ -148,7 +148,7 @@ Hardware: GPU
 ```
 
 ```bash
-python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=gpu01 base_output_directory=/deps/output  \
+python3 -m maxtext.trainers.pre_train.train run_name=gpu01 base_output_directory=/deps/output  \
   dataset_type=synthetic enable_checkpointing=True steps=10 attention=cudnn_flash_te scan_layers=False \
   use_iota_embed=True hardware=gpu per_device_batch_size=12
 ```

docs/run_maxtext/run_maxtext_via_multihost_job.md

@@ -68,7 +68,7 @@ The `multihost_job.py` script:
 
    ```sh
    RUN_NAME=${YOUR_JOB_NAME?} # You may set this to any unique name for a fresh run.
-   python3 multihost_job.py --NUM_SLICES=${NODE_COUNT?} --RUN_NAME=${RUN_NAME?} --BUCKET_NAME=${BUCKET_NAME?} --CQR_EXTRA_ARGS="--reserved" --COMMAND="bash tools/setup/setup.sh && python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${RUN_NAME?}"
+   python3 multihost_job.py --NUM_SLICES=${NODE_COUNT?} --RUN_NAME=${RUN_NAME?} --BUCKET_NAME=${BUCKET_NAME?} --CQR_EXTRA_ARGS="--reserved" --COMMAND="bash tools/setup/setup.sh && python3 -m maxtext.trainers.pre_train.train run_name=${RUN_NAME?}"
    ```
 
    We tell `multihost_job` to target the `reserved` pool by by including `--reserved` as extra arguments to the CQR request, but you may instead target the `on-demand` pool by removing the `--CQR_EXTRA_ARGS` flag (on-demand is default), or the pre-emptible pool with `--CQR_EXTRA_ARGS="--best-effort"`, which may be necessary if your reservation is full.

docs/run_maxtext/run_maxtext_via_multihost_runner.md

@@ -106,7 +106,7 @@ Although there are several steps below, most are for the initial setup. Once set
    Set config values for `base_output_directory` and `dataset_path` in `configs/base.yml` if not set already.
 
    ```
-   python3 multihost_runner.py --TPU_PREFIX=${TPU_PREFIX?} --COMMAND="python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${RUN_NAME?}"
+   python3 multihost_runner.py --TPU_PREFIX=${TPU_PREFIX?} --COMMAND="python3 -m maxtext.trainers.pre_train.train run_name=${RUN_NAME?}"
    ```
 
    If you are running the `multihost_runner.py` script from a TPUVM, you will need to set `--INTERNAL_IP=true`.

docs/run_maxtext/run_maxtext_via_pathways.md

@@ -96,7 +96,7 @@ xpk workload create-pathways \
   --project=${PROJECT?} \
   --zone=${ZONE?} \
   --docker-image=${DOCKER_IMAGE?} \
-  --command="python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
+  --command="python3 -m maxtext.trainers.pre_train.train \
     base_output_directory=gs://${BUCKET_NAME?} \
     per_device_batch_size=1 \
     enable_checkpointing=false \
@@ -154,7 +154,7 @@ export JAX_PLATFORMS=proxy
 export JAX_BACKEND_TARGET=grpc://127.0.0.1:29000
 
 # Run the training script
-python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
   base_output_directory=gs://${BUCKET_NAME?} \
   per_device_batch_size=1 \
   enable_checkpointing=false \

docs/run_maxtext/run_maxtext_via_xpk.md

@@ -187,7 +187,7 @@ For instance, to run a job across **four TPU slices**, you would change `--num-s
        --base-docker-image maxtext_base_image\
        --tpu-type v5litepod-256\
        --num-slices 1\
-       --command "python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${USER}-tpu-job base_output_directory=${BASE_OUTPUT_DIR?} dataset_path=${DATASET_PATH?} steps=100"
+       --command "python3 -m maxtext.trainers.pre_train.train run_name=${USER}-tpu-job base_output_directory=${BASE_OUTPUT_DIR?} dataset_path=${DATASET_PATH?} steps=100"
      ```
 
    - **On your GPU cluster:**
@@ -199,7 +199,7 @@ For instance, to run a job across **four TPU slices**, you would change `--num-s
        --base-docker-image maxtext_base_image\
        --device-type h100-80gb-8\
        --num-nodes 2\
-       --command "python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml run_name=${USER}-gpu-job base_output_directory=${BASE_OUTPUT_DIR?} dataset_path=${DATASET_PATH?} steps=100"
+       --command "python3 -m maxtext.trainers.pre_train.train run_name=${USER}-gpu-job base_output_directory=${BASE_OUTPUT_DIR?} dataset_path=${DATASET_PATH?} steps=100"
      ```
 
 ______________________________________________________________________

docs/tutorials/first_run.md

@@ -49,7 +49,7 @@ pre-commit install
 4. After installation completes, run training on synthetic data with the following command:
 
 ```sh
-python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \
   dataset_type=synthetic \
@@ -61,7 +61,7 @@ Optional: If you want to try training on a Hugging Face dataset, see [Data Input
 5. To demonstrate model output, run the following command:
 
 ```sh
-python3 -m maxtext.inference.decode src/maxtext/configs/base.yml \
+python3 -m maxtext.inference.decode \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \
   per_device_batch_size=1
@@ -83,7 +83,7 @@ You can use [demo_decoding.ipynb](https://github.com/AI-Hypercomputer/maxtext/bl
 2. After installation is complete, run training with the following command on synthetic data:
 
 ```sh
-python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \
   dataset_type=synthetic \
@@ -93,7 +93,7 @@ python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml \
 3. To demonstrate model output, run the following command:
 
 ```sh
-python3 -m maxtext.inference.decode src/maxtext/configs/base.yml \
+python3 -m maxtext.inference.decode \
   run_name=${YOUR_JOB_NAME?} \
   base_output_directory=gs://<my-bucket> \
   per_device_batch_size=1

docs/tutorials/posttraining/full_finetuning.md

@@ -101,7 +101,6 @@ Below is a sample training script.
 
 ```sh
 python3 -m maxtext.trainers.pre_train.train \
-  src/maxtext/configs/base.yml \
   run_name=${RUN_NAME?} \
   base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
   load_parameters_path=${MODEL_CKPT_PATH?} \

docs/tutorials/posttraining/knowledge_distillation.md

@@ -132,7 +132,7 @@ python3 -m pip install torch --index-url https://download.pytorch.org/whl/cpu
 export PRE_TRAINED_MODEL_CKPT_DIRECTORY=${BASE_DIRECTORY?}/llama3.1-8b-ckpt
 
 # Convert to MaxText format
-python3 -m maxtext.checkpoint_conversion.to_maxtext src/maxtext/configs/base.yml \
+python3 -m maxtext.checkpoint_conversion.to_maxtext \
     model_name=llama3.1-8b \
     hf_access_token=${HF_TOKEN?} \
     base_output_directory=${PRE_TRAINED_MODEL_CKPT_DIRECTORY?} \
@@ -170,7 +170,7 @@ You can now fine-tune your smaller student model using supervised fine-tuning te
 Example command to run fine-tuning on a TPU v6e-8:
 
 ```bash
-python3 -m maxtext.trainers.post_train.sft.train_sft_deprecated src/maxtext/configs/post_train/sft.yml \
+python3 -m maxtext.trainers.post_train.sft.train_sft_deprecated \
   run_name=${RUN_NAME?} \
   base_output_directory=${BASE_DIRECTORY?}/distillation/qwen3-32b-distill-llama3.1-8b \
   tokenizer_path=meta-llama/Llama-3.1-8B-Instruct tokenizer_type=huggingface \
@@ -209,7 +209,7 @@ largest_dir="${sorted_dirs[-1]}"
 FINE_TUNED_MODEL_CKPT_PATH=${CHECKPOINTS_PATH?}/${largest_dir}/model_params
 
 # Fine-tune student model on original dataset
-python3 -m maxtext.trainers.post_train.sft.train_sft src/maxtext/configs/post_train/sft.yml \
+python3 -m maxtext.trainers.post_train.sft.train_sft \
   run_name=${RUN_NAME?}_stage2 \
   base_output_directory=${BASE_DIRECTORY?}/distillation/qwen3-32b-distill-llama3.1-8b \
   tokenizer_path=meta-llama/Llama-3.1-8B-Instruct tokenizer_type=huggingface \

docs/tutorials/posttraining/multimodal.md

@@ -38,7 +38,7 @@ Then use this command to convert an unscanned checkpoint from HuggingFace to Max
 ```shell
 export HF_ACCESS_TOKEN=hf_...
 export MAXTEXT_CKPT_GCS_PATH=gs://...
-python -m maxtext.checkpoint_conversion.to_maxtext maxtext/configs/base.yml \
+python -m maxtext.checkpoint_conversion.to_maxtext \
     model_name=gemma3-4b \
     hf_access_token=${HF_ACCESS_TOKEN?} \
     base_output_directory=${MAXTEXT_CKPT_GCS_PATH?} \

docs/tutorials/posttraining/rl.md

@@ -133,7 +133,7 @@ export MAXTEXT_CKPT_PATH=<gcs path for MaxText checkpoint> # e.g., gs://my-bucke
 Run the following command for GRPO:
 
 ```
-python3 -m src.maxtext.trainers.post_train.rl.train_rl src/maxtext/configs/post_train/rl.yml \
+python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
   tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \
@@ -157,7 +157,7 @@ The overview of what this run will do is as follows:
 Run the following command for GSPO:
 
 ```
-python3 -m src.maxtext.trainers.post_train.rl.train_rl src/maxtext/configs/post_train/rl.yml \
+python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
   tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \

docs/tutorials/posttraining/rl_on_multi_host.md

@@ -196,7 +196,7 @@ xpk workload create-pathways --workload ${WORKLOAD?} \
 --tpu-type=${TPU_TYPE?} --num-slices=1 \
 --project=${PROJECT_ID?} --priority=high \
 --command "HF_TOKEN=${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 src.maxtext.trainers.post_train.rl.train_rl src/maxtext/configs/post_train/rl.yml \
+python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
   tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \
@@ -213,7 +213,7 @@ xpk workload create-pathways --workload ${WORKLOAD?} \
 --tpu-type=${TPU_TYPE?} --num-slices=1 \
 --project=${PROJECT_ID?} --priority=high \
 --command "HF_TOKEN=${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 src.maxtext.trainers.post_train.rl.train_rl src/maxtext/configs/post_train/rl.yml \
+python3 -m maxtext.trainers.post_train.rl.train_rl \
   model_name=${MODEL?} \
   tokenizer_path=${TOKENIZER?} \
   load_parameters_path=${MAXTEXT_CKPT_PATH?} \

docs/tutorials/posttraining/sft.md

@@ -88,7 +88,7 @@ export PRE_TRAINED_MODEL_CKPT_PATH=<gcs path for MaxText checkpoint> # e.g., gs:
 Now you are ready to run SFT using the following command:
 
 ```sh
-python3 -m maxtext.trainers.post_train.sft.train_sft src/maxtext/configs/post_train/sft.yml \
+python3 -m maxtext.trainers.post_train.sft.train_sft \
     run_name=${RUN_NAME?} \
     base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
     model_name=${PRE_TRAINED_MODEL?} \

docs/tutorials/posttraining/sft_on_multi_host.md

@@ -143,7 +143,7 @@ xpk workload create \
 --workload=${WORKLOAD_NAME?} \
 --tpu-type=${TPU_TYPE?} \
 --num-slices=${TPU_SLICE?} \
---command "python3 -m maxtext.trainers.post_train.sft.train_sft src/maxtext/configs/post_train/sft.yml run_name=${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?} 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?}"
 ```
 
 Once the fine-tuning is completed, you can access your model checkpoints at `$OUTPUT_PATH/$WORKLOAD_NAME/checkpoints`.
@@ -159,7 +159,7 @@ xpk workload create-pathways \
 --workload=${WORKLOAD_NAME?} \
 --tpu-type=${TPU_TYPE?} \
 --num-slices=${TPU_SLICE?} \
---command="JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE=1 python3 -m maxtext.trainers.post_train.sft.train_sft src/maxtext/configs/post_train/sft.yml run_name=${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=${HF_TOKEN?} tokenizer_path=${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"
 ```
 
 Once the fine-tuning is completed, you can access your model checkpoints at `$OUTPUT_PATH/$WORKLOAD_NAME/checkpoints`.

docs/tutorials/pretraining.md

@@ -35,7 +35,7 @@ We can use this **command** for pretraining:
 
 ```bash
 # replace base_output_directory with your bucket
-python3 -m maxtext.trainers.pre_train.train maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
 base_output_directory=gs://runner-maxtext-logs run_name=demo \
 model_name=deepseek2-16b per_device_batch_size=1 steps=10 max_target_length=2048 enable_checkpointing=false \
 dataset_type=hf hf_path=allenai/c4 hf_data_dir=en train_split=train \
@@ -102,7 +102,7 @@ This **command** shows pretraining with Grain pipeline, along with evaluation:
 
 ```bash
 # replace DATASET_GCS_BUCKET and base_output_directory with your buckets
-python3 -m maxtext.trainers.pre_train.train maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
 base_output_directory=gs://runner-maxtext-logs run_name=demo \
 model_name=deepseek2-16b per_device_batch_size=1 steps=10 max_target_length=2048 enable_checkpointing=false \
 dataset_type=grain grain_file_type=arrayrecord grain_train_files=/tmp/gcsfuse/array-record/c4/en/3.0.1/c4-train.array_record* grain_worker_count=2 \
@@ -139,7 +139,7 @@ This **command** shows pretraining with TFDS pipeline, along with evaluation:
 
 ```bash
 # replace base_output_directory and dataset_path with your buckets
-python3 -m maxtext.trainers.pre_train.train maxtext/configs/base.yml \
+python3 -m maxtext.trainers.pre_train.train \
 base_output_directory=gs://runner-maxtext-logs run_name=demo \
 model_name=deepseek2-16b per_device_batch_size=1 steps=10 max_target_length=2048 enable_checkpointing=false \
 dataset_type=tfds dataset_path=gs://maxtext-dataset dataset_name='c4/en:3.0.1' train_split=train \

src/maxtext/experimental/agent/ckpt_conversion_agent/README.md

@@ -66,7 +66,7 @@ If a ground-truth version isn't available, you'll need to debug the conversion m
 3. After the conversion is done, run a decode to check the correctness of the generated code.
 Example command:
 ```bash
-python3 -m maxtext.inference.decode src/maxtext/configs/base.yml model_name=gemma3-4b tokenizer_path=src/maxtext/assets/tokenizers/tokenizer.gemma3 \
+python3 -m maxtext.inference.decode model_name=gemma3-4b tokenizer_path=src/maxtext/assets/tokenizers/tokenizer.gemma3 \
   load_parameters_path=<Your-converted-ckpt-path> per_device_batch_size=1 run_name=ht_test \
   max_prefill_predict_length=8 max_target_length=16 steps=1 async_checkpointing=false scan_layers=true \
   prompt='I love to' attention='dot_product'