diff --git a/examples/train_gpt2/README.md b/examples/train_gpt2/README.md
index c50591ee4..f1025343e 100644
--- a/examples/train_gpt2/README.md
+++ b/examples/train_gpt2/README.md
@@ -3,3 +3,9 @@
 This Tango example showcases how you could fine-tune GPT2 from [transformers](https://github.com/huggingface/transformers) on WikiText2 or a similar dataset.
 It's best that you run this experiment on a machine with a GPU and PyTorch [properly installed](https://pytorch.org/get-started/locally/#start-locally),
 otherwise Tango will fall back to CPU-only and it will be extremely slow.
+
+You can kick off a training job with the following command:
+
+```
+$ tango run config.jsonnet
+```
diff --git a/examples/train_gpt2/config.jsonnet b/examples/train_gpt2/config.jsonnet
index cbf4743d3..9f3824cff 100644
--- a/examples/train_gpt2/config.jsonnet
+++ b/examples/train_gpt2/config.jsonnet
@@ -1,76 +1,76 @@
-local pretrained_model = "gpt2";
+local pretrained_model = 'gpt2';
 local training_steps = 200;
 local warmup_steps = 20;
 local batch_size = 8;
 local validate_every = 20;
-local distributed = false;  # Set to `true` to train on 2 (or more) GPUs.
+local distributed = false;  // Set to `true` to train on 2 (or more) GPUs.
 local devices = if distributed then 2 else 1;
 local grad_accum = if distributed then 2 else 4;
 
 local distributed_dataloader = {
-    "batch_size": batch_size,
-    "collate_fn": {"type": "transformers_default"},
-    "sampler": {
-        "type": "torch::DistributedSampler",
-        "shuffle": true,
-        "drop_last": true,
-    }
+  batch_size: batch_size,
+  collate_fn: { type: 'transformers_default' },
+  sampler: {
+    type: 'torch::DistributedSampler',
+    shuffle: true,
+    drop_last: true,
+  },
 };
 
 local single_device_dataloader = {
-    "shuffle": true,
-    "batch_size": batch_size,
-    "collate_fn": {"type": "transformers_default"},
+  shuffle: true,
+  batch_size: batch_size,
+  collate_fn: { type: 'transformers_default' },
 };
 
 local dataloader = if distributed then distributed_dataloader else single_device_dataloader;
 
 {
-    "steps": {
-        "raw_data": {
-            "type": "datasets::load",
-            "path": "wikitext",
-            "name": "wikitext-2-raw-v1",
-        },
-        "tokenized_data": {
-            "type": "tokenize_data",
-            "dataset": {"type": "ref", "ref": "raw_data"},
-            "pretrained_model_name": pretrained_model,
-        },
-        "trained_model": {
-            "type": "torch::train",
-            "model": {
-                "type": "gpt2",
-                "pretrained_model_name_or_path": pretrained_model,
-            },
-            "dataset_dict": {"type": "ref", "ref": "tokenized_data"},
-            "train_dataloader": dataloader,
-            "validation_split": "validation",
-            "optimizer": {
-                "type": "transformers_adamw",
-                "lr": 0.0007,
-                "betas": [0.9, 0.95],
-                "eps": 1e-6,
-                "correct_bias": false,
-            },
-            "lr_scheduler": {
-                "type": "linear_with_warmup",
-                "num_warmup_steps": warmup_steps,
-                "num_training_steps": training_steps,
-            },
-            "grad_accum": grad_accum,
-            "train_steps": training_steps,
-            "validate_every": validate_every,
-            "checkpoint_every": validate_every,
-            "log_every": 1,
-            "device_count": devices,
-        }
-        "final_metrics": {
-            "type": "torch::eval",
-            "model": {"type": "ref", "ref": "trained_model"},
-            "dataset_dict": {"type": "ref", "ref": "tokenized_data"},
-            "dataloader": single_device_dataloader,
-            "test_split": "test",
-        },
-    }
+  steps: {
+    raw_data: {
+      type: 'datasets::load',
+      path: 'wikitext',
+      name: 'wikitext-2-raw-v1',
+    },
+    tokenized_data: {
+      type: 'tokenize_data',
+      dataset: { type: 'ref', ref: 'raw_data' },
+      pretrained_model_name: pretrained_model,
+    },
+    trained_model: {
+      type: 'torch::train',
+      model: {
+        type: 'gpt2',
+        pretrained_model_name_or_path: pretrained_model,
+      },
+      dataset_dict: { type: 'ref', ref: 'tokenized_data' },
+      train_dataloader: dataloader,
+      validation_split: 'validation',
+      optimizer: {
+        type: 'transformers_adamw',
+        lr: 0.0007,
+        betas: [0.9, 0.95],
+        eps: 1e-6,
+        correct_bias: false,
+      },
+      lr_scheduler: {
+        type: 'linear_with_warmup',
+        num_warmup_steps: warmup_steps,
+        num_training_steps: training_steps,
+      },
+      grad_accum: grad_accum,
+      train_steps: training_steps,
+      validate_every: validate_every,
+      checkpoint_every: validate_every,
+      log_every: 1,
+      device_count: devices,
+    },
+    final_metrics: {
+      type: 'torch::eval',
+      model: { type: 'ref', ref: 'trained_model' },
+      dataset_dict: { type: 'ref', ref: 'tokenized_data' },
+      dataloader: single_device_dataloader,
+      test_split: 'test',
+    },
+  },
 }