Add ds model card, rebased (#2101) [skip ci]

* rebased add_ds_model_card

* manual rebasing

* fix redundancy

* lint

* include case when ds_tag is none

* conform to kwargs in create_model_card

src/axolotl/core/trainer_builder.py

@@ -107,6 +107,22 @@ def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
     return kwargs
 
 
+def _sanitize_kwargs_for_ds_tagging(dataset_tags, kwargs=None):
+    if isinstance(dataset_tags, str):
+        dataset_tags = [dataset_tags]
+
+    if (dataset_tags is not None) and (kwargs is not None):
+        if "dataset_tags" not in kwargs:
+            kwargs["dataset_tags"] = dataset_tags
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], list):
+            kwargs["dataset_tags"].extend(dataset_tags)
+        elif "dataset_tags" in kwargs and isinstance(kwargs["dataset_tags"], str):
+            dataset_tags.append(kwargs["dataset_tags"])
+            kwargs["dataset_tags"] = dataset_tags
+
+    return kwargs
+
+
 @dataclass
 class AxolotlTrainingMixins:
     """
@@ -418,10 +434,12 @@ def __init__(
         *_args,
         bench_data_collator=None,
         eval_data_collator=None,
+        dataset_tags=None,
         **kwargs,
     ):
         self.bench_data_collator = bench_data_collator
         self.eval_data_collator = eval_data_collator
+        self.dataset_tags = dataset_tags
         super().__init__(*_args, **kwargs)
         self.train_data_collator = self.data_collator
         self._stored_metrics = defaultdict(lambda: defaultdict(list))
@@ -919,6 +937,9 @@ def push_to_hub(self, *args, **kwargs) -> str:
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
+        kwargs = _sanitize_kwargs_for_ds_tagging(
+            dataset_tags=self.dataset_tags, kwargs=kwargs
+        )
         kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
@@ -1042,8 +1063,9 @@ class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
 
     tag_names = ["axolotl", "dpo"]
 
-    def __init__(self, *args, **kwargs):
+    def __init__(self, *args, dataset_tags=None, **kwargs):
         super().__init__(*args, **kwargs)
+        self.dataset_tags = dataset_tags
         self.optimizer = None
 
     def create_optimizer(self):
@@ -1082,6 +1104,9 @@ def push_to_hub(self, *args, **kwargs) -> str:
         Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
         model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
         """
+        kwargs = _sanitize_kwargs_for_ds_tagging(
+            dataset_tags=self.dataset_tags, kwargs=kwargs
+        )
         kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
 
         return super().push_to_hub(*args, **kwargs)
@@ -1806,6 +1831,10 @@ def build(self, total_num_steps):
         else:
             trainer_kwargs["tokenizer"] = self.tokenizer
 
+        if (trainer_cls is not AxolotlRewardTrainer) and self.cfg.datasets is not None:
+            trainer_kwargs["dataset_tags"] = [
+                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
+            ]
         trainer = trainer_cls(
             model=self.model,
             train_dataset=self.train_dataset,
@@ -2079,6 +2108,10 @@ def build(self, total_num_steps):
         else:
             dpo_trainer_kwargs["tokenizer"] = self.tokenizer
 
+        if self.cfg.datasets is not None and (trainer_cls is AxolotlDPOTrainer):
+            dpo_trainer_kwargs["dataset_tags"] = [
+                d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
+            ]
         dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,

src/axolotl/train.py

@@ -259,11 +259,31 @@ def terminate_handler(_, __, model_weakref):
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
     if not cfg.hub_model_id:
+        from huggingface_hub import HfApi
+        from huggingface_hub.utils import RepositoryNotFoundError
+
         try:
-            trainer.create_model_card(
-                model_name=cfg.output_dir.lstrip("./").encode("utf-8").decode("utf-8")
-            )
-        except (AttributeError, UnicodeDecodeError):
+            # Check to make sure the base model is from HuggingFace not a local directory
+            hf_api = HfApi()
+            hf_api.model_info(cfg.base_model)
+
+            model_card_kwarg = {
+                "model_name": cfg.output_dir.lstrip("./")
+                .encode("utf-8")
+                .decode("utf-8")
+            }
+            if cfg.datasets is not None:
+                if cfg.rl is not None or cfg.reward_model:
+                    model_card_kwarg["dataset_name"] = [
+                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
+                    ]
+                else:
+                    model_card_kwarg["dataset_tags"] = [
+                        d["path"] for d in cfg.datasets if not Path(d["path"]).is_dir()
+                    ]
+
+            trainer.create_model_card(**model_card_kwarg)
+        except (AttributeError, UnicodeDecodeError, RepositoryNotFoundError):
             pass
     elif cfg.hub_model_id:
         # defensively push to the hub to ensure the model card is updated