support modelscope models and datasets (#1481)

* support modelscope

* change modelscope args

* remove useless import

* remove useless import

* fix

* wip

* fix

* remove useless code

* add readme

* add some comments

* change print to raise error

* update comment

* Update loader.py

---------

Co-authored-by: Daniel Han <danielhanchen@gmail.com>

README.md

@@ -212,6 +212,9 @@ For **advanced installation instructions** or if you see weird errors during ins
 - Go to our official [Documentation](https://docs.unsloth.ai) for saving to GGUF, checkpointing, evaluation and more!
 - We support Huggingface's TRL, Trainer, Seq2SeqTrainer or even Pytorch code!
 - We're in 🤗Hugging Face's official docs! Check out the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
+- If you want to download models from the ModelScope community, please use an environment variable: `UNSLOTH_USE_MODELSCOPE=1`, and install the modelscope library by: `pip install modelscope -U`.
+
+> unsloth_cli.py also supports `UNSLOTH_USE_MODELSCOPE=1` to download models and datasets. please remember to use the model and dataset id in the ModelScope community.
 
 ```python
 from unsloth import FastLanguageModel 

unsloth-cli.py

@@ -30,11 +30,14 @@ Happy fine-tuning!
 """
 
 import argparse
+import os
+
 
 def run(args):
     import torch
     from unsloth import FastLanguageModel
     from datasets import load_dataset
+    from transformers.utils import strtobool
     from trl import SFTTrainer
     from transformers import TrainingArguments
     from unsloth import is_bfloat16_supported
@@ -86,8 +89,13 @@ def run(args):
             texts.append(text)
         return {"text": texts}
 
-    # Load and format dataset
-    dataset = load_dataset(args.dataset, split="train")
+    use_modelscope = strtobool(os.environ.get('UNSLOTH_USE_MODELSCOPE', 'False'))
+    if use_modelscope:
+        from modelscope import MsDataset
+        dataset = MsDataset.load(args.dataset, split="train")
+    else:
+        # Load and format dataset
+        dataset = load_dataset(args.dataset, split="train")
     dataset = dataset.map(formatting_prompts_func, batched=True)
     print("Data is formatted and ready!")
 

unsloth/models/loader.py

@@ -31,6 +31,15 @@ except:
 pass
 from huggingface_hub import HfFileSystem
 
+# [TODO] Move USE_MODELSCOPE to utils
+USE_MODELSCOPE = os.environ.get("UNSLOTH_USE_MODELSCOPE", "0") == "1"
+if USE_MODELSCOPE:
+    import importlib
+    if importlib.util.find_spec("modelscope") is None:
+        raise ImportError(f'You are using the modelscope hub, please install modelscope by `pip install modelscope -U`')
+    pass
+pass
+
 # https://github.com/huggingface/transformers/pull/26037 allows 4 bit loading!
 from unsloth_zoo.utils import Version, _get_dtype
 transformers_version = Version(transformers_version)
@@ -72,6 +81,11 @@ class FastLanguageModel(FastLlamaModel):
         if not use_exact_model_name:
             model_name = get_model_name(model_name, load_in_4bit)
 
+        if USE_MODELSCOPE and not os.path.exists(model_name):
+            from modelscope import snapshot_download
+            model_name = snapshot_download(model_name)
+        pass
+
         # First check if it's a normal model via AutoConfig
         from huggingface_hub.utils import disable_progress_bars, enable_progress_bars, are_progress_bars_disabled
         was_disabled = are_progress_bars_disabled()
@@ -355,6 +369,11 @@ class FastVisionModel(FastBaseVisionModel):
         if not use_exact_model_name:
             model_name = get_model_name(model_name, load_in_4bit)
 
+        if USE_MODELSCOPE and not os.path.exists(model_name):
+            from modelscope import snapshot_download
+            model_name = snapshot_download(model_name)
+        pass
+
         # First check if it's a normal model via AutoConfig
         from huggingface_hub.utils import disable_progress_bars, enable_progress_bars, are_progress_bars_disabled
         was_disabled = are_progress_bars_disabled()