model --> model_name_or_path (huggingface#1452)

* `model` --> `model_name_or_path`

* fix style

docs/source/clis.mdx

@@ -56,7 +56,7 @@ You can pass any of these arguments either to the CLI or the YAML file.
 Follow the basic instructions above and run `trl sft --output_dir <output_dir> <*args>`: 
 
 ```bash
-trl sft --config config.yaml --output_dir your-output-dir 
+trl sft --model_name_or_path facebook/opt-125m --dataset_name imdb --output_dir opt-sft-imdb
 ```
 
 The SFT CLI is based on the `examples/scripts/sft.py` script.
@@ -82,7 +82,7 @@ python examples/datasets/anthropic_hh.py --push_to_hub --hf_entity your-hf-org
 Once your dataset being pushed, run the dpo CLI as follows:
 
 ```bash
-trl dpo --config config.yaml --output_dir your-output-dir 
+trl dpo --model_name_or_path facebook/opt-125m --dataset_name trl-internal-testing/Anthropic-hh-rlhf-processed --output_dir opt-sft-hh-rlhf
 ```
 
 The SFT CLI is based on the `examples/scripts/dpo.py` script.
@@ -92,7 +92,7 @@ The SFT CLI is based on the `examples/scripts/dpo.py` script.
 The chat CLI lets you quickly load the model and talk to it. Simply run the following:
 
 ```bash
-trl chat --model  Qwen/Qwen1.5-0.5B-Chat 
+trl chat --model_name_or_path  Qwen/Qwen1.5-0.5B-Chat 
 ```
 
 Note that the chat interface relies on the chat template of the tokenizer to format the inputs for the model. Make sure your tokenizer has a chat template defined.

examples/scripts/chat.py

@@ -156,7 +156,7 @@ def save_chat(chat, args, filename):
     folder = args.save_folder
 
     if filename is None:
-        filename = create_default_filename(args.model)
+        filename = create_default_filename(args.model_name_or_path)
         filename = os.path.join(folder, filename)
     os.makedirs(os.path.dirname(filename), exist_ok=True)
 
@@ -210,7 +210,9 @@ def parse_settings(user_input, current_args, interface):
         return current_args, True
 
 
-def load_model(args):
+def load_model_and_tokenizer(args):
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
+
     torch_dtype = args.torch_dtype if args.torch_dtype in ["auto", None] else getattr(torch, args.torch_dtype)
     quantization_config = get_quantization_config(args)
     model_kwargs = dict(
@@ -221,12 +223,12 @@ def load_model(args):
         device_map=get_kbit_device_map() if quantization_config is not None else None,
         quantization_config=quantization_config,
     )
-    model = AutoModelForCausalLM.from_pretrained(args.model, **model_kwargs)
+    model = AutoModelForCausalLM.from_pretrained(args.model_name_or_path, **model_kwargs)
 
     if getattr(model, "hf_device_map", None) is None:
         model = model.to(args.device)
-    print(model.device)
-    return model
+
+    return model, tokenizer
 
 
 def chat_cli():
@@ -247,11 +249,10 @@ def chat_cli():
     else:
         user = args.user
 
-    tokenizer = AutoTokenizer.from_pretrained(args.model)
-    model = load_model(args)
+    model, tokenizer = load_model_and_tokenizer(args)
     generation_streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)
 
-    interface = RichInterface(model_name=args.model, user_name=user)
+    interface = RichInterface(model_name=args.model_name_or_path, user_name=user)
     interface.clear()
     chat = clear_chat_history(current_args.system_prompt)
     while True:

trl/commands/cli_utils.py

@@ -174,7 +174,7 @@ class DpoScriptArguments:
 @dataclass
 class ChatArguments:
     # general settings
-    model: str = field(metadata={"help": "Name of the pre-trained model"})
+    model_name_or_path: str = field(metadata={"help": "Name of the pre-trained model"})
     user: str = field(default=None, metadata={"help": "Username to display in chat interface"})
     system_prompt: str = field(default=None, metadata={"help": "System prompt"})
     save_folder: str = field(default="./chat_history/", metadata={"help": "Folder to save chat history"})