Transformers cli clean command (#37657)

* transformers-cli -> transformers

* Chat command works with positional argument

* update doc references to transformers-cli

* doc headers

* deepspeed

---------

Co-authored-by: Joao Gante <joao@huggingface.co>

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -16,7 +16,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
+      description: Please share your system info with us. You can run the command `transformers env` and copy-paste its output below.
       placeholder: transformers version, platform, python version, ...
     validations:
       required: true

.github/ISSUE_TEMPLATE/migration.yml

@@ -6,7 +6,7 @@ body:
     id: system-info
     attributes:
       label: System Info
-      description: Please share your system info with us. You can run the command `transformers-cli env` and copy-paste its output below.
+      description: Please share your system info with us. You can run the command `transformers env` and copy-paste its output below.
       render: shell
       placeholder: transformers version, platform, python version, ...
     validations:

.github/workflows/add-model-like.yml

@@ -54,7 +54,7 @@ jobs:
       - name: Create model files
         run: |
           . ~/venv/bin/activate
-          transformers-cli add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
+          transformers add-new-model-like --config_file tests/fixtures/add_distilbert_like_config.json --path_to_repo .
           make style
           make fix-copies
 

CONTRIBUTING.md

@@ -78,7 +78,7 @@ Once you've confirmed the bug hasn't already been reported, please include the f
 To get the OS and software versions automatically, run the following command:
 
 ```bash
-transformers-cli env
+transformers env
 ```
 
 You can also run the same command from the root of the repository:

README.md

@@ -121,7 +121,7 @@ To chat with a model, the usage pattern is the same. The only difference is you
 > [!TIP]
 > You can also chat with a model directly from the command line.
 > ```shell
-> transformers-cli chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct
+> transformers chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct
 > ```
 
 ```py

docs/source/de/add_new_model.md

@@ -402,7 +402,7 @@ Andernfalls beginnen wir mit der Erstellung eines neuen Modells. Wir empfehlen d
 ein bestehendes Modell:
 
 ```bash
-transformers-cli add-new-model-like
+transformers add-new-model-like
 ```
 
 Sie werden mit einem Fragebogen aufgefordert, die grundlegenden Informationen Ihres Modells einzugeben.

docs/source/de/contributing.md

@@ -63,7 +63,7 @@ Wenn Sie sich vergewissert haben, dass der Fehler noch nicht gemeldet wurde, geb
 Um das Betriebssystem und die Softwareversionen automatisch auszugeben, führen Sie den folgenden Befehl aus:
 
 ```bash
-transformers-cli env
+transformers env
 ```
 
 Sie können denselben Befehl auch im Hauptverzeichnis des Repositorys ausführen:

docs/source/en/add_new_model.md

@@ -161,7 +161,7 @@ The downside is that if you aren't used to them, it may take some time to get us
 Run the command below to start and complete the questionnaire with some basic information about the new model. This command jumpstarts the process by automatically generating some model code that you'll need to adapt.
 
 ```bash
-transformers-cli add-new-model-like
+transformers add-new-model-like
 ```
 
 ## Create a pull request
@@ -292,7 +292,7 @@ Once you're able to run the original checkpoint, you're ready to start adapting
 
 ## Adapt the model code
 
-The `transformers-cli add-new-model-like` command should have generated a model and configuration file.
+The `transformers add-new-model-like` command should have generated a model and configuration file.
 
 - `src/transformers/models/brand_new_llama/modeling_brand_new_llama.py`
 - `src/transformers/models/brand_new_llama/configuration_brand_new_llama.py`
@@ -551,10 +551,10 @@ While this example doesn't include an image processor, you may need to implement
 
 If you do need to implement a new image processor, refer to an existing image processor to understand the expected structure. Slow image processors ([`BaseImageProcessor`]) and fast image processors ([`BaseImageProcessorFast`]) are designed differently, so make sure you follow the correct structure based on the processor type you're implementing.
 
-Run the following command (only if you haven't already created the fast image processor with the `transformers-cli add-new-model-like` command) to generate the necessary imports and to create a prefilled template for the fast image processor. Modify the template to fit your model.
+Run the following command (only if you haven't already created the fast image processor with the `transformers add-new-model-like` command) to generate the necessary imports and to create a prefilled template for the fast image processor. Modify the template to fit your model.
 
 ```bash
-transformers-cli add-fast-image-processor --model-name your_model_name
+transformers add-fast-image-processor --model-name your_model_name
 ```
 
 This command will generate the necessary imports and provide a pre-filled template for the fast image processor. You can then modify it to fit your model's needs.

docs/source/en/conversations.md

@@ -25,12 +25,12 @@ Check model leaderboards like [OpenLLM](https://hf.co/spaces/HuggingFaceH4/open_
 
 This guide shows you how to quickly start chatting with Transformers from the command line, how build and format a conversation, and how to chat using the [`TextGenerationPipeline`].
 
-## transformers-cli
+## transformers CLI
 
 Chat with a model directly from the command line as shown below. It launches an interactive session with a model. Enter `clear` to reset the conversation, `exit` to terminate the session, and `help` to display all the command options.
 
 ```bash
-transformers-cli chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct
+transformers chat Qwen/Qwen2.5-0.5B-Instruct
 ```
 
 <div class="flex justify-center">
@@ -40,7 +40,7 @@ transformers-cli chat --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct
 For a full list of options, run the command below.
 
 ```bash
-transformers-cli chat -h
+transformers chat -h
 ```
 
 The chat is implemented on top of the [AutoClass](./model_doc/auto), using tooling from [text generation](./llm_tutorial) and [chat](./chat_templating).

docs/source/en/model_doc/bert.md

@@ -81,10 +81,10 @@ print(f"The predicted token is: {predicted_token}")
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers-cli run --task fill-mask --model google-bert/bert-base-uncased --device 0
+echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers run --task fill-mask --model google-bert/bert-base-uncased --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/code_llama.md

@@ -92,10 +92,10 @@ print(filled_text)
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "# Function to calculate the factorial of a number\ndef factorial(n):" | transformers-cli run --task text-generation --model meta-llama/CodeLlama-7b-hf --device 0
+echo -e "# Function to calculate the factorial of a number\ndef factorial(n):" | transformers run --task text-generation --model meta-llama/CodeLlama-7b-hf --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/cohere.md

@@ -59,11 +59,11 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
 # pip install -U flash-attn --no-build-isolation
-transformers-cli chat --model_name_or_path CohereForAI/c4ai-command-r-v01 --torch_dtype auto --attn_implementation flash_attention_2
+transformers chat CohereForAI/c4ai-command-r-v01 --torch_dtype auto --attn_implementation flash_attention_2
 ```
 
 </hfoption>

docs/source/en/model_doc/distilbert.md

@@ -83,10 +83,10 @@ print(f"Predicted label: {predicted_label}")
 
 </hfoption>
 
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "I love using Hugging Face Transformers!" | transformers-cli run --task text-classification --model distilbert-base-uncased-finetuned-sst-2-english
+echo -e "I love using Hugging Face Transformers!" | transformers run --task text-classification --model distilbert-base-uncased-finetuned-sst-2-english
 ```
 
 </hfoption>
@@ -213,7 +213,3 @@ echo -e "I love using Hugging Face Transformers!" | transformers-cli run --task
 
 </jax>
 </frameworkcontent>
-
-
-
-

docs/source/en/model_doc/electra.md

@@ -78,10 +78,10 @@ print(f"Predicted label: {predicted_label}")
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "This restaurant has amazing food." | transformers-cli run --task text-classification --model bhadresh-savani/electra-base-emotion --device 0
+echo -e "This restaurant has amazing food." | transformers run --task text-classification --model bhadresh-savani/electra-base-emotion --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/falcon.md

@@ -76,11 +76,11 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
 # pip install -U flash-attn --no-build-isolation
-transformers-cli chat --model_name_or_path tiiuae/falcon-7b-instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
+transformers chat tiiuae/falcon-7b-instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/falcon_mamba.md

@@ -73,10 +73,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-transformers-cli chat --model_name_or_path tiiuae/falcon-mamba-7b-instruct --torch_dtype auto --device 0
+transformers chat tiiuae/falcon-mamba-7b-instruct --torch_dtype auto --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/gemma.md

@@ -80,10 +80,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "LLMs generate text through a process known as" | transformers-cli run --task text-generation --model google/gemma-2b --device 0
+echo -e "LLMs generate text through a process known as" | transformers run --task text-generation --model google/gemma-2b --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/gemma2.md

@@ -80,10 +80,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```
-echo -e "Explain quantum computing simply." | transformers-cli run --task text-generation --model google/gemma-2-2b --device 0
+echo -e "Explain quantum computing simply." | transformers run --task text-generation --model google/gemma-2-2b --device 0
 ```
 </hfoption>
 </hfoptions>

docs/source/en/model_doc/gemma3.md

@@ -99,10 +99,10 @@ print(processor.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model google/gemma-3-1b-pt --device 0
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model google/gemma-3-1b-pt --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/gpt2.md

@@ -64,10 +64,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Hello, I'm a language model" | transformers-cli run --task text-generation --model openai-community/gpt2 --device 0
+echo -e "Hello, I'm a language model" | transformers run --task text-generation --model openai-community/gpt2 --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/jamba.md

@@ -75,10 +75,10 @@ output = model.generate(**input_ids, cache_implementation="static")
 print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model ai21labs/AI21-Jamba-Mini-1.6 --device 0
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model ai21labs/AI21-Jamba-Mini-1.6 --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/llama.md

@@ -74,10 +74,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model huggyllama/llama-7b --device 0
+echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model huggyllama/llama-7b --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/llama2.md

@@ -74,10 +74,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-transformers-cli chat --model_name_or_path meta-llama/Llama-2-7b-chat-hf --torch_dtype auto --attn_implementation flash_attention_2
+transformers chat meta-llama/Llama-2-7b-chat-hf --torch_dtype auto --attn_implementation flash_attention_2
 ```
 
 </hfoption>
@@ -175,4 +175,3 @@ visualizer("Plants create energy through a process known as")
 
 [[autodoc]] LlamaForSequenceClassification
     - forward
-

docs/source/en/model_doc/longformer.md

@@ -76,10 +76,10 @@ tokenizer.decode(predictions).split()
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "San Francisco 49ers cornerback Shawntae Spencer will miss the rest of the <mask> with a torn ligament in his left knee." | transformers-cli run --task fill-mask --model allenai/longformer-base-4096 --device 0
+echo -e "San Francisco 49ers cornerback Shawntae Spencer will miss the rest of the <mask> with a torn ligament in his left knee." | transformers run --task fill-mask --model allenai/longformer-base-4096 --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/mistral.md

@@ -78,10 +78,10 @@ The example below demonstrates how to chat with [`Pipeline`] or the [`AutoModel`
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```python
-echo -e "My favorite condiment is" | transformers-cli chat --model_name_or_path mistralai/Mistral-7B-v0.3 --torch_dtype auto --device 0 --attn_implementation flash_attention_2
+echo -e "My favorite condiment is" | transformers chat mistralai/Mistral-7B-v0.3 --torch_dtype auto --device 0 --attn_implementation flash_attention_2
 ```
 
 </hfoption>

docs/source/en/model_doc/mobilebert.md

@@ -76,10 +76,10 @@ print(f"The predicted token is: {predicted_token}")
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "The capital of France is [MASK]." | transformers-cli run --task fill-mask --model google/mobilebert-uncased --device 0
+echo -e "The capital of France is [MASK]." | transformers run --task fill-mask --model google/mobilebert-uncased --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/modernbert.md

@@ -79,10 +79,10 @@ print(f"The predicted token is: {predicted_token}")
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers-cli run --task fill-mask --model answerdotai/ModernBERT-base --device 0
+echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers run --task fill-mask --model answerdotai/ModernBERT-base --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/openai-gpt.md

@@ -70,10 +70,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "The future of AI is" | transformers-cli run --task text-generation --model openai-community/openai-gpt --device 0
+echo -e "The future of AI is" | transformers run --task text-generation --model openai-community/openai-gpt --device 0
 
 ```
 </hfoption>

docs/source/en/model_doc/phi.md

@@ -65,10 +65,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "'''def print_prime(n): """ Print all primes between 1 and n"""'''" | transformers-cli run --task text-classification --model microsoft/phi-1.5 --device 0
+echo -e "'''def print_prime(n): """ Print all primes between 1 and n"""'''" | transformers run --task text-classification --model microsoft/phi-1.5 --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/qwen2.md

@@ -100,11 +100,11 @@ print(response)
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
 # pip install -U flash-attn --no-build-isolation
-transformers-cli chat --model_name_or_path Qwen/Qwen2-7B-Instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
+transformers chat Qwen/Qwen2-7B-Instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
 ```
 
 </hfoption>

docs/source/en/model_doc/t5.md

@@ -75,10 +75,10 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 </hfoption>
-<hfoption id="transformers-cli">
+<hfoption id="transformers CLI">
 
 ```bash
-echo -e "translate English to French: The weather is nice today." | transformers-cli run --task text2text-generation --model google-t5/t5-base --device 0
+echo -e "translate English to French: The weather is nice today." | transformers run --task text2text-generation --model google-t5/t5-base --device 0
 ```
 
 </hfoption>

docs/source/es/converting_tensorflow_models.md

@@ -20,9 +20,9 @@ Te proporcionamos una interfaz de línea de comando (`CLI`, por sus siglas en in
 
 <Tip>
 
-Desde 2.3.0, el script para convertir es parte de la CLI de transformers (**transformers-cli**) disponible en cualquier instalación de transformers >= 2.3.0.
+Desde 2.3.0, el script para convertir es parte de la CLI de transformers (**transformers**) disponible en cualquier instalación de transformers >= 2.3.0.
 
-La siguiente documentación refleja el formato para el comando **transformers-cli convert**.
+La siguiente documentación refleja el formato para el comando **transformers convert**.
 
 </Tip>
 
@@ -41,7 +41,7 @@ Aquí hay un ejemplo del proceso para convertir un modelo `BERT-Base Uncased` pr
 ```bash
 export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
 
-transformers-cli convert --model_type bert \
+transformers convert --model_type bert \
   --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
   --config $BERT_BASE_DIR/bert_config.json \
   --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
@@ -60,7 +60,7 @@ Aquí hay un ejemplo del proceso para convertir un modelo `ALBERT Base` pre-entr
 ```bash
 export ALBERT_BASE_DIR=/path/to/albert/albert_base
 
-transformers-cli convert --model_type albert \
+transformers convert --model_type albert \
   --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
   --config $ALBERT_BASE_DIR/albert_config.json \
   --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
@@ -75,7 +75,7 @@ Este es un ejemplo del proceso para convertir un modelo OpenAI GPT pre-entrenado
 ```bash
 export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
 
-transformers-cli convert --model_type gpt \
+transformers convert --model_type gpt \
   --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
   [--config OPENAI_GPT_CONFIG] \
@@ -89,7 +89,7 @@ Aquí hay un ejemplo del proceso para convertir un modelo OpenAI GPT-2 pre-entre
 ```bash
 export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/openai-community/gpt2/pretrained/weights
 
-transformers-cli convert --model_type gpt2 \
+transformers convert --model_type gpt2 \
   --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
   [--config OPENAI_GPT2_CONFIG] \
@@ -104,7 +104,7 @@ Aquí hay un ejemplo del proceso para convertir un modelo XLNet pre-entrenado:
 export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
 export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
 
-transformers-cli convert --model_type xlnet \
+transformers convert --model_type xlnet \
   --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
   --config $TRANSFO_XL_CONFIG_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
@@ -118,7 +118,7 @@ Aquí hay un ejemplo del proceso para convertir un modelo XLM pre-entrenado:
 ```bash
 export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
 
-transformers-cli convert --model_type xlm \
+transformers convert --model_type xlm \
   --tf_checkpoint $XLM_CHECKPOINT_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
  [--config XML_CONFIG] \
@@ -132,7 +132,7 @@ Aquí hay un ejemplo del proceso para convertir un modelo T5 pre-entrenado:
 ```bash
 export T5=/path/to/t5/uncased_L-12_H-768_A-12
 
-transformers-cli convert --model_type t5 \
+transformers convert --model_type t5 \
   --tf_checkpoint $T5/t5_model.ckpt \
   --config $T5/t5_config.json \
   --pytorch_dump_output $T5/pytorch_model.bin

docs/source/it/add_new_model.md

@@ -355,7 +355,7 @@ Se questo non é il caso, cominciamo con il generare un nuovo modello. Ti consig
 un modello esistente:
 
 ```bash
-transformers-cli add-new-model-like
+transformers add-new-model-like
 ```
 
 Ti verrà richiesto con un questionario di compilare le informazioni di base del tuo modello.

docs/source/it/converting_tensorflow_models.md

@@ -18,10 +18,10 @@ in modelli che possono essere caricati utilizzando i metodi `from_pretrained` de
 
 <Tip>
 
-A partire dalla versione 2.3.0 lo script di conversione è parte di transformers CLI (**transformers-cli**), disponibile in ogni installazione
+A partire dalla versione 2.3.0 lo script di conversione è parte di transformers CLI (**transformers**), disponibile in ogni installazione
 di transformers >=2.3.0.
 
-La seguente documentazione riflette il formato dei comandi di **transformers-cli convert**.
+La seguente documentazione riflette il formato dei comandi di **transformers convert**.
 
 </Tip>
 
@@ -49,7 +49,7 @@ Questo è un esempio del processo di conversione per un modello `BERT-Base Uncas
 
 ```bash
 export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
-transformers-cli convert --model_type bert \
+transformers convert --model_type bert \
   --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
   --config $BERT_BASE_DIR/bert_config.json \
   --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
@@ -70,7 +70,7 @@ Ecco un esempio del procedimento di conversione di un modello `ALBERT Base` pre-
 
 ```bash
 export ALBERT_BASE_DIR=/path/to/albert/albert_base
-transformers-cli convert --model_type albert \
+transformers convert --model_type albert \
   --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
   --config $ALBERT_BASE_DIR/albert_config.json \
   --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
@@ -84,7 +84,7 @@ Ecco un esempio del processo di conversione di un modello OpenAI GPT pre-allenat
 sia salvato nello stesso formato dei modelli pre-allenati OpenAI (vedi [qui](https://github.com/openai/finetune-transformer-lm)):
 ```bash
 export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
-transformers-cli convert --model_type gpt \
+transformers convert --model_type gpt \
   --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
   [--config OPENAI_GPT_CONFIG] \
@@ -97,7 +97,7 @@ Ecco un esempio del processo di conversione di un modello OpenAI GPT-2 pre-allen
 
 ```bash
 export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/openai-community/gpt2/pretrained/weights
-transformers-cli convert --model_type gpt2 \
+transformers convert --model_type gpt2 \
   --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
   [--config OPENAI_GPT2_CONFIG] \
@@ -111,7 +111,7 @@ Ecco un esempio del processo di conversione di un modello XLNet pre-allenato:
 ```bash
 export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
 export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
-transformers-cli convert --model_type xlnet \
+transformers convert --model_type xlnet \
   --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
   --config $TRANSFO_XL_CONFIG_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
@@ -124,7 +124,7 @@ Ecco un esempio del processo di conversione di un modello XLM pre-allenato:
 
 ```bash
 export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
-transformers-cli convert --model_type xlm \
+transformers convert --model_type xlm \
   --tf_checkpoint $XLM_CHECKPOINT_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
  [--config XML_CONFIG] \
@@ -137,7 +137,7 @@ Ecco un esempio del processo di conversione di un modello T5 pre-allenato:
 
 ```bash
 export T5=/path/to/t5/uncased_L-12_H-768_A-12
-transformers-cli convert --model_type t5 \
+transformers convert --model_type t5 \
   --tf_checkpoint $T5/t5_model.ckpt \
   --config $T5/t5_config.json \
   --pytorch_dump_output $T5/pytorch_model.bin

docs/source/ja/add_new_model.md

@@ -312,7 +312,7 @@ cd transformers
 既存のモデル:
 
 ```bash
-transformers-cli add-new-model-like
+transformers add-new-model-like
 ```
 
 モデルの基本情報を入力するためのアンケートが表示されます。
@@ -747,5 +747,3 @@ brand_new_bert.push_to_hub("brand_new_bert")
 さあ、コミュニティからあなたの作業に対する評価を得る時が来ました！モデルの追加を完了することは、TransformersおよびNLPコミュニティにとって重要な貢献です。あなたのコードとポートされた事前学習済みモデルは、何百人、何千人という開発者や研究者によって確実に使用されるでしょう。あなたの仕事に誇りを持ち、コミュニティとあなたの成果を共有しましょう。
 
 **あなたはコミュニティの誰でも簡単にアクセスできる別のモデルを作成しました！ 🤯**
-
-

docs/source/ko/add_new_model.md

@@ -272,7 +272,7 @@ cd transformers
 기존 모델:
 
 ```bash
-transformers-cli add-new-model-like
+transformers add-new-model-like
 ```
 
 모델의 기본 정보를 입력하는 설문지가 표시됩니다.

docs/source/ko/contributing.md

@@ -63,7 +63,7 @@ limitations under the License.
 운영체제와 소프트웨어 버전을 자동으로 가져오려면 다음 명령을 실행하세요:
 
 ```bash
-transformers-cli env
+transformers env
 ```
 
 저장소의 루트 디렉터리에서도 같은 명령을 실행할 수 있습니다:

docs/source/pt/converting_tensorflow_models.md

@@ -21,10 +21,10 @@ que podem ser carregados usando os métodos `from_pretrained` da biblioteca.
 
 <Tip>
 
-A partir da versão 2.3.0 o script de conversão agora faz parte do transformers CLI (**transformers-cli**) disponível em qualquer instalação
+A partir da versão 2.3.0 o script de conversão agora faz parte do transformers CLI (**transformers**) disponível em qualquer instalação
 transformers >= 2.3.0.
 
-A documentação abaixo reflete o formato do comando **transformers-cli convert**.
+A documentação abaixo reflete o formato do comando **transformers convert**.
 
 </Tip>
 
@@ -49,7 +49,7 @@ Aqui está um exemplo do processo de conversão para um modelo `BERT-Base Uncase
 ```bash
 export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
 
-transformers-cli convert --model_type bert \
+transformers convert --model_type bert \
   --tf_checkpoint $BERT_BASE_DIR/bert_model.ckpt \
   --config $BERT_BASE_DIR/bert_config.json \
   --pytorch_dump_output $BERT_BASE_DIR/pytorch_model.bin
@@ -71,7 +71,7 @@ Aqui está um exemplo do processo de conversão para o modelo `ALBERT Base` pré
 ```bash
 export ALBERT_BASE_DIR=/path/to/albert/albert_base
 
-transformers-cli convert --model_type albert \
+transformers convert --model_type albert \
   --tf_checkpoint $ALBERT_BASE_DIR/model.ckpt-best \
   --config $ALBERT_BASE_DIR/albert_config.json \
   --pytorch_dump_output $ALBERT_BASE_DIR/pytorch_model.bin
@@ -88,7 +88,7 @@ foi salvo com o mesmo formato do modelo pré-treinado OpenAI (veja [aqui](https:
 ```bash
 export OPENAI_GPT_CHECKPOINT_FOLDER_PATH=/path/to/openai/pretrained/numpy/weights
 
-transformers-cli convert --model_type gpt \
+transformers convert --model_type gpt \
   --tf_checkpoint $OPENAI_GPT_CHECKPOINT_FOLDER_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
   [--config OPENAI_GPT_CONFIG] \
@@ -102,7 +102,7 @@ Aqui está um exemplo do processo de conversão para um modelo OpenAI GPT-2 pré
 ```bash
 export OPENAI_GPT2_CHECKPOINT_PATH=/path/to/openai-community/gpt2/pretrained/weights
 
-transformers-cli convert --model_type gpt2 \
+transformers convert --model_type gpt2 \
   --tf_checkpoint $OPENAI_GPT2_CHECKPOINT_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
   [--config OPENAI_GPT2_CONFIG] \
@@ -117,7 +117,7 @@ Aqui está um exemplo do processo de conversão para um modelo XLNet pré-treina
 export TRANSFO_XL_CHECKPOINT_PATH=/path/to/xlnet/checkpoint
 export TRANSFO_XL_CONFIG_PATH=/path/to/xlnet/config
 
-transformers-cli convert --model_type xlnet \
+transformers convert --model_type xlnet \
   --tf_checkpoint $TRANSFO_XL_CHECKPOINT_PATH \
   --config $TRANSFO_XL_CONFIG_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT \
@@ -131,7 +131,7 @@ Aqui está um exemplo do processo de conversão para um modelo XLM pré-treinado
 ```bash
 export XLM_CHECKPOINT_PATH=/path/to/xlm/checkpoint
 
-transformers-cli convert --model_type xlm \
+transformers convert --model_type xlm \
   --tf_checkpoint $XLM_CHECKPOINT_PATH \
   --pytorch_dump_output $PYTORCH_DUMP_OUTPUT
  [--config XML_CONFIG] \
@@ -145,7 +145,7 @@ Aqui está um exemplo do processo de conversão para um modelo T5 pré-treinado:
 ```bash
 export T5=/path/to/t5/uncased_L-12_H-768_A-12
 
-transformers-cli convert --model_type t5 \
+transformers convert --model_type t5 \
   --tf_checkpoint $T5/t5_model.ckpt \
   --config $T5/t5_config.json \
   --pytorch_dump_output $T5/pytorch_model.bin

docs/source/zh/contributing.md

@@ -63,7 +63,7 @@ limitations under the License.
 想要自动获取操作系统和软件版本，请运行以下命令：
 
 ```bash
-transformers-cli env
+transformers env
 ```
 
 你也可以从代码仓库的根目录下运行相同的命令：

examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py

@@ -99,7 +99,7 @@ class ModelArguments:
     use_auth_token: bool = field(
         default=False,
         metadata={
-            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "help": "Will use the token generated when running `transformers login` (necessary to use this script "
             "with private models)."
         },
     )

setup.py

@@ -466,7 +466,7 @@ setup(
     package_data={"": ["**/*.cu", "**/*.cpp", "**/*.cuh", "**/*.h", "**/*.pyx", "py.typed"]},
     zip_safe=False,
     extras_require=extras,
-    entry_points={"console_scripts": ["transformers-cli=transformers.commands.transformers_cli:main"]},
+    entry_points={"console_scripts": ["transformers=transformers.commands.transformers_cli:main", "transformers-cli=transformers.commands.transformers_cli:main_cli"]},
     python_requires=">=3.9.0",
     install_requires=list(install_requires),
     classifiers=[

src/transformers/commands/chat.py

@@ -358,7 +358,7 @@ class ChatArguments:
     """
 
     # General settings
-    model_name_or_path: str = field(metadata={"help": "Name of the pre-trained model."})
+    model_name_or_path: Optional[str] = field(default=None, metadata={"help": "Name of the pre-trained model."})
     user: Optional[str] = field(default=None, metadata={"help": "Username to display in chat interface."})
     system_prompt: Optional[str] = field(default=None, metadata={"help": "System prompt."})
     save_folder: str = field(default="./chat_history/", metadata={"help": "Folder to save chat history."})
@@ -435,9 +435,20 @@ class ChatCommand(BaseTransformersCLICommand):
         """
         dataclass_types = (ChatArguments,)
         chat_parser = parser.add_parser("chat", help=HELP_STRING, dataclass_types=dataclass_types)
+
+        group = chat_parser.add_argument_group("Positional arguments")
+        group.add_argument(
+            "model_name_or_path_positional", type=str, nargs="?", default=None, help="Name of the pre-trained model."
+        )
+
         chat_parser.set_defaults(func=chat_command_factory)
 
     def __init__(self, args):
+        args.model_name_or_path = args.model_name_or_path_positional or args.model_name_or_path
+
+        if args.model_name_or_path is None:
+            raise ValueError("--model_name_or_path required for chat command.")
+
         self.args = args
 
     @staticmethod

src/transformers/commands/convert.py

@@ -73,7 +73,7 @@ class ConvertCommand(BaseTransformersCLICommand):
         finetuning_task_name: str,
         *args,
     ):
-        self._logger = logging.get_logger("transformers-cli/converting")
+        self._logger = logging.get_logger("transformers/converting")
 
         self._logger.info(f"Loading model {model_type}")
         self._model_type = model_type

src/transformers/commands/serving.py

@@ -37,7 +37,7 @@ except (ImportError, AttributeError):
     _serve_dependencies_installed = False
 
 
-logger = logging.get_logger("transformers-cli/serving")
+logger = logging.get_logger("transformers/serving")
 
 
 def serve_command_factory(args: Namespace):

src/transformers/commands/train.py

@@ -91,7 +91,7 @@ class TrainCommand(BaseTransformersCLICommand):
         train_parser.set_defaults(func=train_command_factory)
 
     def __init__(self, args: Namespace):
-        self.logger = logging.get_logger("transformers-cli/training")
+        self.logger = logging.get_logger("transformers/training")
 
         self.framework = "tf" if is_tf_available() else "torch"
 

src/transformers/commands/transformers_cli.py

@@ -12,6 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import warnings
 
 from transformers import HfArgumentParser
 from transformers.commands.add_fast_image_processor import AddFastImageProcessorCommand
@@ -24,9 +25,17 @@ from transformers.commands.run import RunCommand
 from transformers.commands.serving import ServeCommand
 
 
+def main_cli():
+    warnings.warn(
+        "`transformers-cli` is deprecated in favour of `transformers` directly and will be removed in v5.",
+        DeprecationWarning,
+    )
+    main()
+
+
 def main():
-    parser = HfArgumentParser(prog="Transformers CLI tool", usage="transformers-cli <command> [<args>]")
+    parser = HfArgumentParser(prog="Transformers CLI tool", usage="transformers <command> [<args>]")
-    commands_parser = parser.add_subparsers(help="transformers-cli command helpers")
+    commands_parser = parser.add_subparsers(help="transformers command helpers")
 
     # Register commands
     ChatCommand.register_subcommand(commands_parser)

src/transformers/models/fsmt/convert_fsmt_original_pytorch_checkpoint_to_pytorch.py

@@ -257,7 +257,7 @@ def convert_fsmt_checkpoint_to_pytorch(fsmt_checkpoint_path, pytorch_dump_folder
     print("Conversion is done!")
     print("\nLast step is to upload the files to s3")
     print(f"cd {data_root}")
-    print(f"transformers-cli upload {model_dir}")
+    print(f"transformers upload {model_dir}")
 
 
 if __name__ == "__main__":

tests/utils/test_cli.py

@@ -23,7 +23,7 @@ from transformers.testing_utils import CaptureStd, require_torch
 class CLITest(unittest.TestCase):
     @patch("sys.argv", ["fakeprogrampath", "env"])
     def test_cli_env(self):
-        # test transformers-cli env
+        # test transformers env
         import transformers.commands.transformers_cli
 
         with CaptureStd() as cs: