[pipeline] A simple fix for half-precision & 8bit models (#21479)

* v1 fix

* adapt from suggestions

* make style

* fix tests

* add gpu tests

* update docs

* fix other tests

* Apply suggestions from code review

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>

* better fix

* make fixup

* better example

* revert changes

* proposal

* more elegant solution

* Update src/transformers/pipelines/automatic_speech_recognition.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

---------

Co-authored-by: Nicolas Patry <patry.nicolas@protonmail.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/en/pipeline_tutorial.mdx

@@ -105,6 +105,8 @@ If the model is too large for a single GPU, you can set `device_map="auto"` to a
 generator(model="openai/whisper-large", device_map="auto")
 ```
 
+Note that if  `device_map="auto"` is passed, there is no need to add the argument `device=device` when instantiating your `pipeline` as you may encounter some unexpected behavior!
+
 ### Batch size
 
 By default, pipelines will not batch inference for reasons explained in detail [here](https://huggingface.co/docs/transformers/main_classes/pipelines#pipeline-batching). The reason is that batching is not necessarily faster, and can actually be quite slower in some cases.
@@ -257,4 +259,32 @@ sudo apt install -y tesseract-ocr
 pip install pytesseract
 ```
 
-</Tip>
+</Tip>
+
+## Using `pipeline` on large models with 🤗 `accelerate`:
+
+You can easily run `pipeline` on large models using 🤗 `accelerate`! First make sure you have installed `accelerate` with `pip install accelerate`. 
+
+First load your model using `device_map="auto"`! We will use `facebook/opt-1.3b` for our example.
+
+```py
+# pip install accelerate
+import torch
+from transformers import pipeline
+
+pipe = pipeline(model="facebook/opt-1.3b", torch_dtype=torch.bfloat16, device_map="auto")
+output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
+```
+
+You can also pass 8-bit loaded models if you install `bitsandbytes` and add the argument `load_in_8bit=True`
+
+```py
+# pip install accelerate bitsandbytes
+import torch
+from transformers import pipeline
+
+pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True})
+output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
+```
+
+Note that you can replace the checkpoint with any of the Hugging Face model that supports large model loading such as BLOOM!