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Update all references to canonical models (#29001)

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This commit is contained in:
Lysandre Debut
2024-02-16 08:16:58 +01:00
committed by GitHub
parent 1e402b957d
commit f497f564bb
561 changed files with 2682 additions and 2687 deletions
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20
docs/source/en/generation_strategies.md
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@@ -54,7 +54,7 @@ When you load a model explicitly, you can inspect the generation configuration t
```python
>>> from transformers import AutoModelForCausalLM
>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
>>> model = AutoModelForCausalLM.from_pretrained("distilbert/distilgpt2")
>>> model.generation_config
GenerationConfig {
"bos_token_id": 50256,
@@ -121,8 +121,8 @@ one for summarization with beam search). You must have the right Hub permissions
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
>>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
>>> translation_generation_config = GenerationConfig(
... num_beams=4,
@@ -162,8 +162,8 @@ your screen, one word at a time:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
>>> tok = AutoTokenizer.from_pretrained("gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> tok = AutoTokenizer.from_pretrained("openai-community/gpt2")
>>> model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2")
>>> inputs = tok(["An increasing sequence: one,"], return_tensors="pt")
>>> streamer = TextStreamer(tok)
@@ -187,7 +187,7 @@ Here, we'll show some of the parameters that control the decoding strategies and
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> prompt = "I look forward to"
>>> checkpoint = "distilgpt2"
>>> checkpoint = "distilbert/distilgpt2"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
>>> inputs = tokenizer(prompt, return_tensors="pt")
@@ -208,7 +208,7 @@ The two main parameters that enable and control the behavior of contrastive sear
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> checkpoint = "gpt2-large"
>>> checkpoint = "openai-community/gpt2-large"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
@@ -235,7 +235,7 @@ To enable multinomial sampling set `do_sample=True` and `num_beams=1`.
>>> from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed
>>> set_seed(0) # For reproducibility
>>> checkpoint = "gpt2-large"
>>> checkpoint = "openai-community/gpt2-large"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
@@ -260,7 +260,7 @@ To enable this decoding strategy, specify the `num_beams` (aka number of hypothe
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> prompt = "It is astonishing how one can"
>>> checkpoint = "gpt2-medium"
>>> checkpoint = "openai-community/gpt2-medium"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
>>> inputs = tokenizer(prompt, return_tensors="pt")
@@ -283,7 +283,7 @@ the `num_beams` greater than 1, and set `do_sample=True` to use this decoding st
>>> set_seed(0) # For reproducibility
>>> prompt = "translate English to German: The house is wonderful."
>>> checkpoint = "t5-small"
>>> checkpoint = "google-t5/t5-small"
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
>>> inputs = tokenizer(prompt, return_tensors="pt")