Refactor the Transformers and TransformersVision model (dottxt-ai#1430)

The objective of this commit is to adapt `Transfomers` and `TransformersVision` to the
model interface used in v1.0. The main changes are:
- make the models inherit from the base class `Model` and create a
`ModelTypeAdapter` for them
- modify the signature of the `generate` method and make it the only way of
generating text with the model.
- modify the handling of model/tokenizer optional arguments to avoid manipulating
them ourselves (giving to the user the responsibility of choosing those they need).
- delete the tests for those models in the `generate` directory and test them instead
in the `models` directory

docs/reference/models/transformers.md

@@ -10,8 +10,10 @@
 
 You can use `outlines.from_transformers` to load a `transformers` model and tokenizer:
 
+You can also provide keyword arguments in an optional `model_kwargs` parameter. Those will be passed to the `from_pretrained` method of the model class. One such argument is `device_map`, which allows you to specify the device on which the model will be loaded.
+
+For instance:
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
 from outlines import models
 
 model_name = "microsoft/Phi-3-mini-4k-instruct"

outlines/models/transformers.py

@@ -316,7 +316,7 @@ def __init__(self, model: "PreTrainedModel", processor):
         self.processor.padding_side = "left"
         self.processor.pad_token = "[PAD]"
 
-        tokenizer: "PreTrainedTokenizer" = self.processor.tokenizer
+        tokenizer = self.processor.tokenizer
 
         super().__init__(model, tokenizer)
 

outlines/types/__init__.py

@@ -5,9 +5,9 @@
 
 from jsonschema import Draft202012Validator as Validator
 from jsonschema.exceptions import SchemaError
+from outlines_core.fsm.json_schema import build_regex_from_schema
 from pydantic import BaseModel, TypeAdapter
 from typing_extensions import _TypedDictMeta  # type: ignore
-from outlines_core.fsm.json_schema import build_regex_from_schema
 
 from . import airports, countries, locale
 from outlines.types.dsl import (

tests/generate/test_generate.py

@@ -302,21 +302,21 @@ def test_generate_json(request, model_fixture, sample_schema):
     generator(**get_inputs(model_fixture), max_tokens=100)
 
 
-# TODO: add support for genson in the Regex type of v1.0
-# def test_integrate_genson_generate_json(request):
-#    from genson import SchemaBuilder
-#
-#    builder = SchemaBuilder()
-#    builder.add_schema({"type": "object", "properties": {}})
-#    builder.add_object({"name": "Toto", "age": 5})
-#
-#    model = request.getfixturevalue("model_transformers_opt125m")
-#
-#    generator = generate.json(model, builder)
-#    res = generator("Return a json of a young boy")
-#
-#    assert "name" in res
-#    assert "age" in res
+@pytest.mark.xfail(reason="Genson has not been added to JsonType.")
+def test_integrate_genson_generate_json(request):
+    from genson import SchemaBuilder
+
+    builder = SchemaBuilder()
+    builder.add_schema({"type": "object", "properties": {}})
+    builder.add_object({"name": "Toto", "age": 5})
+
+    model = request.getfixturevalue("model_transformers_opt125m")
+
+    generator = generate.json(model, builder)
+    res = generator("Return a json of a young boy")
+
+    assert "name" in res
+    assert "age" in res
 
 
 @pytest.mark.parametrize("model_fixture", ALL_MODEL_FIXTURES)

tests/models/test_transformers.py

@@ -38,6 +38,17 @@ def test_transformers_instantiate_mamba():
     assert isinstance(model, Transformers)
 
 
+def test_transformers_instantiate_tokenizer_kwargs():
+    model = outlines.from_transformers(
+        transformers.AutoModelForCausalLM.from_pretrained(TEST_MODEL),
+        transformers.AutoTokenizer.from_pretrained(
+            TEST_MODEL, additional_special_tokens=["<t1>", "<t2>"]
+        ),
+    )
+    assert "<t1>" in model.tokenizer.special_tokens
+    assert "<t2>" in model.tokenizer.special_tokens
+
+
 @pytest.fixture
 def model():
     model = outlines.from_transformers(

tests/models/test_transformers_vision.py

@@ -18,6 +18,7 @@
 from outlines.models.transformers import TransformersVision
 from outlines.types import Choice, JsonType, Regex
 
+
 TEST_MODEL = "trl-internal-testing/tiny-LlavaForConditionalGeneration"
 TEST_CLIP_MODEL = "openai/clip-vit-base-patch32"
 IMAGE_URLS = [