Fix tests

outlines/__init__.py

@@ -24,7 +24,7 @@
 )
 
 
-models = [
+model_list = [
     "from_anthropic",
     "from_gemini",
     "from_llamacpp",
@@ -47,4 +47,4 @@
     "Prompt",
     "vectorize",
     "grammars",
-] + models
+] + model_list

tests/generate/test_generate.py

@@ -4,8 +4,8 @@
 
 import pytest
 
+import outlines
 import outlines.generate as generate
-import outlines.models as models
 import outlines.samplers as samplers
 
 ##########################################
@@ -22,7 +22,7 @@ def model_llamacpp(tmp_path_factory):
         filename="TinyMistral-248M-v2-Instruct.Q4_K_M.gguf",
         verbose=False,
     )
-    return models.LlamaCpp(llm)
+    return outlines.from_llamacpp(LlamaCpp(llm))
 
 
 @pytest.fixture(scope="session")
@@ -44,56 +44,60 @@ def model_exllamav2(tmp_path_factory):
 
 @pytest.fixture(scope="session")
 def model_mlxlm(tmp_path_factory):
-    return models.mlxlm("mlx-community/TinyLlama-1.1B-Chat-v1.0-4bit")
+    from mlx_lm import load
+    return outlines.from_mlxlm(*load("mlx-community/TinyLlama-1.1B-Chat-v1.0-4bit"))
 
 
 @pytest.fixture(scope="session")
 def model_mlxlm_phi3(tmp_path_factory):
-    return models.mlxlm("mlx-community/Phi-3-mini-4k-instruct-4bit")
+    from mlx_lm import load
+    return outlines.from_mlxlm(*load("mlx-community/Phi-3-mini-4k-instruct-4bit"))
 
 
 @pytest.fixture(scope="session")
 def model_transformers_random(tmp_path_factory):
-    return models.Transformers("hf-internal-testing/tiny-random-gpt2")
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    return outlines.from_transformers(
+        AutoModelForCausalLM.fromt_pretrained("hf-internal-testing/tiny-random-gpt2"),
+        Tokenizer.fromt_pretrained("hf-internal-testing/tiny-random-gpt2")
+    )
 
 
 @pytest.fixture(scope="session")
 def model_transformers_opt125m(tmp_path_factory):
-    return models.Transformers("facebook/opt-125m")
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    return outlines.from_transformers(
+        AutoModelForCausalLM.fromt_pretrained("facebook/opt-125m"),
+        Tokenizer.fromt_pretrained("facebook/opt-125m")
+    )
 
 
 @pytest.fixture(scope="session")
 def model_mamba(tmp_path_factory):
-    return models.Mamba(model_name="state-spaces/mamba-130m-hf")
+    from transformers import MambaModel, AutoTokenizer
+    return outlines.from_transformers(MambaModel.from_pretrained(model_name="state-spaces/mamba-130m-hf"), AutoTokenizer.from_pretrained(model_name="state-spaces/mamba-130m-hf"))
 
 
 @pytest.fixture(scope="session")
 def model_bart(tmp_path_factory):
-    from transformers import AutoModelForSeq2SeqLM
-
-    return models.Transformers("facebook/bart-base", model_class=AutoModelForSeq2SeqLM)
+    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+    return outlines.from_transformers(AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-base"), AutoTokenizer.from_pretrained('facebook/bart-base'))
 
 
 @pytest.fixture(scope="session")
 def model_transformers_vision(tmp_path_factory):
     import torch
-    from transformers import LlavaNextForConditionalGeneration
-
-    return models.transformers_vision(
-        "llava-hf/llava-v1.6-mistral-7b-hf",
-        model_class=LlavaNextForConditionalGeneration,
-        device="cuda",
-        model_kwargs=dict(
-            torch_dtype=torch.bfloat16,
-            load_in_4bit=True,
-            low_cpu_mem_usage=True,
-        ),
+    from transformers import LlavaNextForConditionalGeneration, AutoTokenizer
+    return outlines.from_transformers(
+        LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", torch_dtype=torch.bfloat16, load_in_4bit=True, low_mem_cpu_usage=True),
+        AutoTokenizer.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
     )
 
 
 @pytest.fixture(scope="session")
 def model_vllm(tmp_path_factory):
-    return models.vllm("facebook/opt-125m", gpu_memory_utilization=0.1)
+    from vllm import LLM
+    return outlines.from_vllm(LLM("facebook/opt-125m", gpu_memory_utilization=0.1))
 
 
 # TODO: exllamav2 failing in main, address in https://github.com/dottxt-ai/outlines/issues/808

tests/generate/test_integration_vllm.py

@@ -4,19 +4,19 @@
 import pytest
 import torch
 from pydantic import BaseModel, constr
-from vllm import LLM
-
-try:
-    from vllm.sampling_params import SamplingParams
-except ImportError:
-    pass
 
 import outlines
 import outlines.generate as generate
 import outlines.grammars as grammars
 import outlines.models as models
 import outlines.samplers as samplers
 
+try:
+    from vllm import LLM
+    from vllm.sampling_params import SamplingParams
+except ImportError:
+    pass
+
 pytestmark = pytest.mark.skipif(
     not torch.cuda.is_available(), reason="vLLM models can only be run on GPU."
 )