Add ollama support and provider examples

examples/provider_examples/oai.py

@@ -0,0 +1,20 @@
+import pandas as pd
+
+import lotus
+from lotus.models import OpenAIModel
+
+lm = OpenAIModel()
+
+lotus.settings.configure(lm=lm)
+data = {
+    "Course Name": [
+        "Probability and Random Processes",
+        "Optimization Methods in Engineering",
+        "Digital Design and Integrated Circuits",
+        "Computer Security",
+    ]
+}
+df = pd.DataFrame(data)
+user_instruction = "{Course Name} requires a lot of math"
+df = df.sem_filter(user_instruction)
+print(df)

examples/provider_examples/ollama.py

@@ -0,0 +1,25 @@
+import pandas as pd
+
+import lotus
+from lotus.models import OpenAIModel
+
+lm = OpenAIModel(
+    api_base="http://localhost:11434/v1",
+    model="llama3.2",
+    hf_name="meta-llama/Llama-3.2-3B-Instruct",
+    provider="ollama",
+)
+
+lotus.settings.configure(lm=lm)
+data = {
+    "Course Name": [
+        "Probability and Random Processes",
+        "Optimization Methods in Engineering",
+        "Digital Design and Integrated Circuits",
+        "Computer Security",
+    ]
+}
+df = pd.DataFrame(data)
+user_instruction = "{Course Name} requires a lot of math"
+df = df.sem_filter(user_instruction)
+print(df)

examples/provider_examples/vllm.py

@@ -0,0 +1,24 @@
+import pandas as pd
+
+import lotus
+from lotus.models import OpenAIModel
+
+lm = OpenAIModel(
+    model="meta-llama/Meta-Llama-3.1-70B-Instruct",
+    api_base="http://localhost:8000/v1",
+    provider="vllm",
+)
+
+lotus.settings.configure(lm=lm)
+data = {
+    "Course Name": [
+        "Probability and Random Processes",
+        "Optimization Methods in Engineering",
+        "Digital Design and Integrated Circuits",
+        "Computer Security",
+    ]
+}
+df = pd.DataFrame(data)
+user_instruction = "{Course Name} requires a lot of math"
+df = df.sem_filter(user_instruction)
+print(df)

lotus/models/openai_model.py

@@ -11,13 +11,6 @@
 import lotus
 from lotus.models.lm import LM
 
-# Mapping from Databricks model names to their Hugging Face model names for tokenizers
-DBRX_NAME_TO_MODEL = {
-    "databricks-dbrx-instruct": "databricks/dbrx-instruct",
-    "databricks-llama-2-70b-chat": "meta-llama/Llama-2-70b-chat-hf",
-    "databricks-mixtral-8x7b-instruct": "mistralai/Mixtral-8x7B-Instruct-v0.1",
-}
-
 ERRORS = (openai.RateLimitError, openai.APIError)
 
 
@@ -46,16 +39,17 @@ class OpenAIModel(LM):
     def __init__(
         self,
         model: str = "gpt-4o-mini",
+        hf_name: Optional[str] = None,
         api_key: Optional[str] = None,
         api_base: Optional[str] = None,
         provider: str = "openai",
-        max_batch_size=64,
-        max_ctx_len=4096,
+        max_batch_size: int = 64,
+        max_ctx_len: int = 4096,
         **kwargs: Dict[str, Any],
     ):
         super().__init__()
         self.provider = provider
-        self.use_chat = provider in ["openai", "dbrx"]
+        self.use_chat = provider in ["openai", "dbrx", "ollama"]
         self.max_batch_size = max_batch_size
         self.max_ctx_len = max_ctx_len
 
@@ -70,14 +64,11 @@ def __init__(
 
         self.client = OpenAI(api_key=api_key, base_url=api_base)
 
-        self.kwargs["model"] = model
         # TODO: Refactor this
         if self.provider == "openai":
             self.tokenizer = tiktoken.encoding_for_model(model)
-        elif model in DBRX_NAME_TO_MODEL:
-            self.tokenizer = AutoTokenizer.from_pretrained(DBRX_NAME_TO_MODEL[model])
         else:
-            self.tokenizer = AutoTokenizer.from_pretrained(model)
+            self.tokenizer = AutoTokenizer.from_pretrained(hf_name)
 
     def handle_chat_request(self, messages: List, **kwargs: Dict[str, Any]) -> Union[List, Tuple[List, List]]:
         """Handle single chat request to OpenAI server.