Templates: Switch to Jinja2

Jinja2 is a lightweight template parser that's used in Transformers
for parsing chat completions. It's much more efficient than Fastchat
and can be imported as part of requirements.

Also allows for unblocking Pydantic's version.

Users now have to provide their own template if needed. A separate
repo may be usable for common prompt template storage.

Signed-off-by: kingbri <bdashore3@proton.me>

model.py

@@ -17,6 +17,7 @@ from exllamav2.generator import(
 
 from gen_logging import log_generation_params, log_prompt, log_response
 from typing import List, Optional, Union
+from templating import PromptTemplate
 from utils import coalesce, unwrap
 
 # Bytes to reserve on first device when loading with auto split
@@ -31,7 +32,7 @@ class ModelContainer:
     draft_cache: Optional[ExLlamaV2Cache] = None
     tokenizer: Optional[ExLlamaV2Tokenizer] = None
     generator: Optional[ExLlamaV2StreamingGenerator] = None
-    prompt_template: Optional[str] = None
+    prompt_template: Optional[PromptTemplate] = None
 
     cache_fp8: bool = False
     gpu_split_auto: bool = True
@@ -103,7 +104,20 @@ class ModelContainer:
         """
 
         # Set prompt template override if provided
-        self.prompt_template = kwargs.get("prompt_template")
+        prompt_template_name = kwargs.get("prompt_template")
+        if prompt_template_name:
+            try:
+                with open(pathlib.Path(f"templates/{prompt_template_name}.jinja"), "r") as raw_template:
+                    self.prompt_template = PromptTemplate(
+                        name = prompt_template_name,
+                        template = raw_template.read()
+                    )
+            except OSError:
+                print("Chat completions are disabled because the provided prompt template couldn't be found.")
+                self.prompt_template = None
+        else:
+            print("Chat completions are disabled because a provided prompt template couldn't be found.")
+            self.prompt_template = None
 
         # Set num of experts per token if provided
         num_experts_override = kwargs.get("num_experts_per_token")