feat: Add llama-3-vision-alpha chat format

llama_cpp/llama_chat_format.py

@@ -2165,7 +2165,7 @@ def functionary_v1_v2_chat_handler(
 
 
 class Llava15ChatHandler:
-    DEFAULT_SYSTEM_MESSAGE =  "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions."
+    DEFAULT_SYSTEM_MESSAGE: Optional[str] =  "A chat between a curious human and an artificial intelligence assistant.  The assistant gives helpful, detailed, and polite answers to the human's questions."
 
     CHAT_FORMAT = (
         "{% for message in messages %}"
@@ -2288,7 +2288,7 @@ class Llava15ChatHandler:
         assert self.clip_ctx is not None
 
         system_prompt = _get_system_message(messages)
-        if system_prompt == "":
+        if system_prompt == "" and self.DEFAULT_SYSTEM_MESSAGE is not None:
             messages = [llama_types.ChatCompletionRequestSystemMessage(role="system", content=self.DEFAULT_SYSTEM_MESSAGE)] + messages
 
         image_urls = self.get_image_urls(messages)
@@ -2771,6 +2771,66 @@ class NanoLlavaChatHandler(Llava15ChatHandler):
         "{% endif %}"
     )
 
+class Llama3VisionAlpha(Llava15ChatHandler):
+    # question = "<image>" + q
+
+    # prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+    DEFAULT_SYSTEM_MESSAGE = None
+
+    CHAT_FORMAT = (
+        "{% for message in messages %}"
+
+        "<|start_header_id|>"
+
+        "{% if message.role == 'user' %}"
+
+        "user<|end_header_id|>\n\n"
+
+        "{% if message.content is iterable %}"
+
+        # <image>
+        "{% for content in message.content %}"
+        "{% if content.type == 'image_url' %}"
+        "{% if content.image_url is string %}"
+        "{{ content.image_url }}"
+        "{% endif %}"
+        "{% if content.image_url is mapping %}"
+        "{{ content.image_url.url }}"
+        "{% endif %}"
+        "{% endif %}"
+        "{% endfor %}"
+
+        # Question:
+        "{% for content in message.content %}"
+        "{% if content.type == 'text' %}"
+        "{{ content.text }}"
+        "{% endif %}"
+        "{% endfor %}"
+
+        "{% endif %}"
+
+        # Question:
+        "{% if message.content is string %}"
+        "{{ message.content }}"
+        "{% endif %}"
+
+        "{% endif %}"
+
+        # Answer:
+        "{% if message.role == 'assistant' %}"
+        "assistant<|end_header_id|>\n\n"
+        "{{ message.content }}"
+        "{% endif %}"
+
+        "<|eot_id|>"
+
+        "{% endfor %}"
+
+        # Generation prompt
+        "{% if add_generation_prompt %}"
+        "<|start_header_id|>assistant<|end_header_id|>\n\n"
+        "{% endif %}"
+    )
 
 @register_chat_completion_handler("chatml-function-calling")
 def chatml_function_calling(

llama_cpp/server/model.py

@@ -140,6 +140,20 @@ class LlamaProxy:
                 chat_handler = llama_cpp.llama_chat_format.NanoLlavaChatHandler(
                     clip_model_path=settings.clip_model_path, verbose=settings.verbose
                 )
+        elif settings.chat_format == "llama-3-vision-alpha":
+            assert settings.clip_model_path is not None, "clip model not found"
+            if settings.hf_model_repo_id is not None:
+                chat_handler = (
+                    llama_cpp.llama_chat_format.Llama3VisionAlpha.from_pretrained(
+                        repo_id=settings.hf_model_repo_id,
+                        filename=settings.clip_model_path,
+                        verbose=settings.verbose,
+                    )
+                )
+            else:
+                chat_handler = llama_cpp.llama_chat_format.Llama3VisionAlpha(
+                    clip_model_path=settings.clip_model_path, verbose=settings.verbose
+                )
         elif settings.chat_format == "hf-autotokenizer":
             assert (
                 settings.hf_pretrained_model_name_or_path is not None