feat: Add ability to load chat format from huggingface autotokenizer or tokenizer_config.json files.

llama_cpp/_utils.py

@@ -1,7 +1,8 @@
 import os
 import sys
 
-import sys, traceback
+import sys
+from typing import Any, Dict
 
 # Avoid "LookupError: unknown encoding: ascii" when open() called in a destructor
 outnull_file = open(os.devnull, "w")
@@ -55,3 +56,25 @@ class suppress_stdout_stderr(object):
 
             self.os.close(self.old_stdout_fileno)
             self.os.close(self.old_stderr_fileno)
+
+
+class MetaSingleton(type):
+    """
+    Metaclass for implementing the Singleton pattern.
+    """
+
+    _instances: Dict[type, Any] = {}
+
+    def __call__(cls, *args: Any, **kwargs: Any) -> Any:
+        if cls not in cls._instances:
+            cls._instances[cls] = super(MetaSingleton, cls).__call__(*args, **kwargs)
+        return cls._instances[cls]
+
+
+class Singleton(object, metaclass=MetaSingleton):
+    """
+    Base class for implementing the Singleton pattern.
+    """
+
+    def __init__(self):
+        super(Singleton, self).__init__()

llama_cpp/llama_chat_format.py

@@ -6,18 +6,28 @@ import ctypes
 import dataclasses
 from typing import Any, Dict, Iterator, List, Optional, Tuple, Union, Protocol
 
+import jinja2
+
 import llama_cpp.llama as llama
 import llama_cpp.llama_types as llama_types
 import llama_cpp.llama_grammar as llama_grammar
 
-from ._utils import suppress_stdout_stderr
+from ._utils import suppress_stdout_stderr, Singleton
 
 
 class LlamaChatCompletionHandler(Protocol):
+    """Base Protocol for a llama chat completion handler.
+
+    Very generic protocol that can be used to implement any chat format.
+    The only hard requirement is that it must return a ChatCompletion when
+    stream=False and an iterator of ChatCompletionChunks when stream=True."""
+
     def __call__(
         self,
         *,
+        # llama.cpp instance
         llama: llama.Llama,
+        # openai api parameters
         messages: List[llama_types.ChatCompletionRequestMessage],
         functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
         function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
@@ -26,8 +36,6 @@ class LlamaChatCompletionHandler(Protocol):
         temperature: float = 0.2,
         top_p: float = 0.95,
         top_k: int = 40,
-        min_p: float = 0.05,
-        typical_p: float = 1.0,
         stream: bool = False,
         stop: Optional[Union[str, List[str]]] = [],
         seed: Optional[int] = None,
@@ -38,14 +46,17 @@ class LlamaChatCompletionHandler(Protocol):
         presence_penalty: float = 0.0,
         frequency_penalty: float = 0.0,
         repeat_penalty: float = 1.1,
+        model: Optional[str] = None,
+        logit_bias: Optional[Dict[str, float]] = None,
+        # llama.cpp parameters
+        min_p: float = 0.05,
+        typical_p: float = 1.0,
         tfs_z: float = 1.0,
         mirostat_mode: int = 0,
         mirostat_tau: float = 5.0,
         mirostat_eta: float = 0.1,
-        model: Optional[str] = None,
         logits_processor: Optional[llama.LogitsProcessorList] = None,
         grammar: Optional[llama.LlamaGrammar] = None,
-        logit_bias: Optional[Dict[str, float]] = None,
         **kwargs,  # type: ignore
     ) -> Union[
         llama_types.CreateChatCompletionResponse,
@@ -54,21 +65,83 @@ class LlamaChatCompletionHandler(Protocol):
         ...
 
 
-CHAT_HANDLERS: Dict[str, LlamaChatCompletionHandler] = {}
+class LlamaChatCompletionHandlerNotFoundException(Exception):
+    pass
+
+
+class LlamaChatCompletionHandlerRegistry(Singleton):
+    _chat_handlers: Dict[str, LlamaChatCompletionHandler] = {}
+
+    def register_chat_completion_handler(
+        self,
+        name: str,
+        chat_handler: LlamaChatCompletionHandler,
+        overwrite: bool = False,
+    ):
+        if not overwrite and name in self._chat_handlers:
+            raise ValueError(
+                f"Formatter with name '{name}' is already registered. Use `overwrite=True` to overwrite it."
+            )
+        self._chat_handlers[name] = chat_handler
+
+    def unregister_chat_handler(self, name: str):
+        if name in self._chat_handlers:
+            del self._chat_handlers[name]
+        else:
+            raise ValueError(f"No formatter registered under the name '{name}'.")
+
+    def get_chat_completion_handler_by_name(
+        self, name: str
+    ) -> LlamaChatCompletionHandler:
+        try:
+            chat_handler = self._chat_handlers[name]
+            return chat_handler
+        except KeyError:
+            raise LlamaChatCompletionHandlerNotFoundException(
+                f"Invalid chat handler: {name} (valid formats: {list(self._chat_handlers.keys())})"
+            )
 
 
 def get_chat_completion_handler(name: str) -> LlamaChatCompletionHandler:
-    return CHAT_HANDLERS[name]
+    return LlamaChatCompletionHandlerRegistry().get_chat_completion_handler_by_name(
+        name
+    )
 
 
 def register_chat_completion_handler(name: str):
     def decorator(f: LlamaChatCompletionHandler):
-        CHAT_HANDLERS[name] = f
+        LlamaChatCompletionHandlerRegistry().register_chat_completion_handler(name, f)
         return f
 
     return decorator
 
 
+### Chat Formatter ###
+
+
+@dataclasses.dataclass
+class ChatFormatterResponse:
+    prompt: str
+    stop: Optional[Union[str, List[str]]] = None
+
+
+class ChatFormatter(Protocol):
+    """Base Protocol for a chat formatter. A chat formatter is a function that
+    takes a list of messages and returns a formatted prompt. It can also return
+    a stop token or list of stop tokens to use for the completion."""
+
+    def __call__(
+        self,
+        *,
+        messages: List[llama_types.ChatCompletionRequestMessage],
+        **kwargs: Any,
+    ) -> ChatFormatterResponse:
+        ...
+
+
+### Utility functions for formatting chat prompts ###
+
+
 def _get_system_message(
     messages: List[llama_types.ChatCompletionRequestMessage],
 ) -> str:
@@ -80,14 +153,18 @@ def _get_system_message(
 
 
 def _map_roles(
-    messages: List[llama_types.ChatCompletionRequestMessage], role_map: Dict[str, str]
+    messages: List[llama_types.ChatCompletionRequestMessage],
+    role_map: Dict[str, str],
 ) -> List[Tuple[str, Optional[str]]]:
     """Map the message roles."""
     output: List[Tuple[str, Optional[str]]] = []
     for message in messages:
         role = message["role"]
         if role in role_map:
-            output.append((role_map[role], message["content"]))
+            content: str | None = (
+                message["content"] if isinstance(message["content"], str) else None
+            )
+            output.append((role_map[role], content))
     return output
 
 
@@ -99,7 +176,8 @@ def _format_llama2(
     ret = system_message + sep
     for i, (role, message) in enumerate(messages):
         if system_message and i == 0:
-            ret += message + seps[i % 2]
+            m = message or ""
+            ret += m + seps[i % 2]
         elif message:
             ret += role + message + " " + seps[i % 2]
         else:
@@ -172,6 +250,7 @@ def _format_chatml(
             ret += role + "\n"
     return ret
 
+
 def _format_chatglm3(
     system_message: str, messages: List[Tuple[str, Optional[str]]], sep: str
 ) -> str:
@@ -187,30 +266,10 @@ def _format_chatglm3(
     return ret
 
 
-@dataclasses.dataclass
-class ChatFormatterResponse:
-    prompt: str
-    stop: Optional[Union[str, List[str]]] = None
-
-
-class ChatFormatter(Protocol):
-    def __call__(
-        self,
-        *,
-        messages: List[llama_types.ChatCompletionRequestMessage],
-        **kwargs: Any,
-    ) -> ChatFormatterResponse:
-        ...
-
-
-class BasicChatHandler:
-    def __init__(self, chat_format: str):
-        self.chat_format = chat_format
-
-
 def _convert_text_completion_to_chat(
     completion: llama_types.Completion,
 ) -> llama_types.ChatCompletion:
+    assert "usage" in completion
     return {
         "id": "chat" + completion["id"],
         "object": "chat.completion",
@@ -286,103 +345,95 @@ def _convert_completion_to_chat(
         return _convert_text_completion_to_chat(completion)
 
 
-_CHAT_FORMATS: Dict[str, ChatFormatter] = {}
+def chat_formatter_to_chat_completion_handler(
+    chat_formatter: ChatFormatter,
+) -> LlamaChatCompletionHandler:
+    def chat_completion_handler(
+        *,
+        llama: llama.Llama,
+        messages: List[llama_types.ChatCompletionRequestMessage],
+        functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
+        function_call: Optional[llama_types.ChatCompletionRequestFunctionCall] = None,
+        tools: Optional[List[llama_types.ChatCompletionTool]] = None,
+        tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
+        temperature: float = 0.2,
+        top_p: float = 0.95,
+        top_k: int = 40,
+        min_p: float = 0.05,
+        typical_p: float = 1.0,
+        stream: bool = False,
+        stop: Optional[Union[str, List[str]]] = [],
+        seed: Optional[int] = None,
+        response_format: Optional[
+            llama_types.ChatCompletionRequestResponseFormat
+        ] = None,
+        max_tokens: Optional[int] = None,
+        presence_penalty: float = 0.0,
+        frequency_penalty: float = 0.0,
+        repeat_penalty: float = 1.1,
+        tfs_z: float = 1.0,
+        mirostat_mode: int = 0,
+        mirostat_tau: float = 5.0,
+        mirostat_eta: float = 0.1,
+        model: Optional[str] = None,
+        logits_processor: Optional[llama.LogitsProcessorList] = None,
+        grammar: Optional[llama.LlamaGrammar] = None,
+        logit_bias: Optional[Dict[str, float]] = None,
+        **kwargs,  # type: ignore
+    ) -> Union[
+        llama_types.CreateChatCompletionResponse,
+        Iterator[llama_types.CreateChatCompletionStreamResponse],
+    ]:
+        result = chat_formatter(
+            messages=messages,
+            functions=functions,
+            function_call=function_call,
+        )
+        prompt = result.prompt
+        if result.stop is not None:
+            stop = [] if stop is None else [stop] if isinstance(stop, str) else stop
+            rstop = result.stop if isinstance(result.stop, list) else [result.stop]
+            stop = stop + rstop
+
+        if response_format is not None and response_format["type"] == "json_object":
+            grammar = llama_grammar.LlamaGrammar.from_string(llama_grammar.JSON_GBNF)
+
+        completion_or_chunks = llama.create_completion(
+            prompt=prompt,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            min_p=min_p,
+            typical_p=typical_p,
+            stream=stream,
+            stop=stop,
+            seed=seed,
+            max_tokens=max_tokens,
+            presence_penalty=presence_penalty,
+            frequency_penalty=frequency_penalty,
+            repeat_penalty=repeat_penalty,
+            tfs_z=tfs_z,
+            mirostat_mode=mirostat_mode,
+            mirostat_tau=mirostat_tau,
+            mirostat_eta=mirostat_eta,
+            model=model,
+            logits_processor=logits_processor,
+            grammar=grammar,
+            logit_bias=logit_bias,
+        )
+        return _convert_completion_to_chat(completion_or_chunks, stream=stream)
+
+    return chat_completion_handler
 
 
 def register_chat_format(name: str):
     def decorator(f: ChatFormatter):
-        def basic_create_chat_completion(
+        chat_completion_handler = chat_formatter_to_chat_completion_handler(f)
-            *,
+        LlamaChatCompletionHandlerRegistry().register_chat_completion_handler(
-            llama: llama.Llama,
+            name, chat_completion_handler
-            messages: List[llama_types.ChatCompletionRequestMessage],
-            functions: Optional[List[llama_types.ChatCompletionFunction]] = None,
-            function_call: Optional[
-                llama_types.ChatCompletionRequestFunctionCall
-            ] = None,
-            tools: Optional[List[llama_types.ChatCompletionTool]] = None,
-            tool_choice: Optional[llama_types.ChatCompletionToolChoiceOption] = None,
-            temperature: float = 0.2,
-            top_p: float = 0.95,
-            top_k: int = 40,
-            min_p: float = 0.05,
-            typical_p: float = 1.0,
-            stream: bool = False,
-            stop: Optional[Union[str, List[str]]] = [],
-            seed: Optional[int] = None,
-            response_format: Optional[
-                llama_types.ChatCompletionRequestResponseFormat
-            ] = None,
-            max_tokens: Optional[int] = None,
-            presence_penalty: float = 0.0,
-            frequency_penalty: float = 0.0,
-            repeat_penalty: float = 1.1,
-            tfs_z: float = 1.0,
-            mirostat_mode: int = 0,
-            mirostat_tau: float = 5.0,
-            mirostat_eta: float = 0.1,
-            model: Optional[str] = None,
-            logits_processor: Optional[llama.LogitsProcessorList] = None,
-            grammar: Optional[llama.LlamaGrammar] = None,
-            logit_bias: Optional[Dict[str, float]] = None,
-            **kwargs,  # type: ignore
-        ) -> Union[
-            llama_types.CreateChatCompletionResponse,
-            Iterator[llama_types.CreateChatCompletionStreamResponse],
-        ]:
-            result = f(
-                messages=messages,
-                functions=functions,
-                function_call=function_call,
-            )
-            prompt = result.prompt
-            if result.stop is not None:
-                stop = [] if stop is None else [stop] if isinstance(stop, str) else stop
-                rstop = result.stop if isinstance(result.stop, list) else [result.stop]
-                stop = stop + rstop
-
-            if response_format is not None and response_format["type"] == "json_object":
-                grammar = llama_grammar.LlamaGrammar.from_string(
-                    llama_grammar.JSON_GBNF
-                )
-
-            completion_or_chunks = llama.create_completion(
-                prompt=prompt,
-                temperature=temperature,
-                top_p=top_p,
-                top_k=top_k,
-                min_p=min_p,
-                typical_p=typical_p,
-                stream=stream,
-                stop=stop,
-                seed=seed,
-                max_tokens=max_tokens,
-                presence_penalty=presence_penalty,
-                frequency_penalty=frequency_penalty,
-                repeat_penalty=repeat_penalty,
-                tfs_z=tfs_z,
-                mirostat_mode=mirostat_mode,
-                mirostat_tau=mirostat_tau,
-                mirostat_eta=mirostat_eta,
-                model=model,
-                logits_processor=logits_processor,
-                grammar=grammar,
-                logit_bias=logit_bias,
-            )
-            return _convert_completion_to_chat(completion_or_chunks, stream=stream)
-
-        register_chat_completion_handler(name)(basic_create_chat_completion)
-        return f
-
-    return decorator
-
-
-def get_chat_format(name: str):
-    try:
-        return _CHAT_FORMATS[name]
-    except KeyError:
-        raise ValueError(
-            f"Invalid chat format: {name} (valid formats: {list(_CHAT_FORMATS.keys())})"
         )
+        return f
+    return decorator
 
 
 def hf_autotokenizer_to_chat_formatter(
@@ -391,22 +442,78 @@ def hf_autotokenizer_to_chat_formatter(
     # https://huggingface.co/docs/transformers/main/chat_templating
     # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#instruction-format
     # https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/blob/main/tokenizer_config.json
-    from transformers import AutoTokenizer
+    from transformers import AutoTokenizer  # type: ignore
 
-    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
+    tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)  # type: ignore
 
     def format_autotokenizer(
         messages: List[llama_types.ChatCompletionRequestMessage],
         **kwargs: Any,
     ) -> ChatFormatterResponse:
-        tokenizer.use_default_system_prompt = False
+        tokenizer.use_default_system_prompt = False  # type: ignore
-        _prompt = tokenizer.apply_chat_template(messages, tokenize=False)
+        prompt: str = tokenizer.apply_chat_template(messages, tokenize=False)  # type: ignore
+        assert isinstance(prompt, str)
         # Return formatted prompt and eos token by default
-        return ChatFormatterResponse(prompt=_prompt, stop=tokenizer.eos_token)
+        return ChatFormatterResponse(prompt=prompt, stop=tokenizer.eos_token)
 
     return format_autotokenizer
 
 
+def hf_autotokenizer_to_chat_completion_handler(
+    pretrained_model_name_or_path: Union[str, os.PathLike[str]]
+) -> LlamaChatCompletionHandler:
+    chat_formatter = hf_autotokenizer_to_chat_formatter(pretrained_model_name_or_path)
+    return chat_formatter_to_chat_completion_handler(chat_formatter)
+
+
+def hf_tokenizer_config_to_chat_formatter(tokenizer_config: Dict[str, Any]) -> ChatFormatter:
+    assert isinstance(tokenizer_config, dict)
+
+    assert "chat_template" in tokenizer_config
+    assert isinstance(tokenizer_config["chat_template"], str)
+    chat_template = tokenizer_config["chat_template"]
+
+    assert "bos_token" in tokenizer_config
+    assert isinstance(tokenizer_config["bos_token"], str)
+    bos_token = tokenizer_config["bos_token"]
+
+    assert "eos_token" in tokenizer_config
+    assert isinstance(tokenizer_config["eos_token"], str)
+    eos_token = tokenizer_config["eos_token"]
+
+    env = jinja2.Environment(
+        loader=jinja2.BaseLoader(),
+        trim_blocks=True,
+        lstrip_blocks=True,
+    ).from_string(chat_template)
+
+    def format_autotokenizer(
+        messages: List[llama_types.ChatCompletionRequestMessage],
+        **kwargs: Any,
+    ) -> ChatFormatterResponse:
+        # TODO: veryify this is correct
+        # Add a blank assistant message to the end of the messages to prompt the model to generate a response
+        prompt = env.render(
+            messages=[
+                *messages,
+                llama_types.ChatCompletionRequestAssistantMessage(
+                    role="assistant", content=""
+                ),
+            ],
+            bos_token=bos_token,
+            eos_token=eos_token,
+        )
+        return ChatFormatterResponse(prompt=prompt, stop=eos_token)
+    return format_autotokenizer
+
+
+def hf_tokenizer_config_to_chat_completion_handler(
+    tokenizer_config: Dict[str, Any],
+) -> LlamaChatCompletionHandler:
+    chat_formatter = hf_tokenizer_config_to_chat_formatter(tokenizer_config)
+    return chat_formatter_to_chat_completion_handler(chat_formatter)
+
+
 # see https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/tokenization_llama.py
 # system prompt is "embedded" in the first message
 @register_chat_format("llama-2")
@@ -437,21 +544,23 @@ def format_alpaca(
     _prompt = _format_add_colon_two(system_message, _messages, _sep, _sep2)
     return ChatFormatterResponse(prompt=_prompt)
 
+
 @register_chat_format("qwen")
 def format_qwen(
     messages: List[llama_types.ChatCompletionRequestMessage],
     **kwargs: Any,
 ) -> ChatFormatterResponse:
     _roles = dict(user="<|im_start|>user", assistant="<|im_start|>assistant")
-    system_message="You are a helpful assistant."
+    system_message = "You are a helpful assistant."
-    system_template="<|im_start|>system\n{system_message}"
+    system_template = "<|im_start|>system\n{system_message}"
-    system_message=system_template.format(system_message=system_message)
+    system_message = system_template.format(system_message=system_message)
     _messages = _map_roles(messages, _roles)
     _messages.append((_roles["assistant"], None))
     _sep = "<|im_end|>"
     _prompt = _format_chatml(system_message, _messages, _sep)
     _sep2 = "<|endoftext|>"
-    return ChatFormatterResponse(prompt=_prompt,stop=_sep2)
+    return ChatFormatterResponse(prompt=_prompt, stop=_sep2)
+
 
 @register_chat_format("vicuna")
 def format(
@@ -650,6 +759,7 @@ def format_mistrallite(
     _prompt = _format_no_colon_single(system_message, _messages, _sep)
     return ChatFormatterResponse(prompt=_prompt)
 
+
 @register_chat_format("zephyr")
 def format_zephyr(
     messages: List[llama_types.ChatCompletionRequestMessage],
@@ -699,6 +809,7 @@ def format_chatml(
     _prompt = _format_chatml(system_message, _messages, _sep)
     return ChatFormatterResponse(prompt=_prompt, stop=_sep)
 
+
 @register_chat_format("chatglm3")
 def format_chatglm3(
     messages: List[llama_types.ChatCompletionRequestMessage],
@@ -739,7 +850,7 @@ def format_openchat(
 @register_chat_format("saiga")
 def format_saiga(
     messages: list[llama_types.ChatCompletionRequestMessage],
-    **kwargs,
+    **kwargs: Any,
 ) -> ChatFormatterResponse:
     _message_template = "<s>{role}\n{content}</s>"
     _roles = dict(user="user", bot="bot", system="system")

llama_cpp/llama_jinja_format.py

@@ -1,138 +0,0 @@
-"""
-llama_cpp/llama_jinja_format.py
-"""
-import dataclasses
-from typing import Any, Callable, Dict, List, Optional, Protocol, Union
-
-import jinja2
-from jinja2 import Template
-
-# NOTE: We sacrifice readability for usability.
-# It will fail to work as expected if we attempt to format it in a readable way.
-llama2_template = """{% for message in messages %}{% if message['role'] == 'user' %}[INST] {{ message['content'] }} [/INST]\n{% elif message['role'] == 'assistant' %}{{ message['content'] }}\n{% elif message['role'] == 'system' %}<<SYS>> {{ message['content'] }} <</SYS>>\n{% endif %}{% endfor %}"""
-
-
-class MetaSingleton(type):
-    """
-    Metaclass for implementing the Singleton pattern.
-    """
-
-    _instances = {}
-
-    def __call__(cls, *args, **kwargs):
-        if cls not in cls._instances:
-            cls._instances[cls] = super(MetaSingleton, cls).__call__(*args, **kwargs)
-        return cls._instances[cls]
-
-
-class Singleton(object, metaclass=MetaSingleton):
-    """
-    Base class for implementing the Singleton pattern.
-    """
-
-    def __init__(self):
-        super(Singleton, self).__init__()
-
-
-@dataclasses.dataclass
-class ChatFormatterResponse:
-    prompt: str
-    stop: Optional[Union[str, List[str]]] = None
-
-
-# Base Chat Formatter Protocol
-class ChatFormatterInterface(Protocol):
-    def __init__(self, template: Optional[object] = None):
-        ...
-
-    def __call__(
-        self,
-        messages: List[Dict[str, str]],
-        **kwargs,
-    ) -> ChatFormatterResponse:
-        ...
-
-    @property
-    def template(self) -> str:
-        ...
-
-
-class AutoChatFormatter(ChatFormatterInterface):
-    def __init__(
-        self,
-        template: Optional[str] = None,
-        template_class: Optional[Template] = None,
-    ):
-        if template is not None:
-            self._template = template
-        else:
-            self._template = llama2_template  # default template
-
-        self._environment = jinja2.Environment(
-            loader=jinja2.BaseLoader(),
-            trim_blocks=True,
-            lstrip_blocks=True,
-        ).from_string(
-            self._template,
-            template_class=template_class,
-        )
-
-    def __call__(
-        self,
-        messages: List[Dict[str, str]],
-        **kwargs: Any,
-    ) -> ChatFormatterResponse:
-        formatted_sequence = self._environment.render(messages=messages, **kwargs)
-        return ChatFormatterResponse(prompt=formatted_sequence)
-
-    @property
-    def template(self) -> str:
-        return self._template
-
-
-class FormatterNotFoundException(Exception):
-    pass
-
-
-class ChatFormatterFactory(Singleton):
-    _chat_formatters: Dict[str, Callable[[], ChatFormatterInterface]] = {}
-
-    def register_formatter(
-        self,
-        name: str,
-        formatter_callable: Callable[[], ChatFormatterInterface],
-        overwrite=False,
-    ):
-        if not overwrite and name in self._chat_formatters:
-            raise ValueError(
-                f"Formatter with name '{name}' is already registered. Use `overwrite=True` to overwrite it."
-            )
-        self._chat_formatters[name] = formatter_callable
-
-    def unregister_formatter(self, name: str):
-        if name in self._chat_formatters:
-            del self._chat_formatters[name]
-        else:
-            raise ValueError(f"No formatter registered under the name '{name}'.")
-
-    def get_formatter_by_name(self, name: str) -> ChatFormatterInterface:
-        try:
-            formatter_callable = self._chat_formatters[name]
-            return formatter_callable()
-        except KeyError:
-            raise FormatterNotFoundException(
-                f"Invalid chat format: {name} (valid formats: {list(self._chat_formatters.keys())})"
-            )
-
-
-# Define a chat format class
-class Llama2Formatter(AutoChatFormatter):
-    def __init__(self):
-        super().__init__(llama2_template)
-
-
-# With the Singleton pattern applied, regardless of where or how many times
-# ChatFormatterFactory() is called, it will always return the same instance
-# of the factory, ensuring that the factory's state is consistent throughout
-# the application.
-ChatFormatterFactory().register_formatter("llama-2", Llama2Formatter)

llama_cpp/server/model.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import json
+
 from typing import Dict, Optional, Union, List
 
 import llama_cpp
@@ -71,6 +73,24 @@ class LlamaProxy:
             chat_handler = llama_cpp.llama_chat_format.Llava15ChatHandler(
                 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
+            ), "hf_pretrained_model_name_or_path must be set for hf-autotokenizer"
+            chat_handler = (
+                llama_cpp.llama_chat_format.hf_autotokenizer_to_chat_formatter(
+                    settings.hf_pretrained_model_name_or_path
+                )
+            )
+        elif settings.chat_format == "hf-tokenizer-config":
+            assert (
+                settings.hf_tokenizer_config_path is not None
+            ), "hf_tokenizer_config_path must be set for hf-tokenizer-config"
+            chat_handler = (
+                llama_cpp.llama_chat_format.hf_tokenizer_config_to_chat_formatter(
+                    json.load(open(settings.hf_tokenizer_config_path))
+                )
+            )
 
         kv_overrides: Optional[Dict[str, Union[bool, int, float]]] = None
         if settings.kv_overrides is not None:
@@ -141,4 +161,3 @@ class LlamaProxy:
                 cache = llama_cpp.LlamaRAMCache(capacity_bytes=settings.cache_size)
             _model.set_cache(cache)
         return _model
-

llama_cpp/server/settings.py

@@ -134,6 +134,15 @@ class ModelSettings(BaseSettings):
         default=2 << 30,
         description="The size of the cache in bytes. Only used if cache is True.",
     )
+    # Tokenizer Options
+    hf_tokenizer_config_path: Optional[str] = Field(
+        default=None,
+        description="The path to a HuggingFace tokenizer_config.json file.",
+    )
+    hf_pretrained_model_name_or_path: Optional[str] = Field(
+        default=None,
+        description="The model name or path to a pretrained HuggingFace tokenizer model. Same as you would pass to AutoTokenizer.from_pretrained().",
+    )
     # Misc
     verbose: bool = Field(
         default=True, description="Whether to print debug information."

tests/test_llama_chat_format.py

@@ -1,50 +1,65 @@
-from typing import List
+import json
 
-import pytest
+from llama_cpp import (
-
+    ChatCompletionRequestUserMessage,
-from llama_cpp import ChatCompletionMessage
+)
-from llama_cpp.llama_jinja_format import Llama2Formatter
+from llama_cpp.llama_chat_format import hf_tokenizer_config_to_chat_formatter
 
 
-@pytest.fixture
+mistral_7b_tokenizer_config = """{
-def sequence_of_messages() -> List[ChatCompletionMessage]:
+  "add_bos_token": true,
-    return [
+  "add_eos_token": false,
-        ChatCompletionMessage(role="system", content="Welcome to CodeHelp Bot!"),
+  "added_tokens_decoder": {
-        ChatCompletionMessage(
+    "0": {
-            role="user", content="Hi there! I need some help with Python."
+      "content": "<unk>",
-        ),
+      "lstrip": false,
-        ChatCompletionMessage(
+      "normalized": false,
-            role="assistant", content="Of course! What do you need help with in Python?"
+      "rstrip": false,
-        ),
+      "single_word": false,
-        ChatCompletionMessage(
+      "special": true
-            role="user",
+    },
-            content="I'm trying to write a function to find the factorial of a number, but I'm stuck.",
+    "1": {
-        ),
+      "content": "<s>",
-        ChatCompletionMessage(
+      "lstrip": false,
-            role="assistant",
+      "normalized": false,
-            content="I can help with that! Would you like a recursive or iterative solution?",
+      "rstrip": false,
-        ),
+      "single_word": false,
-        ChatCompletionMessage(
+      "special": true
-            role="user", content="Let's go with a recursive solution."
+    },
-        ),
+    "2": {
-    ]
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
+}"""
 
 
-def test_llama2_formatter(sequence_of_messages):
+def test_hf_tokenizer_config_str_to_chat_formatter():
-    expected_prompt = (
+    tokenizer_config = json.loads(mistral_7b_tokenizer_config)
-        "<<SYS>> Welcome to CodeHelp Bot! <</SYS>>\n"
+    chat_formatter = hf_tokenizer_config_to_chat_formatter(
-        "[INST] Hi there! I need some help with Python. [/INST]\n"
+        tokenizer_config
-        "Of course! What do you need help with in Python?\n"
+    )
-        "[INST] I'm trying to write a function to find the factorial of a number, but I'm stuck. [/INST]\n"
+    chat_formatter_respoonse = chat_formatter(
-        "I can help with that! Would you like a recursive or iterative solution?\n"
+        messages=[
-        "[INST] Let's go with a recursive solution. [/INST]\n"
+            ChatCompletionRequestUserMessage(role="user", content="Hello, world!"),
+        ]
     )
 
-    llama2_formatter_instance = Llama2Formatter()
+    assert chat_formatter_respoonse.prompt == ("<s>[INST] Hello, world! [/INST]</s>" "")
-    formatter_response = llama2_formatter_instance(sequence_of_messages)
-    assert (
-        expected_prompt == formatter_response.prompt
-    ), "The formatted prompt does not match the expected output."
-
-
-# Optionally, include a test for the 'stop' if it's part of the functionality.