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295 | class MLXLLM(CustomLLM):
r"""
MLX LLM.
Examples:
Thanks to the HuggingFace team for the example code.
`pip install llama-index-llms-MLXLLM`
```python
from llama_index.llms.mlx import MLXLLM
def messages_to_prompt(messages):
prompt = ""
for message in messages:
if message.role == 'system':
prompt += f"<|system|>\n{message.content}</s>\n"
elif message.role == 'user':
prompt += f"<|user|>\n{message.content}</s>\n"
elif message.role == 'assistant':
prompt += f"<|assistant|>\n{message.content}</s>\n"
# ensure we start with a system prompt, insert blank if needed
if not prompt.startswith("<|system|>\n"):
prompt = "<|system|>\n</s>\n" + prompt
# add final assistant prompt
prompt = prompt + "<|assistant|>\n"
return prompt
def completion_to_prompt(completion):
return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"
from llama_index.core.prompts import PromptTemplate
from llama_index.llms.mlx import MLXLLM
llm = MLXLLM(
model_name="microsoft/phi-2",
context_window=3900,
max_new_tokens=256,
generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
)
response = llm.complete("What is the meaning of life?")
print(str(response))
```
"""
model_name: str = Field(
default=DEFAULT_MLX_MODEL,
description=(
"The model name to use from HuggingFace. "
"Unused if `model` is passed in directly."
),
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of tokens available for input.",
gt=0,
)
max_new_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The maximum number of tokens to generate.",
gt=0,
)
system_prompt: str = Field(
default="",
description=(
"The system prompt, containing any extra instructions or context. "
"The model card on HuggingFace should specify if this is needed."
),
)
query_wrapper_prompt: PromptTemplate = Field(
default=PromptTemplate("{query_str}"),
description=(
"The query wrapper prompt, containing the query placeholder. "
"The model card on HuggingFace should specify if this is needed. "
"Should contain a `{query_str}` placeholder."
),
)
tokenizer_outputs_to_remove: list = Field(
default_factory=list,
description=(
"The outputs to remove from the tokenizer. "
"Sometimes huggingface tokenizers return extra inputs that cause errors."
),
)
tokenizer_kwargs: dict = Field(
default_factory=dict, description="The kwargs to pass to the tokenizer."
)
model_kwargs: dict = Field(
default_factory=dict,
description="The kwargs to pass to the model during initialization.",
)
generate_kwargs: dict = Field(
default_factory=dict,
description="The kwargs to pass to the model during generation.",
)
_model: Any = PrivateAttr()
_tokenizer: Any = PrivateAttr()
_stopping_criteria: Any = PrivateAttr()
def __init__(
self,
context_window: int = DEFAULT_CONTEXT_WINDOW,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
query_wrapper_prompt: Union[str, PromptTemplate] = "{query_str}",
model_name: str = DEFAULT_MLX_MODEL,
model: Optional[Any] = None,
tokenizer: Optional[Any] = None,
tokenizer_kwargs: Optional[dict] = None,
tokenizer_outputs_to_remove: Optional[list] = None,
model_kwargs: Optional[dict] = None,
generate_kwargs: Optional[dict] = None,
callback_manager: Optional[CallbackManager] = None,
system_prompt: str = "",
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
"""Initialize params."""
model_kwargs = model_kwargs or {}
if model is None:
model, tokenizer = load(model_name, **model_kwargs)
else:
model = model
tokenizer = tokenizer
# check context_window
tokenizer_kwargs = tokenizer_kwargs or {}
if "max_length" not in tokenizer_kwargs:
tokenizer_kwargs["max_length"] = context_window
if isinstance(query_wrapper_prompt, str):
query_wrapper_prompt = PromptTemplate(query_wrapper_prompt)
messages_to_prompt = messages_to_prompt or self._tokenizer_messages_to_prompt
super().__init__(
context_window=context_window,
max_new_tokens=max_new_tokens,
query_wrapper_prompt=query_wrapper_prompt,
tokenizer_name=model_name,
model_name=model_name,
tokenizer_kwargs=tokenizer_kwargs or {},
tokenizer_outputs_to_remove=tokenizer_outputs_to_remove or [],
model_kwargs=model_kwargs or {},
generate_kwargs=generate_kwargs or {},
callback_manager=callback_manager,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
self._model = model
self._tokenizer = tokenizer
@classmethod
def class_name(cls) -> str:
return "HuggingFace_LLM"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_new_tokens,
model_name=self.model_name,
)
def _tokenizer_messages_to_prompt(self, messages: Sequence[ChatMessage]) -> str:
"""Use the tokenizer to convert messages to prompt. Fallback to generic."""
if hasattr(self._tokenizer, "apply_chat_template"):
messages_dict = [
{"role": message.role.value, "content": message.content}
for message in messages
]
tokens = self._tokenizer.apply_chat_template(messages_dict)
return self._tokenizer.decode(tokens)
return generic_messages_to_prompt(messages)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
"""Completion endpoint."""
full_prompt = prompt
if not formatted:
if self.query_wrapper_prompt:
full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
if self.system_prompt:
full_prompt = f"{self.system_prompt} {full_prompt}"
completion = generate(
self._model,
self._tokenizer,
full_prompt,
max_tokens=self.max_new_tokens,
**self.generate_kwargs,
)
tokens = self._tokenizer.encode(completion, return_tensors="pt")
return CompletionResponse(text=completion, raw={"model_output": tokens})
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
"""Streaming completion endpoint."""
full_prompt = prompt
if not formatted:
if self.query_wrapper_prompt:
full_prompt = self.query_wrapper_prompt.format(query_str=prompt)
if self.system_prompt:
full_prompt = f"{self.system_prompt} {full_prompt}"
def gen() -> CompletionResponseGen:
text = ""
for x in gen_stream(
self._model,
self._tokenizer,
full_prompt,
max_tokens=self.max_new_tokens,
**self.generate_kwargs,
):
text += x
yield CompletionResponse(text=text, delta=x)
return gen()
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, formatted=True, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, formatted=True, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
|