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299 | class LlamaCPP(CustomLLM):
r"""LlamaCPP LLM.
Examples:
Install llama-cpp-python following instructions:
https://github.com/abetlen/llama-cpp-python
Then `pip install llama-index-llms-llama-cpp`
```python
from llama_index.llms.llama_cpp import LlamaCPP
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"
model_url = "https://huggingface.co/TheBloke/zephyr-7B-beta-GGUF/resolve/main/zephyr-7b-beta.Q4_0.gguf"
llm = LlamaCPP(
model_url=model_url,
model_path=None,
temperature=0.1,
max_new_tokens=256,
context_window=3900,
generate_kwargs={},
model_kwargs={"n_gpu_layers": -1}, # if compiled to use GPU
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
verbose=True,
)
response = llm.complete("Hello, how are you?")
print(str(response))
```
"""
model_url: Optional[str] = Field(
description="The URL llama-cpp model to download and use."
)
model_path: Optional[str] = Field(
description="The path to the llama-cpp model to use."
)
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use for sampling.",
gte=0.0,
lte=1.0,
)
max_new_tokens: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="The maximum number of tokens to generate.",
gt=0,
)
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description="The maximum number of context tokens for the model.",
gt=0,
)
generate_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for generation."
)
model_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Kwargs used for model initialization."
)
verbose: bool = Field(
default=DEFAULT_LLAMA_CPP_MODEL_VERBOSITY,
description="Whether to print verbose output.",
)
_model: Any = PrivateAttr()
def __init__(
self,
model_url: Optional[str] = None,
model_path: Optional[str] = None,
temperature: float = DEFAULT_TEMPERATURE,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
context_window: int = DEFAULT_CONTEXT_WINDOW,
callback_manager: Optional[CallbackManager] = None,
generate_kwargs: Optional[Dict[str, Any]] = None,
model_kwargs: Optional[Dict[str, Any]] = None,
verbose: bool = DEFAULT_LLAMA_CPP_MODEL_VERBOSITY,
system_prompt: Optional[str] = None,
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:
model_kwargs = {
**{"n_ctx": context_window, "verbose": verbose},
**(model_kwargs or {}), # Override defaults via model_kwargs
}
# check if model is cached
if model_path is not None:
if not os.path.exists(model_path):
raise ValueError(
"Provided model path does not exist. "
"Please check the path or provide a model_url to download."
)
else:
model = Llama(model_path=model_path, **model_kwargs)
else:
cache_dir = get_cache_dir()
model_url = model_url or self._get_model_path_for_version()
model_name = os.path.basename(model_url)
model_path = os.path.join(cache_dir, "models", model_name)
if not os.path.exists(model_path):
os.makedirs(os.path.dirname(model_path), exist_ok=True)
self._download_url(model_url, model_path)
assert os.path.exists(model_path)
model = Llama(model_path=model_path, **model_kwargs)
model_path = model_path
generate_kwargs = generate_kwargs or {}
generate_kwargs.update(
{"temperature": temperature, "max_tokens": max_new_tokens}
)
super().__init__(
model_path=model_path,
model_url=model_url,
temperature=temperature,
context_window=context_window,
max_new_tokens=max_new_tokens,
callback_manager=callback_manager,
generate_kwargs=generate_kwargs,
model_kwargs=model_kwargs,
verbose=verbose,
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
@classmethod
def class_name(cls) -> str:
return "LlamaCPP_llm"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self._model.context_params.n_ctx,
num_output=self.max_new_tokens,
model_name=self.model_path,
)
def _get_model_path_for_version(self) -> str:
"""Get model path for the current llama-cpp version."""
import pkg_resources
version = pkg_resources.get_distribution("llama-cpp-python").version
major, minor, patch = version.split(".")
# NOTE: llama-cpp-python<=0.1.78 supports GGML, newer support GGUF
if int(major) <= 0 and int(minor) <= 1 and int(patch) <= 78:
return DEFAULT_LLAMA_CPP_GGML_MODEL
else:
return DEFAULT_LLAMA_CPP_GGUF_MODEL
def _download_url(self, model_url: str, model_path: str) -> None:
completed = False
try:
print("Downloading url", model_url, "to path", model_path)
with requests.get(model_url, stream=True) as r:
with open(model_path, "wb") as file:
total_size = int(r.headers.get("Content-Length") or "0")
if total_size < 1000 * 1000:
raise ValueError(
"Content should be at least 1 MB, but is only",
r.headers.get("Content-Length"),
"bytes",
)
print("total size (MB):", round(total_size / 1000 / 1000, 2))
chunk_size = 1024 * 1024 # 1 MB
for chunk in tqdm(
r.iter_content(chunk_size=chunk_size),
total=int(total_size / chunk_size),
):
file.write(chunk)
completed = True
except Exception as e:
print("Error downloading model:", e)
finally:
if not completed:
print("Download incomplete.", "Removing partially downloaded file.")
os.remove(model_path)
raise ValueError("Download incomplete.")
@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)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
self.generate_kwargs.update({"stream": False})
if not formatted:
prompt = self.completion_to_prompt(prompt)
response = self._model(prompt=prompt, **self.generate_kwargs)
return CompletionResponse(text=response["choices"][0]["text"], raw=response)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
self.generate_kwargs.update({"stream": True})
if not formatted:
prompt = self.completion_to_prompt(prompt)
response_iter = self._model(prompt=prompt, **self.generate_kwargs)
def gen() -> CompletionResponseGen:
text = ""
for response in response_iter:
delta = response["choices"][0]["text"]
text += delta
yield CompletionResponse(delta=delta, text=text, raw=response)
return gen()
|