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368 | class MistralRS(CustomLLM):
r"""
MistralRS LLM.
Examples:
Install `mistralrs` following instructions:
https://github.com/EricLBuehler/mistral.rs/blob/master/mistralrs-pyo3/README.md#installation-from-pypi
Then `pip install llama-index-llms-mistral-rs`
This LLM provides automatic chat templating as an option. If you do not provide `messages_to_prompt`,
mistral.rs will automatically determine one. You can specify a JINJA chat template by passing it in
`model_kwargs` in the `chat_template` key.
```python
from llama_index.llms.mistral_rs import MistralRS
from mistralrs import Which
llm = MistralRS(
which = Which.XLora(
model_id=None, # Automatically determine from ordering file
tokenizer_json=None,
repeat_last_n=64,
xlora_model_id="lamm-mit/x-lora"
order="xlora-paper-ordering.json", # Make sure you copy the ordering file from `mistral.rs/orderings`
tgt_non_granular_index=None,
arch=Architecture.Mistral,
),
temperature=0.1,
max_new_tokens=256,
context_window=3900,
generate_kwargs={},
verbose=True,
)
response = llm.complete("Hello, how are you?")
print(str(response))
```
"""
model_url: Optional[str] = Field(description="local")
model_path: Optional[str] = Field(description="local")
temperature: float = Field(
default=DEFAULT_TEMPERATURE,
description="The temperature to use for sampling.",
ge=0.0,
le=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."
)
_runner: "Runner" = PrivateAttr("Mistral.rs model runner.")
_has_messages_to_prompt: bool = PrivateAttr("If `messages_to_prompt` is provided.")
def __init__(
self,
which: "Which",
temperature: float = DEFAULT_TEMPERATURE,
max_new_tokens: int = DEFAULT_NUM_OUTPUTS,
context_window: int = DEFAULT_CONTEXT_WINDOW,
top_k: int = DEFAULT_TOPK,
top_p: int = DEFAULT_TOPP,
frequency_penalty: Optional[float] = None,
presence_penalty: Optional[float] = None,
in_situ_quant: Optional[str] = None,
max_seqs: int = DEFAULT_MAX_SEQS,
token_source: str = "cache",
prefix_cache_n: str = DEFAULT_PREFIX_CACHE_N,
no_kv_cache: bool = False,
chat_template: Optional[str] = None,
top_logprobs: Optional[int] = None,
callback_manager: Optional[CallbackManager] = None,
generate_kwargs: Optional[Dict[str, Any]] = None,
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:
generate_kwargs = generate_kwargs or {}
generate_kwargs.update(
{
"temperature": temperature,
"max_tokens": max_new_tokens,
"top_k": top_k,
"top_p": top_p,
"top_logprobs": top_logprobs,
"logprobs": top_logprobs is not None,
"frequency_penalty": frequency_penalty,
"presence_penalty": presence_penalty,
}
)
super().__init__(
model_path="local",
model_url="local",
temperature=temperature,
context_window=context_window,
max_new_tokens=max_new_tokens,
callback_manager=callback_manager,
generate_kwargs=generate_kwargs,
model_kwargs={},
verbose=True,
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._runner = Runner(
which=which,
token_source=token_source,
max_seqs=max_seqs,
prefix_cache_n=prefix_cache_n,
no_kv_cache=no_kv_cache,
chat_template=chat_template,
in_situ_quant=in_situ_quant,
)
self._has_messages_to_prompt = messages_to_prompt is not None
@classmethod
def class_name(cls) -> str:
return "MistralRS"
@property
def metadata(self) -> LLMMetadata:
"""LLM metadata."""
return LLMMetadata(
context_window=self.context_window,
num_output=self.max_new_tokens,
model_name=self.model_path,
)
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
try:
from mistralrs import ChatCompletionRequest
except ImportError as e:
raise ValueError(
"Missing `mistralrs` package. Install via `pip install mistralrs`."
) from e
if self._has_messages_to_prompt:
messages = self.messages_to_prompt(messages)
else:
messages = llama_index_to_mistralrs_messages(messages)
self.generate_kwargs.update({"stream": False})
request = ChatCompletionRequest(
messages=messages,
model="",
logit_bias=None,
**self.generate_kwargs,
)
response = self._runner.send_chat_completion_request(request)
return CompletionResponse(
text=response.choices[0].message.content,
logprobs=extract_logprobs(response),
)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
try:
from mistralrs import ChatCompletionRequest
except ImportError as e:
raise ValueError(
"Missing `mistralrs` package. Install via `pip install mistralrs`."
) from e
if self._has_messages_to_prompt:
messages = self.messages_to_prompt(messages)
else:
messages = llama_index_to_mistralrs_messages(messages)
self.generate_kwargs.update({"stream": True})
request = ChatCompletionRequest(
messages=messages,
model="",
logit_bias=None,
**self.generate_kwargs,
)
streamer = self._runner.send_chat_completion_request(request)
def gen() -> CompletionResponseGen:
text = ""
for response in streamer:
delta = response.choices[0].delta.content
text += delta
yield ChatResponse(
message=ChatMessage(
role=MessageRole.ASSISTANT,
content=delta,
),
delta=delta,
logprobs=extract_logprobs_stream(response),
)
return gen()
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
try:
from mistralrs import ChatCompletionRequest
except ImportError as e:
raise ValueError(
"Missing `mistralrs` package. Install via `pip install mistralrs`."
) from e
self.generate_kwargs.update({"stream": False})
if not formatted:
prompt = self.completion_to_prompt(prompt)
request = ChatCompletionRequest(
messages=prompt,
model="",
logit_bias=None,
**self.generate_kwargs,
)
completion_response = self._runner.send_chat_completion_request(request)
return CompletionResponse(
text=completion_response.choices[0].message.content,
logprobs=extract_logprobs(completion_response),
)
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
try:
from mistralrs import ChatCompletionRequest
except ImportError as e:
raise ValueError(
"Missing `mistralrs` package. Install via `pip install mistralrs`."
) from e
self.generate_kwargs.update({"stream": True})
if not formatted:
prompt = self.completion_to_prompt(prompt)
request = ChatCompletionRequest(
messages=prompt,
model="",
logit_bias=None,
**self.generate_kwargs,
)
streamer = self._runner.send_chat_completion_request(request)
def gen() -> CompletionResponseGen:
text = ""
for response in streamer:
delta = response.choices[0].delta.content
text += delta
yield CompletionResponse(
delta=delta,
text=text,
logprobs=extract_logprobs_stream(response),
)
return gen()
|