Update settings fields and defaults
This commit is contained in:
Andrei Betlen
parent
86753976c4
commit
1a00e452ea
1 changed files with 55 additions and 39 deletions
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@ -13,18 +13,41 @@ from sse_starlette.sse import EventSourceResponse
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class Settings(BaseSettings):
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model: str
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n_ctx: int = 2048
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n_batch: int = 512
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n_threads: int = max((os.cpu_count() or 2) // 2, 1)
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f16_kv: bool = True
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use_mlock: bool = False # This causes a silent failure on platforms that don't support mlock (e.g. Windows) took forever to figure out...
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use_mmap: bool = True
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embedding: bool = True
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last_n_tokens_size: int = 64
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logits_all: bool = False
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cache: bool = False # WARNING: This is an experimental feature
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vocab_only: bool = False
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model: str = Field(
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description="The path to the model to use for generating completions."
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)
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n_ctx: int = Field(default=2048, ge=1, description="The context size.")
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n_batch: int = Field(
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default=512, ge=1, description="The batch size to use per eval."
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)
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n_threads: int = Field(
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default=max((os.cpu_count() or 2) // 2, 1),
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ge=1,
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description="The number of threads to use.",
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)
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f16_kv: bool = Field(default=True, description="Whether to use f16 key/value.")
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use_mlock: bool = Field(
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default=bool(llama_cpp.llama_mlock_supported().value),
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description="Use mlock.",
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)
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use_mmap: bool = Field(
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default=bool(llama_cpp.llama_mmap_supported().value),
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description="Use mmap.",
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)
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embedding: bool = Field(default=True, description="Whether to use embeddings.")
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last_n_tokens_size: int = Field(
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default=64,
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ge=0,
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description="Last n tokens to keep for repeat penalty calculation.",
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)
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logits_all: bool = Field(default=True, description="Whether to return logits.")
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cache: bool = Field(
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default=False,
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description="Use a cache to reduce processing times for evaluated prompts.",
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)
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vocab_only: bool = Field(
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default=False, description="Whether to only return the vocabulary."
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)
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router = APIRouter()
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@ -74,79 +97,75 @@ def get_llama():
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with llama_lock:
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yield llama
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model_field = Field(
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description="The model to use for generating completions."
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)
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model_field = Field(description="The model to use for generating completions.")
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max_tokens_field = Field(
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default=16,
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ge=1,
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le=2048,
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description="The maximum number of tokens to generate."
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default=16, ge=1, le=2048, description="The maximum number of tokens to generate."
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)
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temperature_field = Field(
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default=0.8,
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ge=0.0,
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le=2.0,
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description="Adjust the randomness of the generated text.\n\n" +
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"Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run."
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description="Adjust the randomness of the generated text.\n\n"
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+ "Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.",
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)
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top_p_field = Field(
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default=0.95,
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ge=0.0,
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le=1.0,
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description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n" +
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"Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text."
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description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n"
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+ "Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.",
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)
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stop_field = Field(
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default=None,
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description="A list of tokens at which to stop generation. If None, no stop tokens are used."
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description="A list of tokens at which to stop generation. If None, no stop tokens are used.",
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)
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stream_field = Field(
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default=False,
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description="Whether to stream the results as they are generated. Useful for chatbots."
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description="Whether to stream the results as they are generated. Useful for chatbots.",
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)
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top_k_field = Field(
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default=40,
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ge=0,
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description="Limit the next token selection to the K most probable tokens.\n\n" +
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"Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text."
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description="Limit the next token selection to the K most probable tokens.\n\n"
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+ "Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.",
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)
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repeat_penalty_field = Field(
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default=1.0,
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ge=0.0,
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description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n" +
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"Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient."
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description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n"
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+ "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.",
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)
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class CreateCompletionRequest(BaseModel):
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prompt: Optional[str] = Field(
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default="",
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description="The prompt to generate completions for."
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default="", description="The prompt to generate completions for."
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)
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suffix: Optional[str] = Field(
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default=None,
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description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots."
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description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots.",
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)
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max_tokens: int = max_tokens_field
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temperature: float = temperature_field
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top_p: float = top_p_field
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echo: bool = Field(
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default=False,
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description="Whether to echo the prompt in the generated text. Useful for chatbots."
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description="Whether to echo the prompt in the generated text. Useful for chatbots.",
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)
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stop: Optional[List[str]] = stop_field
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stream: bool = stream_field
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logprobs: Optional[int] = Field(
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default=None,
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ge=0,
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description="The number of logprobs to generate. If None, no logprobs are generated."
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description="The number of logprobs to generate. If None, no logprobs are generated.",
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)
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# ignored or currently unsupported
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@ -204,9 +223,7 @@ def create_completion(
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class CreateEmbeddingRequest(BaseModel):
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model: Optional[str] = model_field
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input: str = Field(
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description="The input to embed."
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)
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input: str = Field(description="The input to embed.")
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user: Optional[str]
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class Config:
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@ -239,8 +256,7 @@ class ChatCompletionRequestMessage(BaseModel):
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class CreateChatCompletionRequest(BaseModel):
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messages: List[ChatCompletionRequestMessage] = Field(
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default=[],
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description="A list of messages to generate completions for."
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default=[], description="A list of messages to generate completions for."
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)
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max_tokens: int = max_tokens_field
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temperature: float = temperature_field
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