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docs: Update completion and chat_completion parameter docstrings

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Andrei Betlen 2023-11-24 03:24:19 -05:00
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llama_cpp/llama.py
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@ -1863,13 +1863,27 @@ class Llama:
suffix: A suffix to append to the generated text. If None, no suffix is appended.
max_tokens: The maximum number of tokens to generate. If max_tokens <= 0 or None, the maximum number of tokens to generate is unlimited and depends on n_ctx.
temperature: The temperature to use for sampling.
top_p: The top-p value to use for sampling.
top_p: The top-p value to use for nucleus sampling. Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
min_p: The min-p value to use for minimum p sampling. Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
typical_p: The typical-p value to use for sampling. Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
logprobs: The number of logprobs to return. If None, no logprobs are returned.
echo: Whether to echo the prompt.
stop: A list of strings to stop generation when encountered.
frequency_penalty: The penalty to apply to tokens based on their frequency in the prompt.
presence_penalty: The penalty to apply to tokens based on their presence in the prompt.
repeat_penalty: The penalty to apply to repeated tokens.
top_k: The top-k value to use for sampling.
top_k: The top-k value to use for sampling. Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
stream: Whether to stream the results.
seed: The seed to use for sampling.
tfs_z: The tail-free sampling parameter. Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
mirostat_mode: The mirostat sampling mode.
mirostat_tau: The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
mirostat_eta: The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
model: The name to use for the model in the completion object.
stopping_criteria: A list of stopping criteria to use.
logits_processor: A list of logits processors to use.
grammar: A grammar to use for constrained sampling.
logit_bias: A logit bias to use.
Raises:
ValueError: If the requested tokens exceed the context window.
@ -1944,15 +1958,29 @@ class Llama:
Args:
prompt: The prompt to generate text from.
suffix: A suffix to append to the generated text. If None, no suffix is appended.
max_tokens: The maximum number of tokens to generate. If max_tokens <= 0, the maximum number of tokens to generate is unlimited and depends on n_ctx.
max_tokens: The maximum number of tokens to generate. If max_tokens <= 0 or None, the maximum number of tokens to generate is unlimited and depends on n_ctx.
temperature: The temperature to use for sampling.
top_p: The top-p value to use for sampling.
top_p: The top-p value to use for nucleus sampling. Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
min_p: The min-p value to use for minimum p sampling. Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
typical_p: The typical-p value to use for sampling. Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
logprobs: The number of logprobs to return. If None, no logprobs are returned.
echo: Whether to echo the prompt.
stop: A list of strings to stop generation when encountered.
frequency_penalty: The penalty to apply to tokens based on their frequency in the prompt.
presence_penalty: The penalty to apply to tokens based on their presence in the prompt.
repeat_penalty: The penalty to apply to repeated tokens.
top_k: The top-k value to use for sampling.
top_k: The top-k value to use for sampling. Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
stream: Whether to stream the results.
seed: The seed to use for sampling.
tfs_z: The tail-free sampling parameter. Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
mirostat_mode: The mirostat sampling mode.
mirostat_tau: The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
mirostat_eta: The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
model: The name to use for the model in the completion object.
stopping_criteria: A list of stopping criteria to use.
logits_processor: A list of logits processors to use.
grammar: A grammar to use for constrained sampling.
logit_bias: A logit bias to use.
Raises:
ValueError: If the requested tokens exceed the context window.
@ -2024,13 +2052,31 @@ class Llama:
Args:
messages: A list of messages to generate a response for.
functions: A list of functions to use for the chat completion.
function_call: A function call to use for the chat completion.
tools: A list of tools to use for the chat completion.
tool_choice: A tool choice to use for the chat completion.
temperature: The temperature to use for sampling.
top_p: The top-p value to use for sampling.
top_k: The top-k value to use for sampling.
top_p: The top-p value to use for nucleus sampling. Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
top_k: The top-k value to use for sampling. Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
min_p: The min-p value to use for minimum p sampling. Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
typical_p: The typical-p value to use for sampling. Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
stream: Whether to stream the results.
stop: A list of strings to stop generation when encountered.
seed: The seed to use for sampling.
response_format: The response format to use for the chat completion. Use { "type": "json_object" } to contstrain output to only valid json.
max_tokens: The maximum number of tokens to generate. If max_tokens <= 0 or None, the maximum number of tokens to generate is unlimited and depends on n_ctx.
presence_penalty: The penalty to apply to tokens based on their presence in the prompt.
frequency_penalty: The penalty to apply to tokens based on their frequency in the prompt.
repeat_penalty: The penalty to apply to repeated tokens.
tfs_z: The tail-free sampling parameter.
mirostat_mode: The mirostat sampling mode.
mirostat_tau: The mirostat sampling tau parameter.
mirostat_eta: The mirostat sampling eta parameter.
model: The name to use for the model in the completion object.
logits_processor: A list of logits processors to use.
grammar: A grammar to use.
logit_bias: A logit bias to use.
Returns:
Generated chat completion or a stream of chat completion chunks.