baalajimaestro/llama.cpp
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Added both LlamaChache classes Disk and RAM.

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Maximilian-Winter 2023-05-31 22:33:56 +02:00
parent 9ea7a379d3
commit 29f9c9cca3
1 changed files with 263 additions and 193 deletions
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456
llama_cpp/llama.py
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@ -4,6 +4,7 @@ import uuid
import time import time
import math import math
import multiprocessing import multiprocessing
from abc import ABC
from typing import ( from typing import (
List, List,
Optional, Optional,
@ -26,21 +27,94 @@ import numpy as np
import numpy.typing as npt import numpy.typing as npt
class LlamaCache(ABC):
"""Base cache class for a llama.cpp model."""
class LlamaCache: def __init__(self, capacity_bytes: int = (2 << 30)):
"""Cache for a llama.cpp model.""" pass
@property
def cache_size(self):
return 0
def _find_longest_prefix_key(
self,
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
pass
def __getitem__(self, key: Sequence[int]) -> "LlamaState":
pass
def __contains__(self, key: Sequence[int]) -> bool:
pass
def __setitem__(self, key: Sequence[int], value: "LlamaState"):
pass
class LlamaRAMCache(LlamaCache):
"""Cache for a llama.cpp model using RAM."""
def __init__(self, capacity_bytes: int = (2 << 30)):
super().__init__(capacity_bytes)
self.capacity_bytes = capacity_bytes
self.cache_state: OrderedDict[Tuple[int, ...], "LlamaState"] = OrderedDict()
@property
def cache_size(self):
return sum([state.llama_state_size for state in self.cache_state.values()])
def _find_longest_prefix_key(
self,
key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]:
min_len = 0
min_key = None
keys = (
(k, Llama.longest_token_prefix(k, key)) for k in self.cache_state.keys()
)
for k, prefix_len in keys:
if prefix_len > min_len:
min_len = prefix_len
min_key = k
return min_key
def __getitem__(self, key: Sequence[int]) -> "LlamaState":
key = tuple(key)
_key = self._find_longest_prefix_key(key)
if _key is None:
raise KeyError("Key not found")
value = self.cache_state[_key]
self.cache_state.move_to_end(_key)
return value
def __contains__(self, key: Sequence[int]) -> bool:
return self._find_longest_prefix_key(tuple(key)) is not None
def __setitem__(self, key: Sequence[int], value: "LlamaState"):
key = tuple(key)
if key in self.cache_state:
del self.cache_state[key]
self.cache_state[key] = value
while self.cache_size > self.capacity_bytes:
self.cache_state.popitem(last=False)
class LlamaDiskCache(LlamaCache):
"""Cache for a llama.cpp model using disk."""
def __init__(self, cache_dir="./llama_cache", capacity_bytes: int = (2 << 30)): def __init__(self, cache_dir="./llama_cache", capacity_bytes: int = (2 << 30)):
super().__init__(capacity_bytes)
self.cache = diskcache.Cache(cache_dir) self.cache = diskcache.Cache(cache_dir)
self.capacity_bytes = capacity_bytes
@property @property
def cache_size(self): def cache_size(self):
return self.cache.volume() return self.cache.volume()
def _find_longest_prefix_key( def _find_longest_prefix_key(
self, self,
key: Tuple[int, ...], key: Tuple[int, ...],
) -> Optional[Tuple[int, ...]]: ) -> Optional[Tuple[int, ...]]:
min_len = 0 min_len = 0
min_key = None min_key = None
@ -60,9 +134,6 @@ class LlamaCache:
self.cache.push(_key) self.cache.push(_key)
return value return value
def __contains__(self, key: Sequence[int]) -> bool:
return self._find_longest_prefix_key(tuple(key)) is not None
def __setitem__(self, key: Sequence[int], value: "LlamaState"): def __setitem__(self, key: Sequence[int], value: "LlamaState"):
key = tuple(key) key = tuple(key)
if key in self.cache: if key in self.cache:
@ -73,16 +144,15 @@ class LlamaCache:
del self.cache[key_to_remove] del self.cache[key_to_remove]
class LlamaState: class LlamaState:
def __init__( def __init__(
self, self,
eval_tokens: Deque[int], eval_tokens: Deque[int],
eval_logits: Deque[List[float]], eval_logits: Deque[List[float]],
input_ids: npt.NDArray[np.intc], input_ids: npt.NDArray[np.intc],
scores: npt.NDArray[np.single], scores: npt.NDArray[np.single],
llama_state, # type: llama_cpp.Array[llama_cpp.c_uint8] llama_state, # type: llama_cpp.Array[llama_cpp.c_uint8]
llama_state_size: int, llama_state_size: int,
): ):
self.eval_tokens = eval_tokens self.eval_tokens = eval_tokens
self.eval_logits = eval_logits self.eval_logits = eval_logits
@ -114,25 +184,25 @@ class Llama:
"""High-level Python wrapper for a llama.cpp model.""" """High-level Python wrapper for a llama.cpp model."""
def __init__( def __init__(
self, self,
model_path: str, model_path: str,
# NOTE: These parameters are likely to change in the future. # NOTE: These parameters are likely to change in the future.
n_ctx: int = 512, n_ctx: int = 512,
n_parts: int = -1, n_parts: int = -1,
n_gpu_layers: int = 0, n_gpu_layers: int = 0,
seed: int = 1337, seed: int = 1337,
f16_kv: bool = True, f16_kv: bool = True,
logits_all: bool = False, logits_all: bool = False,
vocab_only: bool = False, vocab_only: bool = False,
use_mmap: bool = True, use_mmap: bool = True,
use_mlock: bool = False, use_mlock: bool = False,
embedding: bool = False, embedding: bool = False,
n_threads: Optional[int] = None, n_threads: Optional[int] = None,
n_batch: int = 512, n_batch: int = 512,
last_n_tokens_size: int = 64, last_n_tokens_size: int = 64,
lora_base: Optional[str] = None, lora_base: Optional[str] = None,
lora_path: Optional[str] = None, lora_path: Optional[str] = None,
verbose: bool = True, verbose: bool = True,
): ):
"""Load a llama.cpp model from `model_path`. """Load a llama.cpp model from `model_path`.
@ -201,12 +271,12 @@ class Llama:
if self.lora_path: if self.lora_path:
if llama_cpp.llama_apply_lora_from_file( if llama_cpp.llama_apply_lora_from_file(
self.ctx, self.ctx,
llama_cpp.c_char_p(self.lora_path.encode("utf-8")), llama_cpp.c_char_p(self.lora_path.encode("utf-8")),
llama_cpp.c_char_p(self.lora_base.encode("utf-8")) llama_cpp.c_char_p(self.lora_base.encode("utf-8"))
if self.lora_base is not None if self.lora_base is not None
else llama_cpp.c_char_p(0), else llama_cpp.c_char_p(0),
llama_cpp.c_int(self.n_threads), llama_cpp.c_int(self.n_threads),
): ):
raise RuntimeError( raise RuntimeError(
f"Failed to apply LoRA from lora path: {self.lora_path} to base path: {self.lora_base}" f"Failed to apply LoRA from lora path: {self.lora_path} to base path: {self.lora_base}"
@ -317,7 +387,7 @@ class Llama:
assert self.ctx is not None assert self.ctx is not None
n_ctx = self._n_ctx n_ctx = self._n_ctx
for i in range(0, len(tokens), self.n_batch): for i in range(0, len(tokens), self.n_batch):
batch = tokens[i : min(len(tokens), i + self.n_batch)] batch = tokens[i: min(len(tokens), i + self.n_batch)]
n_past = min(n_ctx - len(batch), len(self._input_ids)) n_past = min(n_ctx - len(batch), len(self._input_ids))
n_tokens = len(batch) n_tokens = len(batch)
return_code = llama_cpp.llama_eval( return_code = llama_cpp.llama_eval(
@ -339,28 +409,28 @@ class Llama:
n_vocab = self._n_vocab n_vocab = self._n_vocab
cols = n_vocab cols = n_vocab
logits_view = llama_cpp.llama_get_logits(self.ctx) logits_view = llama_cpp.llama_get_logits(self.ctx)
logits = [logits_view[i * cols : (i + 1) * cols] for i in range(rows)] logits = [logits_view[i * cols: (i + 1) * cols] for i in range(rows)]
self.eval_logits.extend(logits) self.eval_logits.extend(logits)
self._scores: npt.NDArray[np.single] = np.concatenate( self._scores: npt.NDArray[np.single] = np.concatenate(
(self._scores, np.array(logits, dtype=np.single)), axis=0 (self._scores, np.array(logits, dtype=np.single)), axis=0
) )
def _sample( def _sample(
self, self,
last_n_tokens_data, # type: llama_cpp.Array[llama_cpp.llama_token] last_n_tokens_data, # type: llama_cpp.Array[llama_cpp.llama_token]
last_n_tokens_size: llama_cpp.c_int, last_n_tokens_size: llama_cpp.c_int,
top_k: llama_cpp.c_int, top_k: llama_cpp.c_int,
top_p: llama_cpp.c_float, top_p: llama_cpp.c_float,
temp: llama_cpp.c_float, temp: llama_cpp.c_float,
tfs_z: llama_cpp.c_float, tfs_z: llama_cpp.c_float,
repeat_penalty: llama_cpp.c_float, repeat_penalty: llama_cpp.c_float,
frequency_penalty: llama_cpp.c_float, frequency_penalty: llama_cpp.c_float,
presence_penalty: llama_cpp.c_float, presence_penalty: llama_cpp.c_float,
mirostat_mode: llama_cpp.c_int, mirostat_mode: llama_cpp.c_int,
mirostat_tau: llama_cpp.c_float, mirostat_tau: llama_cpp.c_float,
mirostat_eta: llama_cpp.c_float, mirostat_eta: llama_cpp.c_float,
penalize_nl: bool = True, penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None, logits_processor: Optional[LogitsProcessorList] = None,
): ):
assert self.ctx is not None assert self.ctx is not None
assert len(self.eval_logits) > 0 assert len(self.eval_logits) > 0
@ -480,19 +550,19 @@ class Llama:
) )
def sample( def sample(
self, self,
top_k: int = 40, top_k: int = 40,
top_p: float = 0.95, top_p: float = 0.95,
temp: float = 0.80, temp: float = 0.80,
repeat_penalty: float = 1.1, repeat_penalty: float = 1.1,
frequency_penalty: float = 0.0, frequency_penalty: float = 0.0,
presence_penalty: float = 0.0, presence_penalty: float = 0.0,
tfs_z: float = 1.0, tfs_z: float = 1.0,
mirostat_mode: int = 0, mirostat_mode: int = 0,
mirostat_eta: float = 0.1, mirostat_eta: float = 0.1,
mirostat_tau: float = 5.0, mirostat_tau: float = 5.0,
penalize_nl: bool = True, penalize_nl: bool = True,
logits_processor: Optional[LogitsProcessorList] = None, logits_processor: Optional[LogitsProcessorList] = None,
): ):
"""Sample a token from the model. """Sample a token from the model.
@ -508,7 +578,7 @@ class Llama:
assert self.ctx is not None assert self.ctx is not None
last_n_tokens_data = [llama_cpp.llama_token(0)] * max( last_n_tokens_data = [llama_cpp.llama_token(0)] * max(
0, self.last_n_tokens_size - len(self._input_ids) 0, self.last_n_tokens_size - len(self._input_ids)
) + self._input_ids[-self.last_n_tokens_size :].tolist() ) + self._input_ids[-self.last_n_tokens_size:].tolist()
return self._sample( return self._sample(
last_n_tokens_data=(llama_cpp.llama_token * self.last_n_tokens_size)( last_n_tokens_data=(llama_cpp.llama_token * self.last_n_tokens_size)(
*last_n_tokens_data *last_n_tokens_data
@ -529,21 +599,21 @@ class Llama:
) )
def generate( def generate(
self, self,
tokens: Sequence[int], tokens: Sequence[int],
top_k: int = 40, top_k: int = 40,
top_p: float = 0.95, top_p: float = 0.95,
temp: float = 0.80, temp: float = 0.80,
repeat_penalty: float = 1.1, repeat_penalty: float = 1.1,
reset: bool = True, reset: bool = True,
frequency_penalty: float = 0.0, frequency_penalty: float = 0.0,
presence_penalty: float = 0.0, presence_penalty: float = 0.0,
tfs_z: float = 1.0, tfs_z: float = 1.0,
mirostat_mode: int = 0, mirostat_mode: int = 0,
mirostat_tau: float = 5.0, mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1, mirostat_eta: float = 0.1,
logits_processor: Optional[LogitsProcessorList] = None, logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None,
) -> Generator[int, Optional[Sequence[int]], None]: ) -> Generator[int, Optional[Sequence[int]], None]:
"""Create a generator of tokens from a prompt. """Create a generator of tokens from a prompt.
@ -606,7 +676,7 @@ class Llama:
logits_processor=logits_processor, logits_processor=logits_processor,
) )
if stopping_criteria is not None and stopping_criteria( if stopping_criteria is not None and stopping_criteria(
self._input_ids.tolist(), self._scores[-1, :].tolist() self._input_ids.tolist(), self._scores[-1, :].tolist()
): ):
return return
tokens_or_none = yield token tokens_or_none = yield token
@ -615,7 +685,7 @@ class Llama:
tokens.extend(tokens_or_none) tokens.extend(tokens_or_none)
def create_embedding( def create_embedding(
self, input: Union[str, List[str]], model: Optional[str] = None self, input: Union[str, List[str]], model: Optional[str] = None
) -> Embedding: ) -> Embedding:
"""Embed a string. """Embed a string.
@ -650,8 +720,8 @@ class Llama:
n_tokens = len(tokens) n_tokens = len(tokens)
total_tokens += n_tokens total_tokens += n_tokens
embedding = llama_cpp.llama_get_embeddings(self.ctx)[ embedding = llama_cpp.llama_get_embeddings(self.ctx)[
: llama_cpp.llama_n_embd(self.ctx) : llama_cpp.llama_n_embd(self.ctx)
] ]
data.append( data.append(
{ {
@ -685,27 +755,27 @@ class Llama:
return list(map(float, self.create_embedding(input)["data"][0]["embedding"])) return list(map(float, self.create_embedding(input)["data"][0]["embedding"]))
def _create_completion( def _create_completion(
self, self,
prompt: str, prompt: str,
suffix: Optional[str] = None, suffix: Optional[str] = None,
max_tokens: int = 16, max_tokens: int = 16,
temperature: float = 0.8, temperature: float = 0.8,
top_p: float = 0.95, top_p: float = 0.95,
logprobs: Optional[int] = None, logprobs: Optional[int] = None,
echo: bool = False, echo: bool = False,
stop: Optional[Union[str, List[str]]] = [], stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0, frequency_penalty: float = 0.0,
presence_penalty: float = 0.0, presence_penalty: float = 0.0,
repeat_penalty: float = 1.1, repeat_penalty: float = 1.1,
top_k: int = 40, top_k: int = 40,
stream: bool = False, stream: bool = False,
tfs_z: float = 1.0, tfs_z: float = 1.0,
mirostat_mode: int = 0, mirostat_mode: int = 0,
mirostat_tau: float = 5.0, mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1, mirostat_eta: float = 0.1,
model: Optional[str] = None, model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None, logits_processor: Optional[LogitsProcessorList] = None,
) -> Union[Iterator[Completion], Iterator[CompletionChunk]]: ) -> Union[Iterator[Completion], Iterator[CompletionChunk]]:
assert self.ctx is not None assert self.ctx is not None
@ -757,19 +827,19 @@ class Llama:
finish_reason = "length" finish_reason = "length"
multibyte_fix = 0 multibyte_fix = 0
for token in self.generate( for token in self.generate(
prompt_tokens, prompt_tokens,
top_k=top_k, top_k=top_k,
top_p=top_p, top_p=top_p,
temp=temperature, temp=temperature,
tfs_z=tfs_z, tfs_z=tfs_z,
mirostat_mode=mirostat_mode, mirostat_mode=mirostat_mode,
mirostat_tau=mirostat_tau, mirostat_tau=mirostat_tau,
mirostat_eta=mirostat_eta, mirostat_eta=mirostat_eta,
frequency_penalty=frequency_penalty, frequency_penalty=frequency_penalty,
presence_penalty=presence_penalty, presence_penalty=presence_penalty,
repeat_penalty=repeat_penalty, repeat_penalty=repeat_penalty,
stopping_criteria=stopping_criteria, stopping_criteria=stopping_criteria,
logits_processor=logits_processor, logits_processor=logits_processor,
): ):
if token == self._token_eos: if token == self._token_eos:
text = self.detokenize(completion_tokens) text = self.detokenize(completion_tokens)
@ -821,7 +891,7 @@ class Llama:
token_end_position += len(self.detokenize([token])) token_end_position += len(self.detokenize([token]))
# Check if stop sequence is in the token # Check if stop sequence is in the token
if token_end_position >= ( if token_end_position >= (
remaining_length - first_stop_position - 1 remaining_length - first_stop_position - 1
): ):
break break
logprobs_or_none: Optional[CompletionLogprobs] = None logprobs_or_none: Optional[CompletionLogprobs] = None
@ -882,7 +952,7 @@ class Llama:
break break
if stopping_criteria is not None and stopping_criteria( if stopping_criteria is not None and stopping_criteria(
self._input_ids.tolist(), self._scores[-1, :].tolist() self._input_ids.tolist(), self._scores[-1, :].tolist()
): ):
text = self.detokenize(completion_tokens) text = self.detokenize(completion_tokens)
finish_reason = "stop" finish_reason = "stop"
@ -947,8 +1017,8 @@ class Llama:
"choices": [ "choices": [
{ {
"text": last_text[ "text": last_text[
: len(last_text) - (token_end_position - end) : len(last_text) - (token_end_position - end)
].decode("utf-8", errors="ignore"), ].decode("utf-8", errors="ignore"),
"index": 0, "index": 0,
"logprobs": logprobs_or_none, "logprobs": logprobs_or_none,
"finish_reason": finish_reason, "finish_reason": finish_reason,
@ -1014,10 +1084,10 @@ class Llama:
for token in all_tokens for token in all_tokens
] ]
all_logprobs = [ all_logprobs = [
Llama.logits_to_logprobs(row.tolist()) for row in self._scores Llama.logits_to_logprobs(row.tolist()) for row in self._scores
][token_offset:] ][token_offset:]
for token, token_str, logprobs_token in zip( for token, token_str, logprobs_token in zip(
all_tokens, all_token_strs, all_logprobs all_tokens, all_token_strs, all_logprobs
): ):
text_offsets.append(text_offset) text_offsets.append(text_offset)
text_offset += len(token_str) text_offset += len(token_str)
@ -1068,27 +1138,27 @@ class Llama:
} }
def create_completion( def create_completion(
self, self,
prompt: str, prompt: str,
suffix: Optional[str] = None, suffix: Optional[str] = None,
max_tokens: int = 128, max_tokens: int = 128,
temperature: float = 0.8, temperature: float = 0.8,
top_p: float = 0.95, top_p: float = 0.95,
logprobs: Optional[int] = None, logprobs: Optional[int] = None,
echo: bool = False, echo: bool = False,
stop: Optional[Union[str, List[str]]] = [], stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0, frequency_penalty: float = 0.0,
presence_penalty: float = 0.0, presence_penalty: float = 0.0,
repeat_penalty: float = 1.1, repeat_penalty: float = 1.1,
top_k: int = 40, top_k: int = 40,
stream: bool = False, stream: bool = False,
tfs_z: float = 1.0, tfs_z: float = 1.0,
mirostat_mode: int = 0, mirostat_mode: int = 0,
mirostat_tau: float = 5.0, mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1, mirostat_eta: float = 0.1,
model: Optional[str] = None, model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None, logits_processor: Optional[LogitsProcessorList] = None,
) -> Union[Completion, Iterator[CompletionChunk]]: ) -> Union[Completion, Iterator[CompletionChunk]]:
"""Generate text from a prompt. """Generate text from a prompt.
@ -1141,27 +1211,27 @@ class Llama:
return completion return completion
def __call__( def __call__(
self, self,
prompt: str, prompt: str,
suffix: Optional[str] = None, suffix: Optional[str] = None,
max_tokens: int = 128, max_tokens: int = 128,
temperature: float = 0.8, temperature: float = 0.8,
top_p: float = 0.95, top_p: float = 0.95,
logprobs: Optional[int] = None, logprobs: Optional[int] = None,
echo: bool = False, echo: bool = False,
stop: Optional[Union[str, List[str]]] = [], stop: Optional[Union[str, List[str]]] = [],
frequency_penalty: float = 0.0, frequency_penalty: float = 0.0,
presence_penalty: float = 0.0, presence_penalty: float = 0.0,
repeat_penalty: float = 1.1, repeat_penalty: float = 1.1,
top_k: int = 40, top_k: int = 40,
stream: bool = False, stream: bool = False,
tfs_z: float = 1.0, tfs_z: float = 1.0,
mirostat_mode: int = 0, mirostat_mode: int = 0,
mirostat_tau: float = 5.0, mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1, mirostat_eta: float = 0.1,
model: Optional[str] = None, model: Optional[str] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None,
logits_processor: Optional[LogitsProcessorList] = None, logits_processor: Optional[LogitsProcessorList] = None,
) -> Union[Completion, Iterator[CompletionChunk]]: ) -> Union[Completion, Iterator[CompletionChunk]]:
"""Generate text from a prompt. """Generate text from a prompt.
@ -1209,7 +1279,7 @@ class Llama:
) )
def _convert_text_completion_to_chat( def _convert_text_completion_to_chat(
self, completion: Completion self, completion: Completion
) -> ChatCompletion: ) -> ChatCompletion:
return { return {
"id": "chat" + completion["id"], "id": "chat" + completion["id"],
@ -1230,8 +1300,8 @@ class Llama:
} }
def _convert_text_completion_chunks_to_chat( def _convert_text_completion_chunks_to_chat(
self, self,
chunks: Iterator[CompletionChunk], chunks: Iterator[CompletionChunk],
) -> Iterator[ChatCompletionChunk]: ) -> Iterator[ChatCompletionChunk]:
for i, chunk in enumerate(chunks): for i, chunk in enumerate(chunks):
if i == 0: if i == 0:
@ -1267,22 +1337,22 @@ class Llama:
} }
def create_chat_completion( def create_chat_completion(
self, self,
messages: List[ChatCompletionMessage], messages: List[ChatCompletionMessage],
temperature: float = 0.2, temperature: float = 0.2,
top_p: float = 0.95, top_p: float = 0.95,
top_k: int = 40, top_k: int = 40,
stream: bool = False, stream: bool = False,
stop: Optional[Union[str, List[str]]] = [], stop: Optional[Union[str, List[str]]] = [],
max_tokens: int = 256, max_tokens: int = 256,
presence_penalty: float = 0.0, presence_penalty: float = 0.0,
frequency_penalty: float = 0.0, frequency_penalty: float = 0.0,
repeat_penalty: float = 1.1, repeat_penalty: float = 1.1,
tfs_z: float = 1.0, tfs_z: float = 1.0,
mirostat_mode: int = 0, mirostat_mode: int = 0,
mirostat_tau: float = 5.0, mirostat_tau: float = 5.0,
mirostat_eta: float = 0.1, mirostat_eta: float = 0.1,
model: Optional[str] = None, model: Optional[str] = None,
) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]: ) -> Union[ChatCompletion, Iterator[ChatCompletionChunk]]:
"""Generate a chat completion from a list of messages. """Generate a chat completion from a list of messages.