SYMBOL INDEX (51 symbols across 7 files)

FILE: example_chat_completion.py
  function main (line 11) | def main(

FILE: example_text_completion.py
  function main (line 11) | def main(

FILE: llama/generation.py
  class CompletionPrediction (line 23) | class CompletionPrediction(TypedDict, total=False):
  class ChatPrediction (line 29) | class ChatPrediction(TypedDict, total=False):
  class Llama (line 35) | class Llama:
    method build (line 37) | def build(
    method __init__ (line 115) | def __init__(self, model: Transformer, tokenizer: Tokenizer):
    method generate (line 121) | def generate(
    method text_completion (line 229) | def text_completion(
    method chat_completion (line 280) | def chat_completion(
  function sample_top_p (line 343) | def sample_top_p(probs, p):

FILE: llama/model.py
  class ModelArgs (line 20) | class ModelArgs:
  class RMSNorm (line 35) | class RMSNorm(torch.nn.Module):
    method __init__ (line 36) | def __init__(self, dim: int, eps: float = 1e-6):
    method _norm (line 41) | def _norm(self, x):
    method forward (line 44) | def forward(self, x):
  function precompute_freqs_cis (line 49) | def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
  function reshape_for_broadcast (line 57) | def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
  function apply_rotary_emb (line 65) | def apply_rotary_emb(
  function repeat_kv (line 78) | def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
  class Attention (line 90) | class Attention(nn.Module):
    method __init__ (line 91) | def __init__(self, args: ModelArgs):
    method forward (line 146) | def forward(
  class FeedForward (line 193) | class FeedForward(nn.Module):
    method __init__ (line 194) | def __init__(
    method forward (line 218) | def forward(self, x):
  class TransformerBlock (line 222) | class TransformerBlock(nn.Module):
    method __init__ (line 223) | def __init__(self, layer_id: int, args: ModelArgs):
    method forward (line 239) | def forward(
  class Transformer (line 251) | class Transformer(nn.Module):
    method __init__ (line 252) | def __init__(self, params: ModelArgs):
    method forward (line 278) | def forward(self, tokens: torch.Tensor, start_pos: int):

FILE: llama/test_tokenizer.py
  class TokenizerTests (line 10) | class TokenizerTests(TestCase):
    method setUp (line 11) | def setUp(self):
    method test_special_tokens (line 15) | def test_special_tokens(self):
    method test_encode (line 21) | def test_encode(self):
    method test_decode (line 31) | def test_decode(self):
    method test_encode_message (line 39) | def test_encode_message(self):
    method test_encode_dialog (line 56) | def test_encode_dialog(self):

FILE: llama/tokenizer.py
  class Message (line 30) | class Message(TypedDict):
  class Tokenizer (line 38) | class Tokenizer:
    method __init__ (line 49) | def __init__(self, model_path: str):
    method encode (line 99) | def encode(
    method decode (line 162) | def decode(self, t: Sequence[int]) -> str:
    method _split_whitespaces_or_nonwhitespaces (line 176) | def _split_whitespaces_or_nonwhitespaces(
  class ChatFormat (line 202) | class ChatFormat:
    method __init__ (line 203) | def __init__(self, tokenizer: Tokenizer):
    method encode_header (line 206) | def encode_header(self, message: Message) -> List[int]:
    method encode_message (line 214) | def encode_message(self, message: Message) -> List[int]:
    method encode_dialog_prompt (line 222) | def encode_dialog_prompt(self, dialog: Dialog) -> List[int]:

FILE: setup.py
  function get_requirements (line 7) | def get_requirements(path: str):