SYMBOL INDEX (47 symbols across 7 files)

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

FILE: example_infilling.py
  function main (line 9) | def main(

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

FILE: llama/generation.py
  class Message (line 32) | class Message(TypedDict):
  class InfillingPrediction (line 38) | class InfillingPrediction(TypedDict, total=False):
  class CompletionPrediction (line 45) | class CompletionPrediction(TypedDict, total=False):
  class ChatPrediction (line 51) | class ChatPrediction(TypedDict, total=False):
  class Llama (line 66) | class Llama:
    method build (line 68) | def build(
    method __init__ (line 128) | def __init__(self, model: Transformer, tokenizer: Tokenizer):
    method generate (line 133) | def generate(
    method text_completion (line 209) | def text_completion(
    method text_infilling (line 241) | def text_infilling(
    method chat_completion (line 298) | def chat_completion(
    method _chat_completion_turns (line 397) | def _chat_completion_turns(
  function sample_top_p (line 463) | def sample_top_p(probs, p):
  function infilling_prompt_tokens (line 474) | def infilling_prompt_tokens(
  function dialog_prompt_tokens (line 506) | def dialog_prompt_tokens(tokenizer: Tokenizer, dialog: Dialog) -> List[i...

FILE: llama/model.py
  class ModelArgs (line 26) | class ModelArgs:
  class RMSNorm (line 41) | class RMSNorm(torch.nn.Module):
    method __init__ (line 42) | def __init__(self, dim: int, eps: float = 1e-6):
    method _norm (line 47) | def _norm(self, x):
    method forward (line 50) | def forward(self, x):
  function precompute_freqs_cis (line 55) | def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
  function reshape_for_broadcast (line 64) | def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
  function apply_rotary_emb (line 72) | def apply_rotary_emb(
  function repeat_kv (line 88) | def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
  class Attention (line 100) | class Attention(nn.Module):
    method __init__ (line 101) | def __init__(self, args: ModelArgs):
    method forward (line 156) | def forward(
  class FeedForward (line 197) | class FeedForward(nn.Module):
    method __init__ (line 198) | def __init__(
    method forward (line 222) | def forward(self, x):
  class TransformerBlock (line 226) | class TransformerBlock(nn.Module):
    method __init__ (line 227) | def __init__(self, layer_id: int, args: ModelArgs):
    method forward (line 243) | def forward(
  class Transformer (line 257) | class Transformer(nn.Module):
    method __init__ (line 258) | def __init__(self, params: ModelArgs):
    method forward (line 284) | def forward(self, tokens: torch.Tensor, start_pos: int):

FILE: llama/tokenizer.py
  class Tokenizer (line 14) | class Tokenizer:
    method __init__ (line 15) | def __init__(self, model_path: str):
    method encode (line 42) | def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
    method decode (line 51) | def decode(self, t: List[int]) -> str:
    method token_piece (line 54) | def token_piece(self, t: int) -> str:
    method encode_infilling (line 57) | def encode_infilling(self, s: str) -> List[int]:
    method decode_infilling (line 61) | def decode_infilling(self, t: List[int]) -> str:

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