| from typing import List, Optional |
| from cog import BasePredictor, Input |
| from transformers import LLaMAForCausalLM, LLaMATokenizer |
| import torch |
|
|
| from train import PROMPT_DICT |
| PROMPT = PROMPT_DICT['prompt_no_input'] |
|
|
| CACHE_DIR = 'alpaca_out' |
|
|
| class Predictor(BasePredictor): |
| def setup(self): |
| self.device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| self.model = LLaMAForCausalLM.from_pretrained("alpaca_out", cache_dir=CACHE_DIR, local_files_only=True) |
| self.model = self.model |
| self.model.to(self.device) |
| self.tokenizer = LLaMATokenizer.from_pretrained("alpaca_out", cache_dir=CACHE_DIR, local_files_only=True) |
|
|
| def predict( |
| self, |
| prompt: str = Input(description=f"Prompt to send to LLaMA."), |
| n: int = Input(description="Number of output sequences to generate", default=1, ge=1, le=5), |
| total_tokens: int = Input( |
| description="Maximum number of tokens for input + generation. A word is generally 2-3 tokens", |
| ge=1, |
| default=2000 |
| ), |
| temperature: float = Input( |
| description="Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.", |
| ge=0.01, |
| le=5, |
| default=0.75, |
| ), |
| top_p: float = Input( |
| description="When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens", |
| ge=0.01, |
| le=1.0, |
| default=1.0 |
| ), |
| repetition_penalty: float = Input( |
| description="Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.", |
| ge=0.01, |
| le=5, |
| default=1 |
| ) |
| ) -> List[str]: |
| format_prompt = PROMPT.format_map({'instruction': prompt}) |
| input = self.tokenizer(format_prompt, return_tensors="pt").input_ids.to(self.device) |
|
|
| outputs = self.model.generate( |
| input, |
| num_return_sequences=n, |
| max_length=total_tokens, |
| do_sample=True, |
| temperature=temperature, |
| top_p=top_p, |
| repetition_penalty=repetition_penalty |
| ) |
| out = self.tokenizer.batch_decode(outputs, skip_special_tokens=True) |
| |
| out = [val.split('Response:')[1] for val in out] |
| return out |
| |
