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| ''' | |
| Based on | |
| https://github.com/abetlen/llama-cpp-python | |
| Documentation: | |
| https://abetlen.github.io/llama-cpp-python/ | |
| ''' | |
| from llama_cpp import Llama, LlamaCache | |
| from modules import shared | |
| from modules.callbacks import Iteratorize | |
| class LlamaCppModel: | |
| def __init__(self): | |
| self.initialized = False | |
| def from_pretrained(self, path): | |
| result = self() | |
| params = { | |
| 'model_path': str(path), | |
| 'n_ctx': 2048, | |
| 'seed': 0, | |
| 'n_threads': shared.args.threads or None, | |
| 'n_batch': shared.args.n_batch, | |
| 'use_mmap': not shared.args.no_mmap, | |
| 'use_mlock': shared.args.mlock | |
| } | |
| self.model = Llama(**params) | |
| self.model.set_cache(LlamaCache) | |
| # This is ugly, but the model and the tokenizer are the same object in this library. | |
| return result, result | |
| def encode(self, string): | |
| if type(string) is str: | |
| string = string.encode() | |
| return self.model.tokenize(string) | |
| def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None): | |
| if type(context) is str: | |
| context = context.encode() | |
| tokens = self.model.tokenize(context) | |
| output = b"" | |
| count = 0 | |
| for token in self.model.generate(tokens, top_k=top_k, top_p=top_p, temp=temperature, repeat_penalty=repetition_penalty): | |
| text = self.model.detokenize([token]) | |
| output += text | |
| if callback: | |
| callback(text.decode()) | |
| count += 1 | |
| if count >= token_count or (token == self.model.token_eos()): | |
| break | |
| return output.decode() | |
| def generate_with_streaming(self, **kwargs): | |
| with Iteratorize(self.generate, kwargs, callback=None) as generator: | |
| reply = '' | |
| for token in generator: | |
| reply += token | |
| yield reply | |