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