Jael Gu
1 year ago
4 changed files with 236 additions and 1 deletions
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# Llama-2 |
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# Llama-2 Chat |
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*author: Jael* |
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<br /> |
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## Description |
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A LLM operator generates answer given prompt in messages using a large language model or service. |
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This operator uses a pretrained [Llama-2](https://ai.meta.com/llama) to generate response. |
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By default, it will download the model file from [HuggingFace](https://huggingface.co/TheBloke) |
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and then run the model with [Llama-cpp](https://github.com/ggerganov/llama.cpp). |
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This operator will automatically install and run model with llama-cpp. |
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If the automatic installation fails in your environment, please refer to [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) for instructions of manual installation. |
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<br /> |
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## Code Example |
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Use the default model to continue the conversation from given messages. |
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*Write a pipeline with explicit inputs/outputs name specifications:* |
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```python |
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from towhee import pipe, ops |
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p = ( |
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pipe.input('question', 'docs', 'history') |
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.map(('question', 'docs', 'history'), 'prompt', ops.prompt.question_answer()) |
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.map('prompt', 'answer', ops.LLM.Llama_2('llama-2-7b-chat')) |
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.output('answer') |
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) |
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history=[('Who won the world series in 2020?', 'The Los Angeles Dodgers won the World Series in 2020.')] |
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question = 'Where was it played?' |
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answer = p(question, [], history).get()[0] |
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``` |
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*Write a [retrieval-augmented generation pipeline](https://towhee.io/tasks/detail/pipeline/retrieval-augmented-generation) with explicit inputs/outputs name specifications:* |
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```python |
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from towhee import pipe, ops |
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temp = '''Use the following pieces of context to answer the question at the end. |
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{context} |
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Question: {question} |
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''' |
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system_msg = 'Your name is TowheeChat.' |
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q1 = 'Who are you?' |
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q2 = 'What is Towhee?' |
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p = ( |
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pipe.input('question', 'docs', 'history') |
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.map(('question', 'docs', 'history'), |
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'prompt', |
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ops.prompt.template(temp, ['question', 'context'], sys_message)) |
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.map('prompt', 'answer', |
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ops.LLM.Llama_2(max_tokens=200)) |
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.output('answer') |
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) |
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history = [] |
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docs = [] |
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ans1 = p(q1, docs, history).get()[0] |
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print(q1, ans1) |
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history.append((q1, ans1)) |
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docs.append('Towhee is a cutting-edge framework designed to streamline the processing of unstructured data through the use of Large Language Model (LLM) based pipeline orchestration.') |
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ans2 = p(q2, docs, history) |
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print(q2, ans2) |
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``` |
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<br /> |
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## Factory Constructor |
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Create the operator via the following factory method: |
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***LLM.Llama_2(model_name_or_file: str)*** |
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**Parameters:** |
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***model_name_or_file***: *str* |
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The model name or path to the model file in string, defaults to 'llama-2-7b-chat'. |
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If model name is in `supported_model_names`, it will download corresponding model file from HuggingFace models. |
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You can also use the local path of a model file, which can be ran by llama-cpp-python. |
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***\*\*kwargs*** |
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Other model parameters such as temperature, max_tokens. |
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<br /> |
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## Interface |
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The operator takes a piece of text in string as input. |
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It returns answer in json. |
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***\_\_call\_\_(txt)*** |
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**Parameters:** |
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***messages***: *list* |
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​ A list of messages to set up chat. |
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Must be a list of dictionaries with key value from "system", "question", "answer". For example, [{"question": "a past question?", "answer": "a past answer."}, {"question": "current question?"}] |
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**Returns**: |
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*answer: str* |
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​ The answer generated. |
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<br /> |
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***supported_model_names()*** |
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**Returns**: |
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A dictionary of supported models with model name as key and huggingface hub id & model filename as value. |
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{ |
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'llama-2-7b-chat': { |
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'hf_id': 'TheBloke/Llama-2-7B-GGML', |
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'filename': 'llama-2-7b.ggmlv3.q4_0.bin' |
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}, |
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'llama-2-13-b-chat': { |
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'hf_id': 'TheBloke/Llama-2-13B-GGML', |
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'filename': 'llama-2-13b-chat.ggmlv3.q4_0.bin' |
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} |
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} |
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from .llama2 import LlamaCpp |
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def llama_2(*args, **kwargs): |
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return LlamaCpp(*args, **kwargs) |
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# Copyright 2021 Zilliz. All rights reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import os |
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from typing import List |
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from huggingface_hub import hf_hub_download |
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from llama_cpp import Llama |
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from towhee.operator.base import PyOperator, SharedType |
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class LlamaCpp(PyOperator): |
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'''Wrapper of Dolly inference''' |
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def __init__(self, |
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model_name_or_file: str = 'llama-2-7b-chat', |
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**kwargs |
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): |
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self.kwargs = kwargs |
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supported_model_names = self.supported_model_names() |
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if model_name_or_file in supported_model_names: |
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model_info = supported_model_names[model_name_or_file] |
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hf_id = model_info['hf_id'] |
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model_filename = model_info['filename'] |
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self.model_path = hf_hub_download(repo_id=hf_id, filename=model_filename) |
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else: |
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self.model_path = model_name_or_file |
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assert os.path.isfile(self.model_path), f'Invalid model path: {self.model_path}' |
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print(111, self.model_path) |
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self.model = Llama(model_path=self.model_path) |
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def __call__(self, messages: List[dict]): |
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prompt = self.parse_inputs(messages) |
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resp = self.model(prompt, **self.kwargs) |
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answer = self.parse_outputs(resp) |
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return answer |
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def parse_inputs(self, messages: List[dict]): |
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assert isinstance(messages, list), \ |
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'Inputs must be a list of dictionaries with keys from ["system", "question", "answer"].' |
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prompt = '' |
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question = messages.pop[-1] |
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assert len(question) == 1 and 'question' in question.keys() |
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question = question['question'] |
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for m in messages: |
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for k, v in m.items(): |
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if k == 'system': |
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prompt += f'''[INST] <<SYS>> {v} <</SYS>> [/INST]\n''' |
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elif k == 'question': |
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prompt += f'''[INST] {v} [/INST]\n''' |
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elif k == 'answer': |
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prompt += f'''{v}\n''' |
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else: |
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raise KeyError(f'Invalid key of message: {k}') |
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prompt = '<s> ' + prompt + ' </s>' + f'<s> [INST] {question} [/INST]' |
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return prompt |
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def parse_outputs(self, response): |
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return response['choices'][0]['text'] |
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@staticmethod |
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def supported_model_names(): |
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models = { |
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'llama-2-7b-chat': { |
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'hf_id': 'TheBloke/Llama-2-7B-GGML', |
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'filename': 'llama-2-7b.ggmlv3.q4_0.bin' |
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}, |
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'llama-2-13-b-chat': { |
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'hf_id': 'TheBloke/Llama-2-13B-GGML', |
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'filename': 'llama-2-13b-chat.ggmlv3.q4_0.bin' |
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} |
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} |
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return models |
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@property |
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def shared_type(self): |
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return SharedType.Shareable |
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llama-cpp-python |
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huggingface-hub |
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