4e6cc424f1
* run demo * format code * add llamaindex * add custom LLM with bigdl * update * add readme * begin ut * add unit test * add license * add license * revised * update * modify docs * remove data folder * update * modify prompt * fixed * fixed * fixed
88 lines
No EOL
3.2 KiB
Python
88 lines
No EOL
3.2 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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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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#
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from bigdl.llm.langchain.llms import TransformersLLM, TransformersPipelineLLM, \
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LlamaLLM, BloomLLM
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from bigdl.llm.langchain.embeddings import TransformersEmbeddings, LlamaEmbeddings, \
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BloomEmbeddings
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from langchain.document_loaders import WebBaseLoader
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from langchain.indexes import VectorstoreIndexCreator
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from langchain.chains.question_answering import load_qa_chain
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from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
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QA_PROMPT)
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.vectorstores import Chroma
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import pytest
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from unittest import TestCase
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import os
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from bigdl.llm.llamaindex.llms import BigdlLLM
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class Test_LlamaIndex_Transformers_API(TestCase):
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def setUp(self):
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self.auto_model_path = os.environ.get('ORIGINAL_CHATGLM2_6B_PATH')
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self.auto_causal_model_path = os.environ.get('ORIGINAL_REPLIT_CODE_PATH')
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self.llama_model_path = os.environ.get('LLAMA_ORIGIN_PATH')
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self.bloom_model_path = os.environ.get('BLOOM_ORIGIN_PATH')
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thread_num = os.environ.get('THREAD_NUM')
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if thread_num is not None:
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self.n_threads = int(thread_num)
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else:
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self.n_threads = 2
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def completion_to_prompt(completion):
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return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"
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def messages_to_prompt(messages):
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prompt = ""
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for message in messages:
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if message.role == "system":
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prompt += f"<|system|>\n{message.content}</s>\n"
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elif message.role == "user":
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prompt += f"<|user|>\n{message.content}</s>\n"
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elif message.role == "assistant":
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prompt += f"<|assistant|>\n{message.content}</s>\n"
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# ensure we start with a system prompt, insert blank if needed
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if not prompt.startswith("<|system|>\n"):
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prompt = "<|system|>\n</s>\n" + prompt
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# add final assistant prompt
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prompt = prompt + "<|assistant|>\n"
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return prompt
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def test_bigdl_llm(self):
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llm = BigdlLLM(
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model_name=self.llama_model_path,
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tokenizer_name=self.llama_model_path,
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context_window=512,
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max_new_tokens=32,
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model_kwargs={},
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generate_kwargs={"temperature": 0.7, "do_sample": False},
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messages_to_prompt=self.messages_to_prompt,
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completion_to_prompt=self.completion_to_prompt,
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device_map="cpu",
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)
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res = llm.complete("What is AI?")
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assert res!=None
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if __name__ == '__main__':
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pytest.main([__file__]) |