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Update prompt template

Signed-off-by: Jael Gu <mengjia.gu@zilliz.com>
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Jael Gu 1 year ago
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c1a9544ef7
  1. 55
      README.md
  2. 5
      llama2.py

55
README.md

@ -20,61 +20,32 @@ If the automatic installation fails in your environment, please refer to [llama-
Use the default model to continue the conversation from given messages.
*Write a pipeline with explicit inputs/outputs name specifications:*
*Use operator:*
```python
from towhee import pipe, ops
from towhee import ops
p = (
pipe.input('question', 'docs', 'history')
.map(('question', 'docs', 'history'), 'prompt', ops.prompt.question_answer())
.map('prompt', 'answer', ops.LLM.Llama_2('llama-2-7b-chat'))
.output('answer')
)
chat = ops.LLM.Llama_2('path/to/model_file.bin', max_tokens=2048, echo=True)
history=[('Who won the world series in 2020?', 'The Los Angeles Dodgers won the World Series in 2020.')]
question = 'Where was it played?'
answer = p(question, [], history).get()[0]
message = [{"question": "Building a website can be done in 10 simple steps:"}]
answer = chat(message)
```
*Write a [retrieval-augmented generation pipeline](https://towhee.io/tasks/detail/pipeline/retrieval-augmented-generation) with explicit inputs/outputs name specifications:*
*Write a pipeline with explicit inputs/outputs name specifications:*
```python
from towhee import pipe, ops
temp = '''Use the following pieces of context to answer the question at the end.
{context}
Question: {question}
'''
system_msg = 'Your name is TowheeChat.'
q1 = 'Who are you?'
q2 = 'What is Towhee?'
p = (
pipe.input('question', 'docs', 'history')
.map(('question', 'docs', 'history'),
'prompt',
ops.prompt.template(temp, ['question', 'context'], system_msg))
.map('prompt', 'answer',
ops.LLM.Llama_2(temperature=0))
.map(('question', 'docs', 'history'), 'prompt', ops.prompt.question_answer())
.map('prompt', 'answer', ops.LLM.Llama_2('llama-2-7b-chat'))
.output('answer')
)
history = []
docs = []
ans1 = p(q1, docs, history).get()[0]
print(q1, ans1)
history.append((q1, ans1))
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.')
ans2 = p(q2, docs, history).get()[0]
print(q2, ans2)
history=[('Who won the world series in 2020?', 'The Los Angeles Dodgers won the World Series in 2020.')]
question = 'Where was it played?'
answer = p(question, [], history).get()[0]
```
<br />
@ -132,8 +103,8 @@ A dictionary of supported models with model name as key and huggingface hub id &
'hf_id': 'TheBloke/Llama-2-7B-GGML',
'filename': 'llama-2-7b.ggmlv3.q4_0.bin'
},
'llama-2-13-b-chat': {
'hf_id': 'TheBloke/Llama-2-13B-GGML',
'llama-2-13b-chat': {
'hf_id': 'TheBloke/Llama-2-13B-chat-GGML',
'filename': 'llama-2-13b-chat.ggmlv3.q4_0.bin'
}
}

5
llama2.py

@ -64,7 +64,10 @@ class LlamaCpp(PyOperator):
prompt += f'''{v}\n'''
else:
raise KeyError(f'Invalid key of message: {k}')
prompt = '<s> ' + prompt + ' </s>' + f'<s> [INST] {question} [/INST]'
if len(prompt) > 0:
prompt = '<s> ' + prompt + ' </s>' + f' {question}'
else:
prompt = question
return prompt
def parse_outputs(self, response):

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