Update README.md
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README.md
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@@ -64,6 +64,56 @@ model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_ma
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generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
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```
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## Known Limitations
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generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer)
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```
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### LangChain Usage
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To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned
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and the default for the pipeline is to only return the new text.
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```
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import torch
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from transformers import pipeline
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generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16,
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trust_remote_code=True, device_map="auto", return_full_text=True)
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```
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You can create a prompt that either has only an instruction or has an instruction with context:
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```
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from langchain import PromptTemplate, LLMChain
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from langchain.llms import HuggingFacePipeline
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# template for an instrution with no input
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prompt = PromptTemplate(
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input_variables=["instruction"],
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template="{instruction}")
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# template for an instruction with input
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prompt_with_context = PromptTemplate(
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input_variables=["instruction", "context"],
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template="{instruction}\n\nInput:\n{context}")
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hf_pipeline = HuggingFacePipeline(pipeline=generate_text)
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llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
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llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)
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```
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Example predicting using a simple instruction:
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```
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print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip())
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```
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Example predicting using an instruction with context:
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```
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context = """George Washington (February 22, 1732[b] – December 14, 1799) was an American military officer, statesman,
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and Founding Father who served as the first president of the United States from 1789 to 1797."""
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print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip())
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```
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## Known Limitations
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