Update README.md

README.md

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