model spits out a URL. Let's render it.poetry run pip install Pillow    Requirement already satisfied: Pillow in /Users/bagatur/langchain/.venv/lib/python3.9/site-packages (9.5.0)        [notice] A new release of pip is available: 23.2 -> 23.2.1    [notice] To update, run: pip install --upgrade pipfrom PIL import Imageimport requestsfrom io import BytesIOresponse = requests.get(image_output)img = Image.open(BytesIO(response.content))imgStreaming Response‚ÄãYou can optionally stream the response as it is produced, which is helpful to show interactivity to users for time-consuming generations. See detailed docs on Streaming for more information.from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Replicate(    streaming=True,    callbacks=[StreamingStdOutCallbackHandler()],    model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",    model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},)prompt = """User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?Assistant:"""_ = llm(prompt)    1. Dogs do not have the physical ability to operate a vehicle.Stop SequencesYou can also specify stop sequences. If you have a definite stop sequence for the generation that you are going to parse with anyway, it is better (cheaper and faster!) to just cancel the generation once one or more stop sequences are reached, rather than letting the model ramble on till the specified max_length. Stop sequences work regardless of whether you are in streaming mode or not, and Replicate only charges you for the generation up until the stop sequence.import timellm = Replicate(    model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",    model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},)prompt = """User: What is the best way to learn python?Assistant:"""start_time = time.perf_counter()raw_output = llm(prompt)
Replicate runs machine learning models in the cloud. We have a library of open-source models that you can run with a few lines of code. If you're building your own machine learning models, Replicate makes it easy to deploy them at scale.
Replicate runs machine learning models in the cloud. We have a library of open-source models that you can run with a few lines of code. If you're building your own machine learning models, Replicate makes it easy to deploy them at scale. ->: model spits out a URL. Let's render it.poetry run pip install Pillow    Requirement already satisfied: Pillow in /Users/bagatur/langchain/.venv/lib/python3.9/site-packages (9.5.0)        [notice] A new release of pip is available: 23.2 -> 23.2.1    [notice] To update, run: pip install --upgrade pipfrom PIL import Imageimport requestsfrom io import BytesIOresponse = requests.get(image_output)img = Image.open(BytesIO(response.content))imgStreaming Response‚ÄãYou can optionally stream the response as it is produced, which is helpful to show interactivity to users for time-consuming generations. See detailed docs on Streaming for more information.from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerllm = Replicate(    streaming=True,    callbacks=[StreamingStdOutCallbackHandler()],    model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",    model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},)prompt = """User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?Assistant:"""_ = llm(prompt)    1. Dogs do not have the physical ability to operate a vehicle.Stop SequencesYou can also specify stop sequences. If you have a definite stop sequence for the generation that you are going to parse with anyway, it is better (cheaper and faster!) to just cancel the generation once one or more stop sequences are reached, rather than letting the model ramble on till the specified max_length. Stop sequences work regardless of whether you are in streaming mode or not, and Replicate only charges you for the generation up until the stop sequence.import timellm = Replicate(    model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",    model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},)prompt = """User: What is the best way to learn python?Assistant:"""start_time = time.perf_counter()raw_output = llm(prompt)