Optimum Intel LLMs optimized with IPEX backend¶

Optimum Intel accelerates Hugging Face pipelines on Intel architectures leveraging Intel Extension for Pytorch, (IPEX) optimizations

Optimum Intel models can be run locally through OptimumIntelLLM entitiy wrapped by LlamaIndex :

In the below line, we install the packages necessary for this demo:

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%pip install llama-index-llms-optimum-intel

%pip install llama-index-llms-optimum-intel

Now that we're set up, let's play around:

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

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!pip install llama-index

!pip install llama-index

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from llama_index.llms.optimum_intel import OptimumIntelLLM

from llama_index.llms.optimum_intel import OptimumIntelLLM

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def messages_to_prompt(messages):
    prompt = ""
    for message in messages:
        if message.role == "system":
            prompt += f"<|system|>\n{message.content}</s>\n"
        elif message.role == "user":
            prompt += f"<|user|>\n{message.content}</s>\n"
        elif message.role == "assistant":
            prompt += f"<|assistant|>\n{message.content}</s>\n"

    # ensure we start with a system prompt, insert blank if needed
    if not prompt.startswith("<|system|>\n"):
        prompt = "<|system|>\n</s>\n" + prompt

    # add final assistant prompt
    prompt = prompt + "<|assistant|>\n"

    return prompt


def completion_to_prompt(completion):
    return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"

def messages_to_prompt(messages): prompt = "" for message in messages: if message.role == "system": prompt += f"<|system|>\n{message.content}\n" elif message.role == "user": prompt += f"<|user|>\n{message.content}\n" elif message.role == "assistant": prompt += f"<|assistant|>\n{message.content}\n" # ensure we start with a system prompt, insert blank if needed if not prompt.startswith("<|system|>\n"): prompt = "<|system|>\n\n" + prompt # add final assistant prompt prompt = prompt + "<|assistant|>\n" return prompt def completion_to_prompt(completion): return f"<|system|>\n\n<|user|>\n{completion}\n<|assistant|>\n"

Model Loading¶

Models can be loaded by specifying the model parameters using the OptimumIntelLLM method.

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oi_llm = OptimumIntelLLM(
    model_name="Intel/neural-chat-7b-v3-3",
    tokenizer_name="Intel/neural-chat-7b-v3-3",
    context_window=3900,
    max_new_tokens=256,
    generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95},
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
    device_map="cpu",
)

oi_llm = OptimumIntelLLM( model_name="Intel/neural-chat-7b-v3-3", tokenizer_name="Intel/neural-chat-7b-v3-3", context_window=3900, max_new_tokens=256, generate_kwargs={"temperature": 0.7, "top_k": 50, "top_p": 0.95}, messages_to_prompt=messages_to_prompt, completion_to_prompt=completion_to_prompt, device_map="cpu", )

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response = oi_llm.complete("What is the meaning of life?")
print(str(response))

response = oi_llm.complete("What is the meaning of life?") print(str(response))

Streaming¶

Using stream_complete endpoint

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response = oi_llm.stream_complete("Who is Mother Teresa?")
for r in response:
    print(r.delta, end="")

response = oi_llm.stream_complete("Who is Mother Teresa?") for r in response: print(r.delta, end="")

Using stream_chat endpoint

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from llama_index.core.llms import ChatMessage

messages = [
    ChatMessage(
        role="system",
        content="You are an American chef in a small restaurant in New Orleans",
    ),
    ChatMessage(role="user", content="What is your dish of the day?"),
]
resp = oi_llm.stream_chat(messages)

for r in resp:
    print(r.delta, end="")

from llama_index.core.llms import ChatMessage messages = [ ChatMessage( role="system", content="You are an American chef in a small restaurant in New Orleans", ), ChatMessage(role="user", content="What is your dish of the day?"), ] resp = oi_llm.stream_chat(messages) for r in resp: print(r.delta, end="")