diff --git a/examples/llama-index-inference/README.md b/examples/llama-index-inference/README.md new file mode 100644 index 000000000..ba1fdc99a --- /dev/null +++ b/examples/llama-index-inference/README.md @@ -0,0 +1,25 @@ +# LLM Inference with `llama-index` and `llama.cpp` + +This AI / Machine Learning example shows how to run LLM inference from a local model downloaded from HuggingFace. It uses a mix of `conda-forge` packages and PyPi packages to get the proper, compatible versions. + +Run the example with: + +```bash +$ pixi run start +``` + +The source code is derived from [the Llama Index documentation](https://docs.llamaindex.ai/en/stable/examples/llm/llama_2_llama_cpp/). This particular set of tools and libraries was selected to show that production-grade deployments are possible with Pixi. The selected libraries in here are fairly lightweight and run a very advanced model locally. This was the performance I received on my local M1 Max machine: + +```bash +llama_print_timings: load time = 2043.14 ms +llama_print_timings: sample time = 22.51 ms / 247 runs ( 0.09 ms per token, 10973.88 tokens per second) +llama_print_timings: prompt eval time = 2043.03 ms / 71 tokens ( 28.78 ms per token, 34.75 tokens per second) +llama_print_timings: eval time = 17786.87 ms / 246 runs ( 72.30 ms per token, 13.83 tokens per second) +llama_print_timings: total time = 19959.11 ms / 317 tokens +``` + +Opportunities for improvement: + +- Modify for Linux / CUDA environments to demonstrate a more practical production stack. +- Enhance the pipeline with a RAG workflow, which is what Llama Index is good at. +- Experiment with different GGUF models for a quality / performance balance that fits your hardware. diff --git a/examples/llama-index-inference/inference.py b/examples/llama-index-inference/inference.py new file mode 100644 index 000000000..20a922255 --- /dev/null +++ b/examples/llama-index-inference/inference.py @@ -0,0 +1,33 @@ +from llama_index.llms.llama_cpp import LlamaCPP +from llama_index.llms.llama_cpp.llama_utils import ( + messages_to_prompt, + completion_to_prompt, +) + +# Source code derived from: https://docs.llamaindex.ai/en/stable/examples/llm/llama_2_llama_cpp/ + +model_url = "https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-v0.3-GGUF/resolve/main/Mistral-7B-Instruct-v0.3.Q5_K_M.gguf" + +llm = LlamaCPP( + # You can pass in the URL to a GGUF model to download it automatically + model_url=model_url, + # optionally, you can set the path to a pre-downloaded model instead of model_url + model_path=None, + temperature=0.1, + max_new_tokens=256, + # llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room + context_window=3900, + # kwargs to pass to __call__() + generate_kwargs={}, + # kwargs to pass to __init__() + # set to at least 1 to use GPU + model_kwargs={"n_gpu_layers": 1}, + # transform inputs into Llama2 format + messages_to_prompt=messages_to_prompt, + completion_to_prompt=completion_to_prompt, + verbose=True, +) + +response_iter = llm.stream_complete("Can you write me a poem about fast cars?") +for response in response_iter: + print(response.delta, end="", flush=True) diff --git a/examples/llama-index-inference/pixi.lock b/examples/llama-index-inference/pixi.lock new file mode 100644 index 000000000..cff1c565c --- /dev/null +++ b/examples/llama-index-inference/pixi.lock @@ -0,0 +1,3206 @@ +version: 5 +environments: + default: + channels: + - 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