clean up llama cpp example, move out of misc (#895)

* clean up llama cpp example, move out of misc

* styling in title

06_gpu_and_ml/llm-serving/llama_cpp.py

@@ -0,0 +1,72 @@
+# # Run `llama.cpp` on Modal
+
+# [`llama.cpp`](https://github.com/ggerganov/llama.cpp) is a C++ library for running LLM inference.
+# It's lightweight, fast, and includes support for exotic quantizations like 5-bit integers.
+# This example shows how you can run `llama.cpp` on Modal.
+
+# We start by defining a container image with `llama.cpp` installed.
+
+import modal
+
+LLAMA_CPP_RELEASE = "b3472"
+
+image = (
+    modal.Image.debian_slim(python_version="3.11")
+    .apt_install(["curl", "unzip"])
+    .run_commands(
+        [
+            f"curl -L -O https://github.com/ggerganov/llama.cpp/releases/download/{LLAMA_CPP_RELEASE}/llama-{LLAMA_CPP_RELEASE}-bin-ubuntu-x64.zip",
+            f"unzip llama-{LLAMA_CPP_RELEASE}-bin-ubuntu-x64.zip",
+        ]
+    )
+)
+
+# Next, we download a pre-trained model to run.
+# We use a model with 5-bit quantization.
+# The model format, `.gguf`, is a custom format used by `llama.cpp`.
+
+MODEL_NAME = "Meta-Llama-3.1-8B-Instruct"
+MODEL_FILE = "Meta-Llama-3.1-8B-Instruct-Q5_K_M.gguf"
+REVISION = "9a8dec50f04fa8fad1dc1e7bc20a84a512e2bb01"
+
+image = image.run_commands(
+    f"curl --fail-with-body -L -O https://huggingface.co/bartowski/{MODEL_NAME}-GGUF/resolve/{REVISION}/{MODEL_FILE}?download=true"
+)
+
+# Now, we're ready to define a serverless function that runs `llama.cpp`.
+# We wrap that function with a decorator from a Modal App,
+# `@app.function` specifying the image it should run on
+# and setting the maximum number of concurrent replicas
+# (here, `100`, which is the default).
+
+app = modal.App("llama-cpp-modal", image=image)
+
+
+@app.function(image=image, concurrency_limit=100)
+def llama_cpp_inference(
+    prompt: str = None,
+    num_output_tokens: int = 128,
+):
+    import subprocess
+
+    if prompt is None:
+        prompt = "Write a poem about New York City.\n"
+    if num_output_tokens is None:
+        num_output_tokens = 128
+
+    subprocess.run(
+        [
+            "/build/bin/llama-cli",
+            "-m",
+            f"/{MODEL_FILE}",
+            "-n",
+            str(num_output_tokens),
+            "-p",
+            prompt,
+        ]
+    )
+
+
+@app.local_entrypoint()
+def main(prompt: str = None, num_output_tokens: int = None):
+    llama_cpp_inference.remote(prompt, num_output_tokens)