serapeum-llama-cpp 0.2.0

llama cpp integration for serapeum

Serapeum llama.cpp Provider

Local GGUF model inference for the Serapeum LLM framework

The serapeum-llama-cpp package runs quantised GGUF models locally using the llama-cpp-python backend. It provides:

• Completion & Chat: Sync and async text generation with streaming
• Multiple Model Sources: Load from a local path, direct URL, or HuggingFace Hub
• Prompt Formatters: Ready-made formatters for Llama 2/Mistral and Llama 3 Instruct
• GPU Offloading: Configurable layer offloading via n_gpu_layers
• Thread Safety: Per-instance locking for concurrent inference
• Model Caching: Shared WeakValueDictionary cache avoids duplicate loads

This adapter implements the serapeum.core.llms.LLM completion interface with CompletionToChatMixin, making it compatible with all Serapeum orchestrators and tools.

Installation

From Source

cd libs/providers/llama-cpp
uv sync --active

From PyPI (when published)

pip install serapeum-llama-cpp

Prerequisites

Before running the examples you need a GGUF model file. For a quick test you can download the tiny stories260K model (~500 KB):

# Download the model
curl -L -o stories260K.gguf \
  https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K.gguf

or just copy and paste the URL into your browser, and the download will start.

Set the LLAMA_MODEL_PATH environment variable to the downloaded file:

export LLAMA_MODEL_PATH="/path/to/stories260K.gguf"

Or add it to a .env file in your project root and load it before running:

# .env
LLAMA_MODEL_PATH=/path/to/stories260K.gguf

from dotenv import load_dotenv
load_dotenv()  # loads LLAMA_MODEL_PATH from .env

All examples below read the model path from this environment variable.

Quick Start

import os
from dotenv import load_dotenv
from serapeum.llama_cpp import LlamaCPP
from serapeum.llama_cpp.formatters.llama3 import (
    messages_to_prompt_v3_instruct,
    completion_to_prompt_v3_instruct,
)

load_dotenv()
model_path = os.environ["LLAMA_MODEL_PATH"]

llm = LlamaCPP(
    model_path=model_path,
    messages_to_prompt=messages_to_prompt_v3_instruct,
    completion_to_prompt=completion_to_prompt_v3_instruct,
)

response = llm.complete("Once upon a time")
print(response.text)

Model Sources

Local File

import os
from dotenv import load_dotenv
from serapeum.llama_cpp import LlamaCPP
from serapeum.llama_cpp.formatters.llama3 import (
    messages_to_prompt_v3_instruct,
    completion_to_prompt_v3_instruct,
)

load_dotenv()

llm = LlamaCPP(
    model_path=os.environ["LLAMA_MODEL_PATH"],
    messages_to_prompt=messages_to_prompt_v3_instruct,
    completion_to_prompt=completion_to_prompt_v3_instruct,
)

Direct URL

The model is downloaded, cached, and reused on subsequent runs:

from serapeum.llama_cpp import LlamaCPP
from serapeum.llama_cpp.formatters.llama2 import (
    messages_to_prompt,
    completion_to_prompt,
)

llm = LlamaCPP(
    model_url="https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf",
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
)

HuggingFace Hub

Downloads via huggingface_hub with automatic caching and SHA-256 verification:

from serapeum.llama_cpp import LlamaCPP
from serapeum.llama_cpp.formatters.llama2 import (
    messages_to_prompt,
    completion_to_prompt,
)

llm = LlamaCPP(
    hf_model_id="TheBloke/Llama-2-13B-chat-GGUF",
    hf_filename="llama-2-13b-chat.Q4_0.gguf",
    messages_to_prompt=messages_to_prompt,
    completion_to_prompt=completion_to_prompt,
)

Prompt Formatters

GGUF models require a specific chat template. Using the wrong formatter produces garbage output. Choose the formatter that matches your model family:

# Llama 3 Instruct / newer models
from serapeum.llama_cpp.formatters.llama3 import (
    messages_to_prompt_v3_instruct,
    completion_to_prompt_v3_instruct,
)

# Llama 2 / Mistral
from serapeum.llama_cpp.formatters.llama2 import (
    messages_to_prompt,
    completion_to_prompt,
)

Streaming

import os
from dotenv import load_dotenv
from serapeum.llama_cpp import LlamaCPP
from serapeum.llama_cpp.formatters.llama3 import (
    messages_to_prompt_v3_instruct,
    completion_to_prompt_v3_instruct,
)

load_dotenv()

llm = LlamaCPP(
    model_path=os.environ["LLAMA_MODEL_PATH"],
    messages_to_prompt=messages_to_prompt_v3_instruct,
    completion_to_prompt=completion_to_prompt_v3_instruct,
)

for chunk in llm.complete("Once upon a time", stream=True):
    print(chunk.delta, end="", flush=True)
print()

Async

import asyncio
import os
from dotenv import load_dotenv
from serapeum.llama_cpp import LlamaCPP
from serapeum.llama_cpp.formatters.llama3 import (
    messages_to_prompt_v3_instruct,
    completion_to_prompt_v3_instruct,
)

load_dotenv()

llm = LlamaCPP(
    model_path=os.environ["LLAMA_MODEL_PATH"],
    messages_to_prompt=messages_to_prompt_v3_instruct,
    completion_to_prompt=completion_to_prompt_v3_instruct,
)

async def main():
    response = await llm.acomplete("Once upon a time")
    print(response.text)

asyncio.run(main())

Configuration

import os
from dotenv import load_dotenv
from serapeum.llama_cpp import LlamaCPP
from serapeum.llama_cpp.formatters.llama3 import (
    messages_to_prompt_v3_instruct,
    completion_to_prompt_v3_instruct,
)

load_dotenv()

llm = LlamaCPP(
    model_path=os.environ["LLAMA_MODEL_PATH"],
    temperature=0.1,              # Sampling temperature (0.0–1.0)
    max_new_tokens=256,           # Maximum tokens to generate
    context_window=4096,          # Context window size
    n_gpu_layers=-1,              # GPU layers (-1 = all)
    stop=["</s>", "<|eot_id|>"], # Stop sequences
    verbose=False,                # Suppress llama.cpp output
    generate_kwargs={},           # Extra kwargs for Llama.__call__
    model_kwargs={},              # Extra kwargs for Llama.__init__
    messages_to_prompt=messages_to_prompt_v3_instruct,
    completion_to_prompt=completion_to_prompt_v3_instruct,
)

Testing

# Unit and mock tests (no model required)
python -m pytest libs/providers/llama-cpp/tests -m "not e2e"

# End-to-end tests (requires a GGUF model)
# Set LLAMA_CPP_TEST_MODEL_PATH to your model file
python -m pytest libs/providers/llama-cpp/tests -m e2e

Links

• Homepage: github.com/Serapieum-of-alex/serapeum
• Documentation: serapieum-of-alex.github.io/serapeum
• llama-cpp-python: github.com/abetlen/llama-cpp-python