Integration Llama Cpp

llama.cpp Integration

🎯 Trigger Conditions

Use when asked about running DSPy programs with llama.cpp, CPU inference, or GGUF model support.

📚 Prerequisites

• llama-cpp-python package installed
• GGUF model available
• Python 3.8+

🛠️ llama.cpp Integration Patterns

1. Basic llama.cpp Setup

from llama_cpp import Llama

# Load GGUF model
llm = Llama(
    model_path="./model.gguf",
    n_ctx=2048,
    n_threads=8
)

# Generate
output = llm(
    "Your prompt",
    max_tokens=1024,
    temperature=0.7
)

2. llama.cpp with DSPy

import dspy
from llama_cpp import Llama

# Create llama.cpp engine
llama = Llama(model_path="./model.gguf")

# Create DSPy LM wrapper
class LlamaCPP_LM(dspy.LM):
    def __init__(self, llama):
        super().__init__()
        self.llama = llama
    
    def __call__(self, prompt, **kwargs):
        # Convert to llama.cpp format
        output = self.llama(prompt, **kwargs)
        return [output["choices"][0]["text"]]

# Use with DSPy
llama_lm = LlamaCPP_LM(llama)
dspy.settings.configure(lm=llama_lm)

3. Quantization Options

# Different quantization levels
models = {
    "q4_k_m": "./model-q4_k_m.gguf",  # Good balance
    "q8_0": "./model-q8_0.gguf",  # Higher quality
    "q2_k": "./model-q2_k.gguf"  # Lower quality, smaller size
}

llm = Llama(model_path=models["q4_k_m"])

4. Performance Tuning

llm = Llama(
    model_path="./model.gguf",
    # CPU settings
    n_threads=8,
    n_batch=512,
    # GPU offload
    n_gpu_layers=35,
    # Context
    n_ctx=2048
)

⚠️ Pitfalls

• Speed: llama.cpp is slower than GPU inference
• Quality: Quantization affects output quality
• Memory: Large models require significant RAM
• Compatibility: Not all models support GGUF

📖 References

• llama.cpp Documentation
• DSPy llama.cpp