Llama 3 8B Function Calling

Use the Llama 3 8B model specifically optimized for function calling and tool use. This skill helps you work with GGUF quantized versions for efficient local inference.

Overview

This model is based on `serpdotai/llama-3-8B-function-calling` and provides weighted/imatrix quantization by mradermacher. It enables function calling capabilities similar to larger models but runs efficiently on consumer hardware.

Instructions

When the user requests to use or work with this model:

1. **Determine User Requirements**

- Ask about their hardware constraints (RAM, GPU)

- Determine quality vs speed preference

- Identify the intended use case (API server, CLI tool, integration)

2. **Recommend Appropriate Quantization**

Based on user needs, recommend from these tiers:

- **Optimal balanced**: Q4_K_S (4.8GB) or Q4_K_M (5.0GB)

- **Higher quality**: Q5_K_M (5.8GB) or Q6_K (6.7GB)

- **Maximum compression**: IQ3_S (3.8GB) or IQ4_XS (4.5GB)

- **Specialized**: Q4_0_4_4, Q4_0_4_8, Q4_0_8_8 for ARM devices

- **Avoid**: IQ1_S/IQ1_M (too low quality), Q2_K (IQ3_XXS is better)

3. **Provide Download Instructions**

- Direct user to appropriate GGUF file at `huggingface.co/mradermacher/llama-3-8B-function-calling-i1-GGUF`

- Or use huggingface-cli: `huggingface-cli download mradermacher/llama-3-8B-function-calling-i1-GGUF llama-3-8B-function-calling.i1-[QUANT].gguf`

4. **Setup for Inference**

Help user set up with their preferred tool:

- **llama.cpp**: Provide server or CLI commands

- **Ollama**: Create Modelfile and import instructions

- **LM Studio**: Guide to import and configure

- **Python (llama-cpp-python)**: Provide code example

5. **Function Calling Pattern**

Explain how to use function calling with this model:

- Format tool definitions in the model's expected schema

- Parse function call responses

- Execute functions and return results to model

- Handle multi-turn function calling conversations

6. **Optimization Tips**

- Context size recommendations (typically 4096-8192)

- Temperature and sampling settings for function calling

- Prompt templates for best results

- Batch size and thread count for performance

Example Usage

**User**: "I want to run Llama 3 function calling locally for a Python project"

**Response**:

1. Recommend Q4_K_M (5.0GB) for balanced performance

2. Provide download command

3. Share llama-cpp-python setup code with function calling example

4. Include prompt template for optimal function calling

**User**: "What's the smallest version that's still usable?"

**Response**:

Constraints

Notes