Llama 3 Groq Tool Use Model

This skill helps you download and run the Llama-3-Groq-8B-Tool-Use model, a specialized variant of Llama 3 optimized for tool use and function calling. The model is available in GGUF format with multiple quantization options to match your hardware capabilities.

What This Skill Does

Instructions

Step 1: Assess Hardware Resources

Determine available memory:

Step 2: Select Quantization Method

**Recommended K-Quants** (best compatibility):

**I-Quants** (better performance below Q4, for CUDA/ROCm only):

Note: I-quants are NOT compatible with Vulkan. Use K-quants for CPU, Apple Metal, or Vulkan backends.

Step 3: Download Model

Using huggingface-cli (install first with `pip install -U "huggingface_hub[cli]"`):

```bash

Example: Download Q4_K_M (recommended default)

huggingface-cli download bartowski/Llama-3-Groq-8B-Tool-Use-GGUF --include "Llama-3-Groq-8B-Tool-Use-Q4_K_M.gguf" --local-dir ./

For split files (models >50GB):

huggingface-cli download bartowski/Llama-3-Groq-8B-Tool-Use-GGUF --include "Llama-3-Groq-8B-Tool-Use-Q8_0.gguf/*" --local-dir ./models/

```

Step 4: Configure Prompt Format

The model uses Llama 3 Instruct format with special tokens:

```

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

```

Ensure your inference engine respects these tokens for proper tool use behavior.

Step 5: Load and Test

Load the model in your preferred tool:

Test with a simple tool use prompt to verify function calling works correctly.

Examples

**Example 1: GPU with 8GB VRAM**

**Example 2: CPU with 16GB RAM**

**Example 3: Limited RAM (8GB)**

Constraints

Additional Resources