Llama 3-based 8B model fine-tuned by Groq for function calling and tool use. Supports structured outputs and multi-turn conversations. Available in GGUF quantized formats for efficient local inference.
This skill provides guidance for using the Llama-3-Groq-8B-Tool-Use model, an 8B parameter model fine-tuned by Groq specifically for function calling and tool use capabilities.
Llama-3-Groq-8B-Tool-Use is a specialized version of Meta's Llama 3 model, optimized for:
The model is available in multiple quantization formats (Q2_K through Q8_0 and f16) to balance quality and performance based on your hardware constraints.
When a user asks about implementing or using the Llama 3 Groq Tool Use model, follow these steps:
1. **Determine Use Case**
- Ask what specific function calling or tool use scenario they need
- Clarify if they need local inference (GGUF) or API-based usage
- Understand their hardware constraints (RAM, GPU availability)
2. **Select Appropriate Quantization**
Based on available resources, recommend:
- **Q4_K_M (5.0 GB)**: Fast, recommended for most use cases
- **Q5_K_M (5.8 GB)**: Better quality with modest size increase
- **Q6_K (6.7 GB)**: Very good quality
- **Q8_0 (8.6 GB)**: Best quality for local inference
- **Q2_K/Q3_K**: For extremely limited resources (lower quality)
3. **Setup Instructions**
Provide guidance for:
- Installing a GGUF-compatible inference engine (llama.cpp, Ollama, LM Studio, etc.)
- Downloading the appropriate quantization from HuggingFace
- Configuring model parameters (context length, temperature, etc.)
4. **Function Calling Implementation**
Guide the user to:
- Define their function schemas in the expected format
- Structure prompts to trigger tool use
- Parse and execute the model's function call outputs
- Handle multi-turn conversations with tool results
5. **Example Code**
Provide working examples for:
- Loading the model in their chosen framework
- Defining function/tool schemas
- Making inference calls with function calling enabled
- Processing structured outputs
6. **Performance Optimization**
- Suggest batch size and context window settings
- Recommend GPU offloading strategies if applicable
- Explain trade-offs between quantization levels
```python
from llama_cpp import Llama
llm = Llama(
model_path="./Llama-3-Groq-8B-Tool-Use.Q4_K_M.gguf",
n_ctx=2048,
n_gpu_layers=-1 # Offload all layers to GPU if available
)
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"}
},
"required": ["location"]
}
}
}
]
response = llm.create_chat_completion(
messages=[
{"role": "user", "content": "What's the weather in San Francisco?"}
],
tools=tools
)
```
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