---
base_model: Groq/Llama-3-Groq-8B-Tool-Use
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- groq
- tool-use
- function-calling
quantized_by: bartowski
lm_studio:
  param_count: 70b
  use_case: tools
  release_date: 17-07-2024
  model_creator: Groq
  prompt_template: Llama 3
  base_model: llama
  original_repo: Groq/Llama-3-Groq-70B-Tool-Use
---
## 💫 Community Model> Llama 3 70B Tool Use by Groq

*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.

**Model creator:** [Groq](https://huggingface.co./Groq)<br>
**Original model**: [Llama-3-Groq-70B-Tool-Use](https://huggingface.co./Groq/Llama-3-Groq-70B-Tool-Use)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co./bartowski) based on `llama.cpp` release [b3389](https://github.com/ggerganov/llama.cpp/releases/tag/b3389)<br>

## Model Summary:
This is a finetune of Llama 3 70B for tool use and function calling.<br>
It excels at, and should be used for, tasks involving API interactions, structured data manipulation, and complex tool use.

## Prompt template:

Choose the `Llama 3` preset in your LM Studio.

Under the hood, the model will see a prompt that's formatted like so:

```
<|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|>
```

## Technical Details

Scores 90.76% overall accuracy on the Berkeley Function Calling Leaderboard (BFCL)

Example tool use prompt:

```
<|start_header_id|>system<|end_header_id|>

You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"name": <function-name>,"arguments": <args-dict>}
</tool_call>

Here are the available tools:
<tools> {
    "name": "get_current_weather",
    "description": "Get the current weather in a given location",
    "parameters": {
        "properties": {
            "location": {
                "description": "The city and state, e.g. San Francisco, CA",
                "type": "string"
            },
            "unit": {
                "enum": [
                    "celsius",
                    "fahrenheit"
                ],
                "type": "string"
            }
        },
        "required": [
            "location"
        ],
        "type": "object"
    }
} </tools><|eot_id|><|start_header_id|>user<|end_header_id|>

What is the weather like in San Francisco?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

<tool_call>
{"id":"call_deok","name":"get_current_weather","arguments":{"location":"San Francisco","unit":"celsius"}}
</tool_call><|eot_id|><|start_header_id|>tool<|end_header_id|>

<tool_response>
{"id":"call_deok","result":{"temperature":"72","unit":"celsius"}}
</tool_response><|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Special thanks

🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

🙏 Special thanks to [Kalomaze](https://github.com/kalomaze) for his dataset (linked [here](https://github.com/ggerganov/llama.cpp/discussions/5263)) for imatrix calibration.

## Disclaimers

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