Text Generation
GGUF
PyTorch
English
facebook
meta
llama
llama-3
groq
tool-use
function-calling
conversational
Instructions to use QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF with Ollama:
ollama run hf.co/QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Llama-3-Groq-8B-Tool-Use-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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---
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- groq
- tool-use
- function-calling
---

# QuantFactory/Llama-3-Groq-8B-Tool-Use-GGUF
This is quantized version of [Groq/Llama-3-Groq-8B-Tool-Use](https://huggingface.co/Groq/Llama-3-Groq-8B-Tool-Use) created using llama.cpp
# Original Model Card
# Llama-3-Groq-8B-Tool-Use
This is the 8B parameter version of the Llama 3 Groq Tool Use model, specifically designed for advanced tool use and function calling tasks.
## Model Details
- **Model Type:** Causal language model fine-tuned for tool use
- **Language(s):** English
- **License:** Meta Llama 3 Community License
- **Model Architecture:** Optimized transformer
- **Training Approach:** Full fine-tuning and Direct Preference Optimization (DPO) on Llama 3 8B base model
- **Input:** Text
- **Output:** Text, with enhanced capabilities for tool use and function calling
## Performance
- **Berkeley Function Calling Leaderboard (BFCL) Score:** 89.06% overall accuracy
- This score represents the best performance among all open-source 8B LLMs on the BFCL
## Usage and Limitations
This model is designed for research and development in tool use and function calling scenarios. It excels at tasks involving API interactions, structured data manipulation, and complex tool use. However, users should note:
- For general knowledge or open-ended tasks, a general-purpose language model may be more suitable
- The model may still produce inaccurate or biased content in some cases
- Users are responsible for implementing appropriate safety measures for their specific use case
Note the model is quite sensitive to the `temperature` and `top_p` sampling configuration. Start at `temperature=0.5, top_p=0.65` and move up or down as needed.
Text prompt example:
```
<|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|>
```
## Ethical Considerations
While fine-tuned for tool use, this model inherits the ethical considerations of the base Llama 3 model. Use responsibly and implement additional safeguards as needed for your application.
## Availability
The model is available through:
- [Groq API console](https://console.groq.com)
- [Hugging Face](https://huggingface.co/Groq/Llama-3-Groq-8B-Tool-Use)
For full details on responsible use, ethical considerations, and latest benchmarks, please refer to the [official Llama 3 documentation](https://llama.meta.com/) and the Groq model card.
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