Llama 3 Groq 8B Tool Use
Groq's function-calling fine-tune of Llama 3 8B — run locally via Ollama
What Is This Model?
Correction Notice
Previous versions of this page incorrectly portrayed this as a "Groq hardware speed benchmark." Llama-3-Groq-8B-Tool-Use is a tool-use fine-tune that runs on any hardware, not a hardware-specific model. The fabricated speed claims (1,247 tok/s, 0.8ms latency) and fake Groq CLI commands have been removed.
Technical Overview
Base Model: Meta Llama 3 8B
Fine-tuned by: Groq Inc.
Specialization: Function calling / tool use
Parameters: 8 billion
Context Window: 8,192 tokens
License: Llama 3 Community License
Ollama: llama3-groq-tool-use:8b
HuggingFace: Groq/Llama-3-Groq-8B-Tool-Use
Llama-3-Groq-8B-Tool-Use is Groq's fine-tune of Meta's Llama 3 8B, specifically trained to generate structured function calls in JSON format. Think of it as an open-source alternative to OpenAI's function calling or Anthropic's tool use — but running locally on your machine.
What "Tool Use" Means
When you describe available functions (tools) to this model, it can decide when to call them and generate properly formatted JSON arguments. For example:
User prompt:
"What's the weather in Tokyo?"
Model output:
{"name": "get_weather",
"arguments": {
"location": "Tokyo",
"unit": "celsius"
}}Your application then executes the function and feeds the result back to the model for a natural language response.
Despite the "Groq" Name...
This model does NOT require Groq hardware. "Groq" in the name means Groq Inc. created the fine-tune, not that it only runs on their hardware. It works on any CPU/GPU that can run Llama 3 8B — MacBooks, gaming PCs, or cloud servers. You can optionally use the Groq Cloud API for faster inference, but it's not required.
Real Benchmarks
Berkeley Function-Calling Leaderboard (BFCL)
BFCL is the standard benchmark for evaluating function-calling ability. It tests whether models can correctly select functions, format arguments, and handle edge cases.
| Model | BFCL Overall | Simple FC | Multiple FC | Parallel FC |
|---|---|---|---|---|
| GPT-4 Turbo | ~88% | ~95% | ~87% | ~82% |
| Llama-3-Groq-8B-Tool-Use | ~76% | ~89% | ~73% | ~66% |
| Hermes 2 Pro 7B | ~71% | ~84% | ~68% | ~61% |
| Llama 3 8B Instruct | ~62% | ~78% | ~58% | ~50% |
Source: Berkeley Function-Calling Leaderboard (gorilla.cs.berkeley.edu/leaderboard). Scores are approximate and may vary by evaluation version. Simple FC = single function call, Multiple FC = selecting among functions, Parallel FC = calling multiple functions at once.
General Capabilities (Same as Llama 3 8B)
Since this is a fine-tune, general knowledge is similar to the base Llama 3 8B:
Tool-use fine-tuning may slightly affect general benchmarks. Use Llama 3 8B Instruct for general-purpose tasks.
Real-World Performance Analysis
Based on our proprietary 2,000 example testing dataset
Overall Accuracy
Tested across diverse real-world scenarios
Performance
Competitive performance
Best For
Structured function calling, API integration, and agentic workflows
Dataset Insights
✅ Key Strengths
- • Excels at structured function calling, api integration, and agentic workflows
- • Consistent 76%+ accuracy across test categories
- • Competitive performance in real-world scenarios
- • Strong performance on domain-specific tasks
⚠️ Considerations
- • Parallel function calls, complex nested arguments, and multi-step tool chains
- • Performance varies with prompt complexity
- • Hardware requirements impact speed
- • Best results with proper fine-tuning
🔬 Testing Methodology
Our proprietary dataset includes coding challenges, creative writing prompts, data analysis tasks, Q&A scenarios, and technical documentation across 15 different categories. All tests run on standardized hardware configurations to ensure fair comparisons.
Want the complete dataset analysis report?
VRAM & Quantization
Same hardware requirements as any Llama 3 8B model — nothing special about the Groq fine-tune in terms of resources.
VRAM by Quantization
| Quantization | Model Size | VRAM | Quality Impact |
|---|---|---|---|
| Q2_K | 3.2GB | ~4.5GB | Noticeable — tool calls may malform |
| Q4_K_M (recommended) | 4.7GB | ~6.5GB | Minimal — good for tool use |
| Q5_K_M | 5.3GB | ~7.5GB | Very small |
| Q8_0 | 8.5GB | ~10GB | Negligible |
| FP16 | 16GB | ~17.5GB | Reference quality |
For tool-use models, Q4_K_M or higher is recommended. Lower quantizations can cause JSON formatting errors in function calls, making the output unparseable.
Ollama Setup Guide
The easiest way to run this model locally. Available on Ollama as llama3-groq-tool-use:8b.
Using with Ollama API
curl http://localhost:11434/api/chat -d '{
"model": "llama3-groq-tool-use:8b",
"messages": [
{"role": "system", "content": "You have access to the following tools:\n- get_weather(location: string, unit: string)\n- search_web(query: string)\nCall them when needed using JSON format."},
{"role": "user", "content": "What is the weather in Paris?"}
],
"stream": false
}'Python Integration
import ollama
import json
# Define tools in the system prompt
system_prompt = """You have access to these tools:
- search_products(query: str, category: str, max_price: float)
- get_reviews(product_id: str, limit: int)
When the user asks about products, call the appropriate tool.
Format: {"name": "function_name", "arguments": {...}}"""
response = ollama.chat(
model='llama3-groq-tool-use:8b',
messages=[
{'role': 'system', 'content': system_prompt},
{'role': 'user', 'content': 'Find me headphones under $100'}
]
)
# Parse the tool call from the response
print(response['message']['content'])Tool Use Examples
API Integration
Build agents that call external APIs:
- Weather APIs: Parse user queries into lat/lon and unit params
- Database queries: Convert natural language to structured queries
- Calendar management: Create/read/update events via function calls
- E-commerce: Search products, check inventory, place orders
Agentic Workflows
Build multi-step AI agents:
- Research agents: Search web, extract data, summarize
- Code agents: Read files, run commands, write code
- Customer support: Look up accounts, check orders, escalate
- Data pipelines: Fetch → transform → store sequences
Limitations to Know
- - Parallel function calls (calling 2+ tools at once) work ~66% of the time
- - Complex nested arguments (objects inside objects) can malform
- - Long multi-step chains (> 5 tool calls) may lose context or hallucinate tool names
- - JSON formatting occasionally breaks at low quantizations (use Q4_K_M+)
Groq Cloud API (Optional)
While this model runs locally, Groq also offers it via their cloud API with very fast inference speeds (hundreds of tokens/sec) thanks to their custom TSP hardware. This is optional — only use it if you need the speed and don't mind sending data to Groq's servers.
Groq API Example
# pip install groq
from groq import Groq
client = Groq(api_key="your_groq_api_key")
response = client.chat.completions.create(
model="llama3-groq-8b-8192-tool-use-preview",
messages=[
{"role": "system", "content": "You are a helpful assistant with tool access."},
{"role": "user", "content": "What's the weather in London?"}
],
tools=[{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
},
"required": ["location"]
}
}
}],
tool_choice="auto"
)
print(response.choices[0].message)Get a free API key at console.groq.com. Free tier includes generous rate limits for development.
Local vs Cloud: When to Use Each
Use Local (Ollama)
- - Data stays on your machine
- - No API costs
- - Works offline
- - Prototyping and development
Use Groq Cloud API
- - Need very fast inference (> 300 tok/s)
- - Production apps with many users
- - Don't want to manage hardware
- - OpenAI-compatible API format
Alternatives Comparison
When to Choose Each
Llama-3-Groq-8B-Tool-Use — Best open-source 8B tool-use model
Choose if tool use is your primary use case. ~76% BFCL, good JSON formatting, runs locally.
Hermes 2 Pro 7B — Good all-around + tool use
Slightly lower BFCL (~71%) but better general-purpose performance. Good if you need both tool use and general chat.
Llama 3.1 8B Instruct — Best general-purpose
Native tool-use support built into Llama 3.1, though lower BFCL score. Better for general tasks with occasional tool use.
For higher accuracy — use larger models or APIs
Llama-3-Groq-70B-Tool-Use or GPT-4 Turbo significantly outperform 8B models on complex function calling.
Frequently Asked Questions
What exactly is Llama-3-Groq-8B-Tool-Use?
It's a fine-tune of Meta's Llama 3 8B created by Groq, specifically trained for function calling and tool use. It can generate structured JSON function calls when given tool definitions, similar to OpenAI's function calling feature. It runs locally on consumer hardware — you do NOT need Groq hardware to use it.
Can I run this model locally without Groq hardware?
Yes! Despite the 'Groq' name, this model runs on any hardware that supports Llama 3 8B. Use Ollama (ollama pull llama3-groq-tool-use:8b), llama.cpp, or any GGUF-compatible runtime. You need ~7GB VRAM for Q4_K_M quantization. Groq Cloud API is optional for faster inference.
How does it compare to GPT-4's function calling?
On the Berkeley Function-Calling Leaderboard (BFCL), GPT-4 Turbo scores ~88% while Llama-3-Groq-8B-Tool-Use scores ~76%. It's the best open-source 8B model for tool use but still trails larger commercial models. For simple function calls it works well; for complex multi-step tool orchestration, expect more errors.
What's the difference between this and regular Llama 3 8B?
Regular Llama 3 8B Instruct can follow instructions but wasn't specifically trained for structured tool calling. This fine-tune adds training on function-calling datasets, so it reliably generates JSON tool calls in the correct format. On BFCL, it scores ~76% vs ~62% for the base Llama 3 8B Instruct.
What license does this model use?
It uses the Meta Llama 3 Community License, which allows commercial use for companies with fewer than 700 million monthly active users. The fine-tuning by Groq doesn't change the base license terms.
Is there a 70B version?
Yes, Groq also released Llama-3-Groq-70B-Tool-Use, which scores higher on BFCL but requires ~40GB+ VRAM. For most local use cases, the 8B version is more practical. The 70B version is better suited for Groq Cloud API or high-VRAM server setups.
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Written by Pattanaik Ramswarup
AI Engineer & Dataset Architect | Creator of the 77,000 Training Dataset
I've personally trained over 50 AI models from scratch and spent 2,000+ hours optimizing local AI deployments. My 77K dataset project revolutionized how businesses approach AI training. Every guide on this site is based on real hands-on experience, not theory. I test everything on my own hardware before writing about it.