---
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
datasets:
- quotientai/limbic-eval-tool-use-mcp
---

# <span style="color: #7FFF7F;">limbic-tool-use-0.5B-32K GGUF Models</span>


## <span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`c7f3169c`](https://github.com/ggerganov/llama.cpp/commit/c7f3169cd523140a288095f2d79befb20a0b73f4).






---

<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;">
  Click here to get info on choosing the right GGUF model format
</a>

---



<!--Begin Original Model Card-->


# Limbic-Tool-Use MCP Function Call Evaluator

This model is a fine-tuned version of Qwen2.5-0.5B-Instruct specifically designed for evaluating function calls in the context of Model Context Protocol (MCP) tools. It can assess whether a function call is correct, uses the wrong tool, has incorrect parameter names, or has incorrect parameter values.

## Model Details

- **Base Model**: [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Task**: Function Call Evaluation for MCP (Model Context Protocol)
- **Training Data**: MCP Server Tools data from public MCP servers, with augmentation / synthetic data generation
- **Model Size**: ~40MB (LoRA adapters only)
- **Context Length**: 32,768 tokens

# Model Usage

## Model Prompts

The prompt for the model takes two inputs: 
- `available_tools` - a list of the tool schemas
- `message_history` - the user request and model tool call response as a list of jsons

```
EVALUATOR_PROMPT = """\
# TOOL CALL EVALUATION RUBRIC

## EVALUATION CRITERIA

### 1. TOOL SELECTION
- [ ] Function name exists in available tools
- [ ] Function purpose matches user intent

### 2. PARAMETER STRUCTURE  
- [ ] All required and relevant parameters are present
- [ ] No hallucinated parameter names
- [ ] Parameter names match tool schema exactly

### 3. PARAMETER VALUES
- [ ] Data types match expected types
- [ ] Values align with user request
- [ ] No fabricated or incorrect values

## CLASSIFICATION RULES
- All criteria passed → `correct`
- Failed criteria 1 → `incorrect_tool`
- Failed criteria 2 → `incorrect_parameter_names`  
- Failed criteria 3 → `incorrect_parameter_values`

---
### AVAILABLE TOOLS
{available_tools}

---
### MESSAGE HISTORY
{message_history}

---
## OUTPUT REQUIREMENT
{{
    "score": < correct | incorrect_tool | incorrect_parameter_names | incorrect_parameter_values >,
    "reason": < [if incorrect, provide a brief list of reasons] >
}}

### EVALUATION:
"""
```
```
SYSTEM_PROMPT = "You are an expert evaluator of function calls. You will be given a function call and a list of available tools. You will need to evaluate the function call and return a score and a reason for the score."
```

### Example Inputs
```
available_tools = [
    {
        "name": "google-play-developer",
        "description": "Get apps by a developer on Google Play",
        "input_schema": {
            "type": "object",
            "properties": {
                "devId": {"type": "string", "description": "Developer ID"},
                "num": {"type": "number", "default": 60, "description": "Number of results"},
                "lang": {"type": "string", "default": "en", "description": "Language code"},
                "country": {"type": "string", "default": "us", "description": "Country code"}
            },
            "required": ["devId"]
        }
    }
]

message_history = [
    {"role": "user", "content": "I'm looking to evaluate the performance of all the apps developed by 'Example Developer' on the Google Play Store. Could you provide me with a list of their recent applications, specifically in English and focused on the US market? Please limit the results to 50 apps for a quicker review."},
    {"role": "assistant", "content": {"function": "name": "google-play-developer", "arguments": {"devId": "com.example.developer", "num": 50, "lang": "en", "country": "us"}}}
]
```

## Output Format
The model outputs evaluations in JSON format:

```json
{
    "score": "correct|incorrect_tool|incorrect_parameter_names|incorrect_parameter_values",
    "reason": ["reasons for failure if incorrect"]
}
```

#### Score Categories

- **correct**: Function call matches available tools and parameters exactly
- **incorrect_tool**: Function name doesn't exist in available tools
- **incorrect_parameter_names**: Function exists but parameter names are wrong
- **incorrect_parameter_values**: Function and parameters exist but values are inappropriate


## Load the Model
```
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("quotientai/limbic-tool-use-0.5B-32K")
model = AutoModelForCausalLM.from_pretrained("quotientai/limbic-tool-use-0.5B-32K")
```

## Generate a Prediction
To make a prediction, you must convert the formatted prompt into its chat format.
```
chat_template = [
  {"role": "system", "content": SYSTEM_PROMPT},
  {"role": "user", "content": "<your-formatted-user-prompt>"}
]
# Apply the chat template
text = tokenizer.apply_chat_template(chat_template, tokenize=False, add_generation_prompt=True)

# Tokenize with truncation
inputs = tokenizer(text, return_tensors="pt", truncation=True).to("cuda")

# Generate your prediction
result = model.generate(**inputs, max_new_tokens=128, use_cache=True)
```

## Citation
```bibtex
@model{limbic-tool-use-0.5B-32K,
  title={Limbic Tool Use Evaluator},
  author={QuotientAI},
  year={2025},
  url={https://huggingface.co/quotientai/limbic-tool-use-0.5B-32K}
}
```

<!--End Original Model Card-->

---

# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

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The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder)

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 Choose an **AI assistant type**:  
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I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
- **Function calling** against live network services  
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  - Automated **Nmap security scans**  
  - **Quantum-readiness checks**  
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🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
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### Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful.

If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊