Instructions to use Mungert/granite-4.0-h-micro-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mungert/granite-4.0-h-micro-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mungert/granite-4.0-h-micro-GGUF", dtype="auto")

llama-cpp-python

How to use Mungert/granite-4.0-h-micro-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mungert/granite-4.0-h-micro-GGUF",
	filename="granite-4.0-h-micro-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mungert/granite-4.0-h-micro-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mungert/granite-4.0-h-micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mungert/granite-4.0-h-micro-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mungert/granite-4.0-h-micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mungert/granite-4.0-h-micro-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 Mungert/granite-4.0-h-micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mungert/granite-4.0-h-micro-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 Mungert/granite-4.0-h-micro-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/granite-4.0-h-micro-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Mungert/granite-4.0-h-micro-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Mungert/granite-4.0-h-micro-GGUF with Ollama:
```
ollama run hf.co/Mungert/granite-4.0-h-micro-GGUF:Q4_K_M
```

Unsloth Studio

How to use Mungert/granite-4.0-h-micro-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/granite-4.0-h-micro-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Mungert/granite-4.0-h-micro-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mungert/granite-4.0-h-micro-GGUF to start chatting

How to use Mungert/granite-4.0-h-micro-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mungert/granite-4.0-h-micro-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Mungert/granite-4.0-h-micro-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mungert/granite-4.0-h-micro-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mungert/granite-4.0-h-micro-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Mungert/granite-4.0-h-micro-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Mungert/granite-4.0-h-micro-GGUF with Docker Model Runner:
```
docker model run hf.co/Mungert/granite-4.0-h-micro-GGUF:Q4_K_M
```

Lemonade

How to use Mungert/granite-4.0-h-micro-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mungert/granite-4.0-h-micro-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-4.0-h-micro-GGUF-Q4_K_M

List all available models

lemonade list

granite-4.0-h-micro-GGUF

File size: 39,088 Bytes

8b36ba6

---
license: apache-2.0
library_name: transformers
tags:
- language
- granite-4.0
---

# <span style="color: #7FFF7F;">granite-4.0-h-micro 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 [`ee09828cb`](https://github.com/ggerganov/llama.cpp/commit/ee09828cb057460b369576410601a3a09279e23c).





---

## <span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here:  
👉 [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py)

While this does increase model file size, it significantly improves precision for a given quantization level.

### **I'd love your feedback—have you tried this? How does it perform for you?**




---

<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-->


# Granite-4.0-H-Micro

📣 **Update [10-07-2025]:** Added a *default system prompt* to the chat template to guide the model towards more *professional, accurate, and safe* responses.

**Model Summary:**
Granite-4.0-H-Micro is a 3B parameter long-context instruct model finetuned from *Granite-4.0-H-Micro-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved *instruction following (IF)* and *tool-calling* capabilities, making them more effective in enterprise applications.

- **Developers:** Granite Team, IBM
- **HF Collection:** [Granite 4.0 Language Models HF Collection](https://huggingface.co/collections/ibm-granite/granite-40-language-models-6811a18b820ef362d9e5a82c)
- **GitHub Repository:** [ibm-granite/granite-4.0-language-models](https://github.com/ibm-granite/granite-4.0-language-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) 
- **Release Date**: October 2nd, 2025
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

**Supported Languages:** 
English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.0 models for languages beyond these languages.

**Intended use:** 
The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.

*Capabilities*
* Summarization
* Text classification
* Text extraction
* Question-answering
* Retrieval Augmented Generation (RAG)
* Code related tasks
* Function-calling tasks
* Multilingual dialog use cases
* Fill-In-the-Middle (FIM) code completions

<!-- <todo>Need to test the examples. (especially the tool calling and RAG ones)</todo>
 -->
 
**Generation:** 
This is a simple example of how to use Granite-4.0-H-Micro model.

Install the following libraries:

```shell
pip install torch torchvision torchaudio
pip install accelerate
pip install transformers
```
Then, copy the snippet from the section that is relevant for your use case.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
model_path = "ibm-granite/granite-4.0-h-micro"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
chat = [
    { "role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens, 
                        max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output[0])
```

Expected output:
```shell
<|start_of_role|>system<|end_of_role|>You are a helpful assistant. Please ensure responses are professional, accurate, and safe.<|end_of_text|>
<|start_of_role|>user<|end_of_role|>Please list one IBM Research laboratory located in the United States. You should only output its name and location.<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>Almaden Research Center, San Jose, California<|end_of_text|>
```

**Tool-calling:** 
Granite-4.0-H-Micro comes with enhanced tool calling capabilities, enabling seamless integration with external functions and APIs. To define a list of  tools please follow OpenAI's function [definition schema](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#defining-functions). 

This is an example of how to use Granite-4.0-H-Micro model tool-calling ability:

```python
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather for a specified city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "Name of the city"
                    }
                },
                "required": ["city"]
            }
        }
    }
]

# change input text as desired
chat = [
    { "role": "user", "content": "What's the weather like in Boston right now?" },
]
chat = tokenizer.apply_chat_template(chat, \
                                     tokenize=False, \
                                     tools=tools, \
                                     add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens, 
                        max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output[0])
```

Expected output:
```shell
<|start_of_role|>system<|end_of_role|>You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{"type": "function", "function": {"name": "get_current_weather", "description": "Get the current weather for a specified city.", "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "Name of the city"}}, "required": ["city"]}}}
</tools>

For each tool call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.<|end_of_text|>
<|start_of_role|>user<|end_of_role|>What's the weather like in Boston right now?<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|><tool_call>
{"name": "get_current_weather", "arguments": {"city": "Boston"}}
</tool_call><|end_of_text|>
```

<!-- **Retrieval Augmented Generation:** 
*Coming soon* -->

**Evaluation Results:** 

<table>
<!--   <caption><b> All Results</b></caption> -->
<thead>
  <tr>
    <th style="text-align:left; background-color: #001d6c; color: white;">Benchmarks</th>
    <th style="text-align:left; background-color: #001d6c; color: white;">Metric</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">Micro Dense</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">H Micro Dense</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">H Tiny MoE</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">H Small MoE</th>
  </tr>
</thead>
  <tbody>
<tr>
  <td colspan="6" style="text-align:center; background-color:  #FFFFFF; color: #2D2D2D; font-style:italic;">
    General Tasks
  </td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMLU</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">65.98</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">67.43</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">68.65</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">78.44</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMLU-Pro</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot, CoT</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">44.5</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">43.48</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">44.94</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">55.47</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">BBH</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">3-shot, CoT</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">72.48</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">69.36</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">66.34</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">81.62</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">AGI EVAL</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">64.29</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">59</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">62.15</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">70.63</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">GPQA</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">30.14</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">32.15</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">32.59</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">40.63</td>
</tr>
<tr>
  <td colspan="6" style="text-align:center; background-color:  #FFFFFF; color: #2D2D2D; font-style:italic;">
    Alignment Tasks
  </td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">AlpacaEval 2.0</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;"></td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">29.49</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">31.49</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">30.61</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">42.48</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">IFEval</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Instruct, Strict</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">85.5</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">86.94</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">84.78</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">89.87</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">IFEval</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Prompt, Strict</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">79.12</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">81.71</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">78.1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">85.22</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">IFEval</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Average</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">82.31</td>
    <td style="text-align:right; background-color:#DAE8FF; color: #2D2D2D;">84.32</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">81.44</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">87.55</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">ArenaHard</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">25.84</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">36.15</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">35.75</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">46.48</td>
</tr>
<tr>
  <td colspan="6" style="text-align:center; background-color:  #FFFFFF; color: #2D2D2D; font-style:italic;">
    Math Tasks
  </td>
</tr>      
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">GSM8K</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">8-shot</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">85.45</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">81.35</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">84.69</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">87.27</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">GSM8K Symbolic</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">8-shot</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">79.82</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">77.5</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">81.1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">87.38</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Minerva Math</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">62.06</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">66.44</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">69.64</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">74</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">DeepMind Math</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">44.56</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">43.83</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">49.92</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">59.33</td>
</tr>
<tr>
  <td colspan="6" style="text-align:center; background-color:  #FFFFFF; color: #2D2D2D; font-style:italic;">
    Code Tasks
  </td>
</tr> 
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">HumanEval</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">80</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">81</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">83</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">88</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">HumanEval+</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">72</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">75</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">76</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">83</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MBPP</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">72</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">73</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">80</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">84</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MBPP+</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">64</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">64</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">69</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">71</td>
</tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">CRUXEval-O</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">41.5</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">41.25</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">39.63</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">50.25</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">BigCodeBench</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">39.21</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">37.9</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">41.06</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">46.23</td>
</tr>
<tr>
  <td colspan="6" style="text-align:center; background-color:  #FFFFFF; color: #2D2D2D; font-style:italic;">
    Tool Calling Tasks
  </td>
</tr> 
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">BFCL v3</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">59.98</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">57.56</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">57.65</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">64.69</td>
</tr>
<tr>
  <td colspan="6" style="text-align:center; background-color:  #FFFFFF; color: #2D2D2D; font-style:italic;">
    Multilingual Tasks
  </td>
</tr> 
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MULTIPLE</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">49.21</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">49.46</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">55.83</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">57.37</td>
</tr> 
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMMLU</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">55.14</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">55.19</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">61.87</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">69.69</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">INCLUDE</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">51.62</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">50.51</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">53.12</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">63.97</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MGSM</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">8-shot</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">28.56</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">44.48</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">45.36</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">38.72</td>
</tr>
<tr>
  <td colspan="6" style="text-align:center; background-color:  #FFFFFF; color: #2D2D2D; font-style:italic;">
    Safety
  </td>
</tr> 
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">SALAD-Bench</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">97.06</td>
    <td style="text-align:right; background-color:#DAE8FF; color: #2D2D2D;">96.28</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">97.77</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">97.3</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">AttaQ</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">86.05</td>
    <td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">84.44</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">86.61</td>
    <td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">86.64</td>
</tr>
</tbody></table>


<table>
  <caption><b>Multilingual Benchmarks and thr included languages:</b></caption>
<thead>
  <tr>
    <th style="text-align:left; background-color: #001d6c; color: white;">Benchmarks</th>
    <th style="text-align:left; background-color: #001d6c; color: white;"># Langs</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">Languages</th>
  </tr>
</thead>
<tbody>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMMLU</td>
    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">11</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">ar, de, en, es, fr, ja, ko, pt, zh, bn, hi</td>
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">INCLUDE</td>
    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">14</td>
<!--     <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">hindi, bengali, tamil, telugu, arabic, german, spanish, french, italian, japanese, korean, dutch, portuguese, chinese</td> -->
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zh</td>
    
</tr>
<tr>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MGSM</td>
    <td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">5</td>
    <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">en, es, fr, ja, zh</td>
</tr>
</tbody>
</table>

**Model Architecture:** 
Granite-4.0-H-Micro baseline is built on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, Mamba2, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.

<table>
<thead>
  <tr>
    <th style="text-align:left; background-color: #001d6c; color: white;">Model</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">Micro Dense</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">H Micro Dense</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">H Tiny MoE</th>
    <th style="text-align:center; background-color: #001d6c; color: white;">H Small MoE</th>
  </tr></thead>
<tbody>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Embedding size</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">2560</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">2048</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">1536</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">4096</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of layers</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">40 attention</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">4 attention / 36 Mamba2</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">4 attention / 36 Mamba2</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">4 attention / 36 Mamba2</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Attention head size</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">64</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of attention heads</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">40</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">32</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">12</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of KV heads</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">8</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">4</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Mamba2 state size</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">128</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Number of Mamba2 heads</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">64</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">48</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
  </tr>

  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">MLP / Shared expert hidden size</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">8192</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">8192</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">1024</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">1536</td>
  </tr>
    

  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Num. Experts</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">72</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Num. active Experts</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">6</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">10</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Expert hidden size</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">-</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">512</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">768</td>
  </tr>

  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">MLP activation</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">SwiGLU</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
  </tr>

  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Sequence length</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">128K</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">128K</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;">Position embedding</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">RoPE</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">NoPE</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">NoPE</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">NoPE</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;"># Parameters</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">3B</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">3B</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">7B</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">32B</td>
  </tr>
  <tr>
    <td style="text-align:left; background-color: #FFFFFF; color: black;"># Active parameters</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">3B</td>
    <td style="text-align:center; background-color: #DAE8FF; color: black;">3B</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">1B</td>
    <td style="text-align:center; background-color: #FFFFFF; color: black;">9B</td>
  </tr>
</tbody></table>

**Training Data:** 
Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities, and (3) a select set of human-curated data.

**Infrastructure:**
We trained the Granite 4.0 Language Models utilizing an NVIDIA GB200 NVL72 cluster hosted in CoreWeave. Intra-rack communication occurs via the 72-GPU NVLink domain, and a non-blocking, full Fat-Tree NDR 400 Gb/s InfiniBand network provides inter-rack communication. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.

**Ethical Considerations and Limitations:** 
Granite 4.0 Instruction Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering multiple languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.

**Resources**
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources

<!-- ## Citation
```
@misc{granite-models,
  author = {author 1, author2, ...},
  title = {},
  journal = {},
  volume = {},
  year = {2024},
  url = {https://arxiv.org/abs/0000.00000},
}
``` -->

<!--End Original Model Card-->

---

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

Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**:  

👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme)  


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)

💬 **How to test**:  
 Choose an **AI assistant type**:  
   - `TurboLLM` (GPT-4.1-mini)  
   - `HugLLM` (Hugginface Open-source models)  
   - `TestLLM` (Experimental CPU-only)  

### **What I’m Testing**  
I’m pushing the limits of **small open-source models for AI network monitoring**, specifically:  
- **Function calling** against live network services  
- **How small can a model go** while still handling:  
  - Automated **Nmap security scans**  
  - **Quantum-readiness checks**  
  - **Network Monitoring tasks**  

🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):  
- ✅ **Zero-configuration setup**  
- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low.
- 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate!  

### **Other Assistants**  
🟢 **TurboLLM** – Uses **gpt-4.1-mini** :
- **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. 
- **Create custom cmd processors to run .net code on Quantum Network Monitor Agents**
- **Real-time network diagnostics and monitoring**
- **Security Audits**
- **Penetration testing** (Nmap/Metasploit)  

🔵 **HugLLM** – Latest Open-source models:  
- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

### 💡 **Example commands you could test**:  
1. `"Give me info on my websites SSL certificate"`  
2. `"Check if my server is using quantum safe encyption for communication"`  
3. `"Run a comprehensive security audit on my server"`
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution!

### 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! 😊