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README.md

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+---
+base_model: LGAI-EXAONE/EXAONE-3.5-32B-Instruct
+base_model_relation: finetune
+license: other
+license_name: exaone
+license_link: LICENSE
+language:
+- en
+- ko
+tags:
+- lg-ai
+- exaone
+- exaone-deep
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# <span style="color: #7FFF7F;">EXAONE-Deep-32B 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 [`73e53dc8`](https://github.com/ggerganov/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
+
+
+
+
+
+---
+
+## <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-->
+
+
+<p align="center">
+<img src="assets/EXAONE_Symbol+BI_3d.png", width="300", style="margin: 40 auto;">
+<br>
+
+# EXAONE-Deep-32B
+
+## Introduction
+
+We introduce EXAONE Deep, which exhibits superior capabilities in various reasoning tasks including math and coding benchmarks, ranging from 2.4B to 32B parameters developed and released by LG AI Research. Evaluation results show that 1) EXAONE Deep **2.4B** outperforms other models of comparable size, 2) EXAONE Deep **7.8B** outperforms not only open-weight models of comparable scale but also a proprietary reasoning model OpenAI o1-mini, and 3) EXAONE Deep **32B** demonstrates competitive performance against leading open-weight models.
+
+For more details, please refer to our [documentation](https://arxiv.org/abs/2503.12524), [blog](https://www.lgresearch.ai/news/view?seq=543) and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-Deep).
+
+<p align="center">
+<img src="assets/exaone_deep_overall_performance.png", width="100%", style="margin: 40 auto;">
+
+This repository contains the reasoning 32B language model with the following features:
+
+- Number of Parameters (without embeddings): 30.95B
+- Number of Layers: 64
+- Number of Attention Heads: GQA with 40 Q-heads and 8 KV-heads
+- Vocab Size: 102,400
+- Context Length: 32,768 tokens
+
+## Quickstart
+
+We recommend to use `transformers` v4.43.1 or later.
+
+Here is the code snippet to run conversational inference with the model:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
+
+model_name = "LGAI-EXAONE/EXAONE-Deep-32B"
+streaming = True    # choose the streaming option
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Choose your prompt:
+#   Math example (AIME 2024)
+prompt = r"""Let $x,y$ and $z$ be positive real numbers that satisfy the following system of equations:
+\[\log_2\left({x \over yz}\right) = {1 \over 2}\]\[\log_2\left({y \over xz}\right) = {1 \over 3}\]\[\log_2\left({z \over xy}\right) = {1 \over 4}\]
+Then the value of $\left|\log_2(x^4y^3z^2)\right|$ is $\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$.
+
+Please reason step by step, and put your final answer within \boxed{}."""
+#   Korean MCQA example (CSAT Math 2025)
+prompt = r"""Question : $a_1 = 2$인 수열 $\{a_n\}$과 $b_1 = 2$인 등차수열 $\{b_n\}$이 모든 자연수 $n$에 대하여\[\sum_{k=1}^{n} \frac{a_k}{b_{k+1}} = \frac{1}{2} n^2\]을 만족시킬 때, $\sum_{k=1}^{5} a_k$의 값을 구하여라.
+
+Options :
+A) 120
+B) 125
+C) 130
+D) 135
+E) 140
+ 
+Please reason step by step, and you should write the correct option alphabet (A, B, C, D or E) within \\boxed{}."""
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+)
+
+if streaming:
+    streamer = TextIteratorStreamer(tokenizer)
+    thread = Thread(target=model.generate, kwargs=dict(
+        input_ids=input_ids.to("cuda"),
+        eos_token_id=tokenizer.eos_token_id,
+        max_new_tokens=32768,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.95,
+        streamer=streamer
+    ))
+    thread.start()
+
+    for text in streamer:
+        print(text, end="", flush=True)
+else:
+    output = model.generate(
+        input_ids.to("cuda"),
+        eos_token_id=tokenizer.eos_token_id,
+        max_new_tokens=32768,
+        do_sample=True,
+        temperature=0.6,
+        top_p=0.95,
+    )
+    print(tokenizer.decode(output[0]))
+```
+
+> ### Note
+> The EXAONE Deep models are trained with an optimized configuration,
+> so we recommend following the [Usage Guideline](#usage-guideline) section to achieve optimal performance.
+
+## Evaluation
+
+The following table shows the evaluation results of reasoning tasks such as math and coding. The full evaluation results can be found in the [documentation](https://arxiv.org/abs/2503.12524).
+
+<table>
+    <tr>
+        <th>Models</th>
+        <th>MATH-500 (pass@1)</th>
+        <th>AIME 2024 (pass@1 / cons@64)</th>
+        <th>AIME 2025 (pass@1 / cons@64)</th>
+        <th>CSAT Math 2025 (pass@1)</th>
+        <th>GPQA Diamond (pass@1)</th>
+        <th>Live Code Bench (pass@1)</th>
+    </tr>
+    <tr>
+        <td>EXAONE Deep 32B</td>
+        <td>95.7</td>
+        <td>72.1 / <strong>90.0</strong></td>
+        <td>65.8 / <strong>80.0</strong></td>
+        <td><strong>94.5</strong></td>
+        <td>66.1</td>
+        <td>59.5</td>
+    </tr>
+    <tr>
+        <td>DeepSeek-R1-Distill-Qwen-32B</td>
+        <td>94.3</td>
+        <td>72.6 / 83.3</td>
+        <td>55.2 / 73.3</td>
+        <td>84.1</td>
+        <td>62.1</td>
+        <td>57.2</td>
+    </tr>
+    <tr>
+        <td>QwQ-32B</td>
+        <td>95.5</td>
+        <td>79.5 / 86.7</td>
+        <td><strong>67.1</strong> / 76.7</td>
+        <td>94.4</td>
+        <td>63.3</td>
+        <td>63.4</td>
+    </tr>
+    <tr>
+        <td>DeepSeek-R1-Distill-Llama-70B</td>
+        <td>94.5</td>
+        <td>70.0 / 86.7</td>
+        <td>53.9 / 66.7</td>
+        <td>88.8</td>
+        <td>65.2</td>
+        <td>57.5</td>
+    </tr>
+    <tr>
+        <td>DeepSeek-R1 (671B)</td>
+        <td><strong>97.3</strong></td>
+        <td><strong>79.8</strong> / 86.7</td>
+        <td>66.8 / <strong>80.0</strong></td>
+        <td>89.9</td>
+        <td><strong>71.5</strong></td>
+        <td><strong>65.9</strong></td>
+    </tr>
+    <tr>
+        <th colspan="7" height="30px"></th>
+    </tr>
+    <tr>
+        <td>EXAONE Deep 7.8B</td>
+        <td><strong>94.8</strong></td>
+        <td><strong>70.0</strong> / <strong>83.3</strong></td>
+        <td><strong>59.6</strong> / <strong>76.7</strong></td>
+        <td><strong>89.9</strong></td>
+        <td><strong>62.6</strong></td>
+        <td><strong>55.2</strong></td>
+    </tr>
+    <tr>
+        <td>DeepSeek-R1-Distill-Qwen-7B</td>
+        <td>92.8</td>
+        <td>55.5 / <strong>83.3</strong></td>
+        <td>38.5 / 56.7</td>
+        <td>79.7</td>
+        <td>49.1</td>
+        <td>37.6</td>
+    </tr>
+    <tr>
+        <td>DeepSeek-R1-Distill-Llama-8B</td>
+        <td>89.1</td>
+        <td>50.4 / 80.0</td>
+        <td>33.6 / 53.3</td>
+        <td>74.1</td>
+        <td>49.0</td>
+        <td>39.6</td>
+    </tr>
+    <tr>
+        <td>OpenAI o1-mini</td>
+        <td>90.0</td>
+        <td>63.6 / 80.0</td>
+        <td>54.8 / 66.7</td>
+        <td>84.4</td>
+        <td>60.0</td>
+        <td>53.8</td>
+    </tr>
+    <tr>
+        <th colspan="7" height="30px"></th>
+    </tr>
+    <tr>
+        <td>EXAONE Deep 2.4B</td>
+        <td><strong>92.3</strong></td>
+        <td><strong>52.5</strong> / <strong>76.7</strong></td>
+        <td><strong>47.9</strong> / <strong>73.3</strong></td>
+        <td><strong>79.2</strong></td>
+        <td><strong>54.3</strong></td>
+        <td><strong>46.6</strong></td>
+    </tr>
+    <tr>
+        <td>DeepSeek-R1-Distill-Qwen-1.5B</td>
+        <td>83.9</td>
+        <td>28.9 / 52.7</td>
+        <td>23.9 / 36.7</td>
+        <td>65.6</td>
+        <td>33.8</td>
+        <td>16.9</td>
+    </tr>
+</table>
+
+## Deployment
+
+EXAONE Deep models can be inferred in the various frameworks, such as:
+- `TensorRT-LLM`
+- `vLLM`
+- `SGLang`
+- `llama.cpp`
+- `Ollama`
+- `LM-Studio`
+
+Please refer to our [EXAONE Deep GitHub](https://github.com/LG-AI-EXAONE/EXAONE-Deep) for more details about the inference frameworks.
+
+## Quantization
+
+We provide the pre-quantized EXAONE Deep models with **AWQ** and several quantization types in **GGUF** format. Please refer to our [EXAONE Deep collection](https://huggingface.co/collections/LGAI-EXAONE/exaone-deep-67d119918816ec6efa79a4aa) to find corresponding quantized models.
+
+## Usage Guideline
+
+To achieve the expected performance, we recommend using the following configurations:
+
+1. Ensure the model starts with `<thought>\n` for reasoning steps. The model's output quality may be degraded when you omit it. You can easily apply this feature by using `tokenizer.apply_chat_template()` with `add_generation_prompt=True`. Please check the example code on [Quickstart](#quickstart) section.
+2. The reasoning steps of EXAONE Deep models enclosed by `<thought>\n...\n</thought>` usually have lots of tokens, so previous reasoning steps may be necessary to be removed in multi-turn situation. The provided tokenizer handles this automatically.
+3. Avoid using system prompt, and build the instruction on the user prompt. 
+4. Additional instructions help the models reason more deeply, so that the models generate better output.
+    - For math problems, the instructions **"Please reason step by step, and put your final answer within \boxed{}."** are helpful.
+    - For more information on our evaluation setting including prompts, please refer to our [Documentation](https://arxiv.org/abs/2503.12524).
+5. In our evaluation, we use `temperature=0.6` and `top_p=0.95` for generation. 
+6. When evaluating the models, it is recommended to test multiple times to assess the expected performance accurately.
+
+## Limitation
+
+The EXAONE language model has certain limitations and may occasionally generate inappropriate responses. The language model generates responses based on the output probability of tokens, and it is determined during learning from training data. While we have made every effort to exclude personal, harmful, and biased information from the training data, some problematic content may still be included, potentially leading to undesirable responses. Please note that the text generated by EXAONE language model does not reflects the views of LG AI Research.
+
+- Inappropriate answers may be generated, which contain personal, harmful or other inappropriate information.
+- Biased responses may be generated, which are associated with age, gender, race, and so on.
+- The generated responses rely heavily on statistics from the training data, which can result in the generation of
+semantically or syntactically incorrect sentences.
+- Since the model does not reflect the latest information, the responses may be false or contradictory.
+
+LG AI Research strives to reduce potential risks that may arise from EXAONE language models. Users are not allowed
+to engage in any malicious activities (e.g., keying in illegal information) that may induce the creation of inappropriate
+outputs violating LG AI’s ethical principles when using EXAONE language models.
+
+## License
+
+The model is licensed under [EXAONE AI Model License Agreement 1.1 - NC](./LICENSE)
+
+## Citation
+ 
+```
+@article{exaone-deep,
+  title={EXAONE Deep: Reasoning Enhanced Language Models},
+  author={{LG AI Research}},
+  journal={arXiv preprint arXiv:2503.12524},
+  year={2025}
+}
+```
+
+## Contact
+LG AI Research Technical Support: [email protected]
+
+<!--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! 😊

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