--- pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers tags: - language - granite-3.2 - ganite base_model: - ibm-granite/granite-3.2-8b-instruct --- # Granite-3.2-8B-Instruct **Model Summary:** Granite-3.2-8B-Instruct is an 8-billion-parameter, long-context AI model fine-tuned for thinking capabilities. Built on top of [Granite-3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct), it has been trained using a mix of permissively licensed open-source datasets and internally generated synthetic data designed for reasoning tasks. The model allows controllability of its thinking capability, ensuring it is applied only when required. - **Developers:** Granite Team, IBM - **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) - **Release Date**: February 26th, 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. However, users may finetune this Granite model for languages beyond these 12 languages. **Intended Use:** This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications. **Capabilities** * **Thinking** * Summarization * Text classification * Text extraction * Question-answering * Retrieval Augmented Generation (RAG) * Code related tasks * Function-calling tasks * Multilingual dialog use cases * Long-context tasks including long document/meeting summarization, long document QA, etc. **Generation:** This is a simple example of how to use Granite-3.2-8B-Instruct 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 from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed import torch model_path="ibm-granite/granite-3.2-8b-instruct" device="cuda" model = AutoModelForCausalLM.from_pretrained( model_path, device_map=device, torch_dtype=torch.bfloat16, ) tokenizer = AutoTokenizer.from_pretrained( model_path ) conv = [{"role": "user", "content":"You have 10 liters of a 30% acid solution. How many liters of a 70% acid solution must be added to achieve a 50% acid mixture?"}] input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device) set_seed(42) output = model.generate( **input_ids, max_new_tokens=8192, ) prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True) print(prediction) ``` **Example Outputs** - thinking=True ```md Here is my thought process: 1. We know we start with 10 liters of a 30% acid solution, so there's 3 liters of pure acid in it (10 liters * 0.3). 2. We need to determine how much of the 70% acid solution to add such that the final mixture is 50% acid. 3. Let's say x liters of the 70% acid solution are added. This will contribute 0.7x liters of pure acid. 4. The total volume of the new mixture will be 10 + x liters, and the total amount of pure acid will be 3 + 0.7x liters. 5. We need to set up an equation where (3 + 0.7x) / (10 + x) = 0.5 (since we want a 50% acid mixture). 6. Solving this equation for x will give us the volume of 70% solution needed. Here is my response: To solve this, we need to set up an equation based on the concentration of acid before and after mixing. 1. **Initial Acid Content**: - We start with 10 liters of a 30% acid solution. - Pure acid in this = 10 * 0.3 = 3 liters. 2. **Variable for Addition**: - Let x be the liters of 70% acid solution to be added. - Pure acid in x liters of 70% solution = 0.7x liters. 3. **Final Mixture Concentration**: - Total volume of mixture = 10 + x liters. - Total pure acid in the mixture = 3 (from initial solution) + 0.7x (from added solution). 4. **Setting up the equation for 50% acid concentration**: - We require that the ratio of pure acid to total volume is 0.5 (50%). - Therefore, the equation is: \[ \frac{3 + 0.7x}{10 + x} = 0.5 \] 5. **Solving the equation**: Multiply both sides by (10 + x) to clear the fraction: \[ 3 + 0.7x = 0.5(10 + x) \] Expanding the right side: \[ 3 + 0.7x = 5 + 0.5x \] Subtract 0.5x from both sides: \[ 3 + 0.2x = 5 \] Subtract 3 from both sides: \[ 0.2x = 2 \] Divide by 0.2: \[ x = 10 \] So, 10 liters of the 70% acid solution must be added to the initial 10 liters of 30% solution to get a 50% acid mixture. Let's verify: - Initial acid: 3 liters - Acid from added solution: 0.7 * 10 = 7 liters - Total acid: 3 + 7 = 10 liters - Total volume: 10 + 10 = 20 liters - Acid percentage: \(\frac{10}{20} = 0.5 = 50\%\) This confirms our solution. Final answer: 10 liters of 70% acid solution should be added. ``` - thinking=False ```md Let's denote the amount of 70% acid solution to be added as x liters. We start with 10 liters of a 30% acid solution, which means there are 0.3 * 10 = 3 liters of pure acid in it. We want to end up with a 50% acid solution after adding x liters of 70% acid solution. The total volume of the mixture will be 10 + x liters, and we want half of this volume to be pure acid. So, the amount of pure acid in the final mixture should be 0.5 * (10 + x) liters. The amount of pure acid in the final mixture comes from two sources: the original 30% solution and the added 70% solution. So, we can set up the following equation: 3 (from the original solution) + 0.7x (from the added solution) = 0.5 * (10 + x) Now, let's solve for x: 3 + 0.7x = 5 + 0.5x 0.7x - 0.5x = 5 - 3 0.2x = 2 x = 2 / 0.2 x = 10 So, you need to add 10 liters of a 70% acid solution to the 10 liters of a 30% acid solution to get a 50% acid mixture. ``` **Evaluation Results:** <table> <thead> <tr> <th style="text-align:left; background-color: #001d6c; color: white;">Models</th> <th style="text-align:center; background-color: #001d6c; color: white;">ArenaHard</th> <th style="text-align:center; background-color: #001d6c; color: white;">Alpaca-Eval-2</th> <th style="text-align:center; background-color: #001d6c; color: white;">MMLU</th> <th style="text-align:center; background-color: #001d6c; color: white;">PopQA</th> <th style="text-align:center; background-color: #001d6c; color: white;">TruthfulQA</th> <th style="text-align:center; background-color: #001d6c; color: white;">BigBenchHard</th> <th style="text-align:center; background-color: #001d6c; color: white;">DROP</th> <th style="text-align:center; background-color: #001d6c; color: white;">GSM8K</th> <th style="text-align:center; background-color: #001d6c; color: white;">HumanEval</th> <th style="text-align:center; background-color: #001d6c; color: white;">HumanEval+</th> <th style="text-align:center; background-color: #001d6c; color: white;">IFEval</th> <th style="text-align:center; background-color: #001d6c; color: white;">AttaQ</th> </tr></thead> <tbody> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;">Llama-3.1-8B-Instruct</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">36.43</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">27.22</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">69.15</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">28.79</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">52.79</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">72.66</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">61.48</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">83.24</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">85.32</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">80.15</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">79.10</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">83.43</td> </tr> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;">DeepSeek-R1-Distill-Llama-8B</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">17.17</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">21.85</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">45.80</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">13.25</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">47.43</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">65.71</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">44.46</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">72.18</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">67.54</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">62.91</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">66.50</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">42.87</td> </tr> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;">Qwen-2.5-7B-Instruct</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">25.44</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">30.34</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">74.30</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">18.12</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">63.06</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">70.40</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">54.71</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">84.46</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">93.35</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">89.91</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">74.90</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">81.90</td> </tr> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;">DeepSeek-R1-Distill-Qwen-7B</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">10.36</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">15.35</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">50.72</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">9.94</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">47.14</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">65.04</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">42.76</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">78.47</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">79.89</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">78.43</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">59.10</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">42.45</td> </tr> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.1-8B-Instruct</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">37.58</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">30.34</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">66.77</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">28.7</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">65.84</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">68.55</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">50.78</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">79.15</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">89.63</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">85.79</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">73.20</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">85.73</td> </tr> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.1-2B-Instruct</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">23.3</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">27.17</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">57.11</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">20.55</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">59.79</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">54.46</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">18.68</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">67.55</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">79.45</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">75.26</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">63.59</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">84.7</td> </tr> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.2-2B-Instruct</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">24.86</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">34.51</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">57.18</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">20.56</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">59.8</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">52.27</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">21.12</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">67.02</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">80.13</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">73.39</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">61.55</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">83.23</td> </tr> <tr> <td style="text-align:left; background-color: #DAE8FF; color: black;"><b>Granite-3.2-8B-Instruct</b></td> <td style="text-align:center; background-color: #DAE8FF; color: black;">55.25</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">61.19</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">66.79</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">28.04</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">66.92</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">64.77</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">50.95</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">81.65</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">89.35</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">85.72</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">74.31</td> <td style="text-align:center; background-color: #DAE8FF; color: black;">85.42</td> </tr> </tbody></table> **Training Data:** Overall, our training data is largely comprised of two key sources: (1) publicly available datasets with permissive license, (2) internal synthetically generated data targeted to enhance reasoning capabilites. <!-- A detailed attribution of datasets can be found in [Granite 3.2 Technical Report (coming soon)](#), and [Accompanying Author List](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/author-ack.pdf). --> **Infrastructure:** We train Granite-3.2-8B-Instruct using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs. **Ethical Considerations and Limitations:** Granite-3.2-8B-Instruct builds upon Granite-3.1-8B-Instruct, leveraging both permissively licensed open-source and select proprietary data for enhanced performance. Since it inherits its foundation from the previous model, all ethical considerations and limitations applicable to [Granite-3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) remain relevant. **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}, } ``` -->