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ibm-granite
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Model Details
Model Provider
ibm-granite
Model Tree
ibm-granite/granite-3.1-8b-instruct
Input Modalities
Output Modalities
Supported Functionality
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, 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.
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
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 torchaudiopip install acceleratepip install transformersThen, copy the snippet from the section that is relevant for your use case.
python
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seedimport 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
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.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 + 0.5x0.7x - 0.5x = 5 - 30.2x = 2x = 2 / 0.2x = 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:
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.
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 remain relevant.
Resources
pip install torch torchvision torchaudiopip install acceleratepip install transformersfrom transformers import AutoModelForCausalLM, AutoTokenizer, set_seedimport 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)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.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 + 0.5x0.7x - 0.5x = 5 - 30.2x = 2x = 2 / 0.2x = 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.