Triangle104/granite-3.2-2b-instruct-Q6_K-GGUF

This model was converted to GGUF format from ibm-granite/granite-3.2-2b-instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model Summary:

Granite-3.2-2B-Instruct is an 2-billion-parameter, long-context AI model fine-tuned for thinking capabilities. Built on top of Granite-3.1-2B-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 Release Date: February 26th, 2025 License: Apache 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-3.2-2B-Instruct model.

Install the following libraries:

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.

from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed import torch

model_path="ibm-granite/granite-3.2-2b-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

Here is my thought process:

1. First, let's understand the composition of the initial 10 liters of 30% acid solution:

   • Acid content = 30% of 10 liters = 0.30 * 10 = 3 liters

2. We want to end up with a 50% acid solution. Let's denote the volume of the 70% acid solution to be added as 'x' liters.

3. The amount of acid in the final mixture (50% solution) should equal the initial acid content plus the acid from the added 70% solution.

   • Total acid in final mixture = 0.50 * (10 + x)

4. The equation representing the total acid content is: 3 (from initial solution) + 0.70 * x (from added solution) = 0.50 * (10 + x)

5. Now, we solve for 'x': 3 + 0.70x = 5 + 0.50x 0.70x - 0.50x = 5 - 3 0.20x = 2 x = 2 / 0.20 x = 10 liters

6. Therefore, 10 liters of a 70% acid solution must be added to the initial 10 liters of 30% acid solution to achieve a 50% acid mixture.

Here is my response:

To solve this problem, we need to determine how much of a 70% acid solution must be added to a 10-liter solution that is already 30% acid to create a new mixture that is 50% acid. Here's how to calculate it:

1. Initial Acid Content:

   • The initial 10-liter solution contains 3 liters of acid (since 30% of 10 liters).

2. Desired Final Acid Content:

   • We want the final mixture to be 50% acid.

3. Equation Setup:

   • Let 'x' be the volume (in liters) of the 70% acid solution to be added.
   • The equation representing the total acid content in the final mixture (which is 10 liters from the initial solution plus 'x' liters of the added solution) should equal 50% of the total volume (10 + x).
   • This gives us: 3 (acid from initial solution) + 0.70x (acid from added solution) = 0.50(10 + x)

4. Solving the Equation:

   • First, simplify the right side of the equation: 0.50(10 + x) = 5 + 0.50x
   • Now, the equation is: 3 + 0.70x = 5 + 0.50x
   • Subtract 0.50x from both sides: 0.20x = 2
   • Divide both sides by 0.20: x = 2 / 0.20
   • x = 10 liters

5. Conclusion:

   • To achieve a 50% acid mixture from the initial 30% solution, you need to add 10 liters of a 70% acid solution.

So, the final answer is that 10 liters of a 70% acid solution must be added to the initial 10 liters of 30% acid solution.

thinking=False

To solve this problem, we'll use the concept of volume and concentration. Let's denote the amount of 70% acid solution we need to add as "x" liters.

First, let's find out how much acid is in the initial 10-liter solution:

Acid in initial solution = 30% of 10 liters = 0.30 * 10 = 3 liters

Now, we want to end up with a 50% acid solution in a total volume of (10 + x) liters. Let's denote the final volume as V.

Final acid concentration = 50% Final acid amount = 50% of V = 0.50 * V

We know the initial acid amount and the final acid amount, so we can set up an equation:

Initial acid amount + Acid from added solution = Final acid amount 3 liters + (70% of x) = 0.50 * (10 + x)

Now, let's solve for x:

0.70x + 3 = 0.50 * 10 + 0.50x 0.70x - 0.50x = 0.50 * 10 - 3 0.20x = 5 - 3 0.20x = 2 x = 2 / 0.20 x = 10 liters

So, you need to add 10 liters of a 70% acid solution to the initial 10-liter 30% acid solution to achieve a 50% acid mixture.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/granite-3.2-2b-instruct-Q6_K-GGUF --hf-file granite-3.2-2b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/granite-3.2-2b-instruct-Q6_K-GGUF --hf-file granite-3.2-2b-instruct-q6_k.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/granite-3.2-2b-instruct-Q6_K-GGUF --hf-file granite-3.2-2b-instruct-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/granite-3.2-2b-instruct-Q6_K-GGUF --hf-file granite-3.2-2b-instruct-q6_k.gguf -c 2048