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README.md
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license: apache-2.0
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---
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license: apache-2.0
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pipeline_tag: text-generation
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language:
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- en
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license_link: LICENSE
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base_model:
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- ibm-granite/granite-3.1-8b-instruct
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quantized_by: bartowski
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tags:
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- llamafile
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- language
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- granite-3.2
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---
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# Granite 3.2 8B Instruct - llamafile
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- Model creator: [IBM](https://huggingface.co/ibm-granite)
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- Original model: [ibm-granite/granite-3.2-8b-instruct](https://huggingface.co/ibm-granite/granite-3.2-8b-instruct)
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Mozilla packaged the IBM Granite 3.2 models into executable weights that we
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call [llamafiles](https://github.com/Mozilla-Ocho/llamafile). This gives
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you the easiest fastest way to use the model on Linux, MacOS, Windows,
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FreeBSD, OpenBSD and NetBSD systems you control on both AMD64 and ARM64.
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*Software Last Updated: 2025-03-31*
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*Llamafile Version: 0.9.2*
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## Quickstart
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To get started, you need both the Granite 3.2 weights, and the llamafile
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software. Both of them are included in a single file, which can be
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downloaded and run as follows:
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```
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wget https://huggingface.co/Mozilla/granite-3.2-8b-instruct-llamafile/resolve/main/granite-3.2-8b-instruct-Q6_K.llamafile
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chmod +x granite-3.2-8b-instruct-Q6_K.llamafile
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./granite-3.2-8b-instruct-Q6_K.llamafile
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```
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The default mode of operation for these llamafiles is our new command
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line chatbot interface.
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## Usage
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You can use triple quotes to ask questions on multiple lines. You can
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pass commands like `/stats` and `/context` to see runtime status
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information. You can change the system prompt by passing the `-p "new
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system prompt"` flag. You can press CTRL-C to interrupt the model.
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Finally CTRL-D may be used to exit.
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If you prefer to use a web GUI, then a `--server` mode is provided, that
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will open a tab with a chatbot and completion interface in your browser.
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For additional help on how it may be used, pass the `--help` flag. The
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server also has an OpenAI API compatible completions endpoint that can
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be accessed via Python using the `openai` pip package.
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```
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./granite-3.2-8b-instruct-Q6_K.llamafile --server
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```
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An advanced CLI mode is provided that's useful for shell scripting. You
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can use it by passing the `--cli` flag. For additional help on how it
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may be used, pass the `--help` flag.
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```
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./granite-3.2-8b-instruct-Q6_K.llamafile --cli -p 'four score and seven' --log-disable
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```
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## Troubleshooting
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Having **trouble?** See the ["Gotchas"
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section](https://github.com/mozilla-ocho/llamafile/?tab=readme-ov-file#gotchas-and-troubleshooting)
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of the README.
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On Linux, the way to avoid run-detector errors is to install the APE
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interpreter.
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```sh
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sudo wget -O /usr/bin/ape https://cosmo.zip/pub/cosmos/bin/ape-$(uname -m).elf
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sudo chmod +x /usr/bin/ape
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sudo sh -c "echo ':APE:M::MZqFpD::/usr/bin/ape:' >/proc/sys/fs/binfmt_misc/register"
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sudo sh -c "echo ':APE-jart:M::jartsr::/usr/bin/ape:' >/proc/sys/fs/binfmt_misc/register"
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```
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On Windows there's a 4GB limit on executable sizes.
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## Context Window
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This model has a max context window size of 128k tokens. By default, a
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context window size of 8192 tokens is used. You can ask llamafile
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to use the maximum context size by passing the `-c 0` flag. That's big
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enough for a small book. If you want to be able to have a conversation
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with your book, you can use the `-f book.txt` flag.
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## GPU Acceleration
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On GPUs with sufficient RAM, the `-ngl 999` flag may be passed to use
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the system's NVIDIA or AMD GPU(s). On Windows, only the graphics card
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driver needs to be installed if you own an NVIDIA GPU. On Windows, if
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you have an AMD GPU, you should install the ROCm SDK v6.1 and then pass
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the flags `--recompile --gpu amd` the first time you run your llamafile.
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On NVIDIA GPUs, by default, the prebuilt tinyBLAS library is used to
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perform matrix multiplications. This is open source software, but it
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doesn't go as fast as closed source cuBLAS. If you have the CUDA SDK
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installed on your system, then you can pass the `--recompile` flag to
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build a GGML CUDA library just for your system that uses cuBLAS. This
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ensures you get maximum performance.
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For further information, please see the [llamafile
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README](https://github.com/mozilla-ocho/llamafile/).
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## About llamafile
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llamafile is a new format introduced by Mozilla on Nov 20th 2023. It
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uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp
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binaries that run on the stock installs of six OSes for both ARM64 and
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AMD64.
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---
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# Granite-3.2-8B-Instruct
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**Model Summary:**
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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.
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- **Developers:** Granite Team, IBM
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- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
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- **Release Date**: February 26th, 2025
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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**Supported Languages:**
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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.
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**Intended Use:**
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This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.
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**Capabilities**
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* **Thinking**
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* Summarization
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* Text classification
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* Text extraction
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* Question-answering
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* Retrieval Augmented Generation (RAG)
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* Code related tasks
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* Function-calling tasks
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* Multilingual dialog use cases
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* Long-context tasks including long document/meeting summarization, long document QA, etc.
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**Generation:**
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This is a simple example of how to use Granite-3.2-8B-Instruct model.
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Install the following libraries:
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```shell
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pip install torch torchvision torchaudio
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pip install accelerate
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pip install transformers
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```
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Then, copy the snippet from the section that is relevant for your use case.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
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import torch
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model_path="ibm-granite/granite-3.2-8b-instruct"
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device="cuda"
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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device_map=device,
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torch_dtype=torch.bfloat16,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_path
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)
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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?"}]
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input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)
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set_seed(42)
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output = model.generate(
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**input_ids,
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max_new_tokens=8192,
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)
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prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
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print(prediction)
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```
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**Example Outputs**
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- thinking=True
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```md
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Here is my thought process:
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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).
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2. We need to determine how much of the 70% acid solution to add such that the final mixture is 50% acid.
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3. Let's say x liters of the 70% acid solution are added. This will contribute 0.7x liters of pure acid.
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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.
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5. We need to set up an equation where (3 + 0.7x) / (10 + x) = 0.5 (since we want a 50% acid mixture).
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6. Solving this equation for x will give us the volume of 70% solution needed.
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Here is my response:
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To solve this, we need to set up an equation based on the concentration of acid before and after mixing.
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1. **Initial Acid Content**:
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- We start with 10 liters of a 30% acid solution.
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- Pure acid in this = 10 * 0.3 = 3 liters.
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2. **Variable for Addition**:
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- Let x be the liters of 70% acid solution to be added.
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- Pure acid in x liters of 70% solution = 0.7x liters.
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3. **Final Mixture Concentration**:
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- Total volume of mixture = 10 + x liters.
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- Total pure acid in the mixture = 3 (from initial solution) + 0.7x (from added solution).
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4. **Setting up the equation for 50% acid concentration**:
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- We require that the ratio of pure acid to total volume is 0.5 (50%).
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- Therefore, the equation is:
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\[
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\frac{3 + 0.7x}{10 + x} = 0.5
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\]
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5. **Solving the equation**:
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Multiply both sides by (10 + x) to clear the fraction:
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\[
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3 + 0.7x = 0.5(10 + x)
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\]
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+
Expanding the right side:
|
241 |
+
|
242 |
+
\[
|
243 |
+
3 + 0.7x = 5 + 0.5x
|
244 |
+
\]
|
245 |
+
|
246 |
+
Subtract 0.5x from both sides:
|
247 |
+
|
248 |
+
\[
|
249 |
+
3 + 0.2x = 5
|
250 |
+
\]
|
251 |
+
|
252 |
+
Subtract 3 from both sides:
|
253 |
+
|
254 |
+
\[
|
255 |
+
0.2x = 2
|
256 |
+
\]
|
257 |
+
|
258 |
+
Divide by 0.2:
|
259 |
+
|
260 |
+
\[
|
261 |
+
x = 10
|
262 |
+
\]
|
263 |
+
|
264 |
+
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.
|
265 |
+
|
266 |
+
Let's verify:
|
267 |
+
|
268 |
+
- Initial acid: 3 liters
|
269 |
+
- Acid from added solution: 0.7 * 10 = 7 liters
|
270 |
+
- Total acid: 3 + 7 = 10 liters
|
271 |
+
- Total volume: 10 + 10 = 20 liters
|
272 |
+
- Acid percentage: \(\frac{10}{20} = 0.5 = 50\%\)
|
273 |
+
|
274 |
+
This confirms our solution.
|
275 |
+
|
276 |
+
Final answer: 10 liters of 70% acid solution should be added.
|
277 |
+
```
|
278 |
+
- thinking=False
|
279 |
+
```md
|
280 |
+
Let's denote the amount of 70% acid solution to be added as x liters.
|
281 |
+
|
282 |
+
We start with 10 liters of a 30% acid solution, which means there are 0.3 * 10 = 3 liters of pure acid in it.
|
283 |
+
|
284 |
+
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.
|
285 |
+
|
286 |
+
So, the amount of pure acid in the final mixture should be 0.5 * (10 + x) liters.
|
287 |
+
|
288 |
+
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:
|
289 |
+
|
290 |
+
3 (from the original solution) + 0.7x (from the added solution) = 0.5 * (10 + x)
|
291 |
+
|
292 |
+
Now, let's solve for x:
|
293 |
+
|
294 |
+
3 + 0.7x = 5 + 0.5x
|
295 |
+
0.7x - 0.5x = 5 - 3
|
296 |
+
0.2x = 2
|
297 |
+
x = 2 / 0.2
|
298 |
+
x = 10
|
299 |
+
|
300 |
+
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.
|
301 |
+
```
|
302 |
+
|
303 |
+
**Evaluation Results:**
|
304 |
+
<table>
|
305 |
+
|
306 |
+
<thead>
|
307 |
+
<tr>
|
308 |
+
<th style="text-align:left; background-color: #001d6c; color: white;">Models</th>
|
309 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">ArenaHard</th>
|
310 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">Alpaca-Eval-2</th>
|
311 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">MMLU</th>
|
312 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">PopQA</th>
|
313 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">TruthfulQA</th>
|
314 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">BigBenchHard</th>
|
315 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">DROP</th>
|
316 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">GSM8K</th>
|
317 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">HumanEval</th>
|
318 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">HumanEval+</th>
|
319 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">IFEval</th>
|
320 |
+
<th style="text-align:center; background-color: #001d6c; color: white;">AttaQ</th>
|
321 |
+
</tr></thead>
|
322 |
+
<tbody>
|
323 |
+
<tr>
|
324 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;">Llama-3.1-8B-Instruct</td>
|
325 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">36.43</td>
|
326 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">27.22</td>
|
327 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">69.15</td>
|
328 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">28.79</td>
|
329 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">52.79</td>
|
330 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">72.66</td>
|
331 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">61.48</td>
|
332 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">83.24</td>
|
333 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">85.32</td>
|
334 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">80.15</td>
|
335 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">79.10</td>
|
336 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">83.43</td>
|
337 |
+
</tr>
|
338 |
+
|
339 |
+
<tr>
|
340 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;">DeepSeek-R1-Distill-Llama-8B</td>
|
341 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">17.17</td>
|
342 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">21.85</td>
|
343 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">45.80</td>
|
344 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">13.25</td>
|
345 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">47.43</td>
|
346 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">65.71</td>
|
347 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">44.46</td>
|
348 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">72.18</td>
|
349 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">67.54</td>
|
350 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">62.91</td>
|
351 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">66.50</td>
|
352 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">42.87</td>
|
353 |
+
</tr>
|
354 |
+
|
355 |
+
<tr>
|
356 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;">Qwen-2.5-7B-Instruct</td>
|
357 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">25.44</td>
|
358 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">30.34</td>
|
359 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">74.30</td>
|
360 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">18.12</td>
|
361 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">63.06</td>
|
362 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">70.40</td>
|
363 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">54.71</td>
|
364 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">84.46</td>
|
365 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">93.35</td>
|
366 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">89.91</td>
|
367 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">74.90</td>
|
368 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">81.90</td>
|
369 |
+
</tr>
|
370 |
+
|
371 |
+
<tr>
|
372 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;">DeepSeek-R1-Distill-Qwen-7B</td>
|
373 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">10.36</td>
|
374 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">15.35</td>
|
375 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">50.72</td>
|
376 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">9.94</td>
|
377 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">47.14</td>
|
378 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">65.04</td>
|
379 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">42.76</td>
|
380 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">78.47</td>
|
381 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">79.89</td>
|
382 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">78.43</td>
|
383 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">59.10</td>
|
384 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">42.45</td>
|
385 |
+
</tr>
|
386 |
+
|
387 |
+
<tr>
|
388 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.1-8B-Instruct</td>
|
389 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">37.58</td>
|
390 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">30.34</td>
|
391 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">66.77</td>
|
392 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">28.7</td>
|
393 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">65.84</td>
|
394 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">68.55</td>
|
395 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">50.78</td>
|
396 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">79.15</td>
|
397 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">89.63</td>
|
398 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">85.79</td>
|
399 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">73.20</td>
|
400 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">85.73</td>
|
401 |
+
</tr>
|
402 |
+
|
403 |
+
|
404 |
+
<tr>
|
405 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.1-2B-Instruct</td>
|
406 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">23.3</td>
|
407 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">27.17</td>
|
408 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">57.11</td>
|
409 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">20.55</td>
|
410 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">59.79</td>
|
411 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">54.46</td>
|
412 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">18.68</td>
|
413 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">67.55</td>
|
414 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">79.45</td>
|
415 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">75.26</td>
|
416 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">63.59</td>
|
417 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">84.7</td>
|
418 |
+
</tr>
|
419 |
+
|
420 |
+
|
421 |
+
<tr>
|
422 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.2-2B-Instruct</td>
|
423 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">24.86</td>
|
424 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">34.51</td>
|
425 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">57.18</td>
|
426 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">20.56</td>
|
427 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">59.8</td>
|
428 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">52.27</td>
|
429 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">21.12</td>
|
430 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">67.02</td>
|
431 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">80.13</td>
|
432 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">73.39</td>
|
433 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">61.55</td>
|
434 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">83.23</td>
|
435 |
+
</tr>
|
436 |
+
|
437 |
+
<tr>
|
438 |
+
<td style="text-align:left; background-color: #DAE8FF; color: black;"><b>Granite-3.2-8B-Instruct</b></td>
|
439 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">55.25</td>
|
440 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">61.19</td>
|
441 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">66.79</td>
|
442 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">28.04</td>
|
443 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">66.92</td>
|
444 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">64.77</td>
|
445 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">50.95</td>
|
446 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">81.65</td>
|
447 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">89.35</td>
|
448 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">85.72</td>
|
449 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">74.31</td>
|
450 |
+
<td style="text-align:center; background-color: #DAE8FF; color: black;">85.42</td>
|
451 |
+
|
452 |
+
</tr>
|
453 |
+
|
454 |
+
|
455 |
+
|
456 |
+
</tbody></table>
|
457 |
+
|
458 |
+
**Training Data:**
|
459 |
+
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.
|
460 |
+
<!-- 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). -->
|
461 |
+
|
462 |
+
**Infrastructure:**
|
463 |
+
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.
|
464 |
+
|
465 |
+
**Ethical Considerations and Limitations:**
|
466 |
+
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.
|
467 |
+
|
468 |
+
|
469 |
+
**Resources**
|
470 |
+
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
|
471 |
+
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
|
472 |
+
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
|
473 |
+
|
474 |
+
<!-- ## Citation
|
475 |
+
```
|
476 |
+
@misc{granite-models,
|
477 |
+
author = {author 1, author2, ...},
|
478 |
+
title = {},
|
479 |
+
journal = {},
|
480 |
+
volume = {},
|
481 |
+
year = {2024},
|
482 |
+
url = {https://arxiv.org/abs/0000.00000},
|
483 |
+
}
|
484 |
+
``` -->
|