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--- |
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pipeline_tag: text-generation |
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inference: false |
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license: apache-2.0 |
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tags: |
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- code |
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- language |
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- granite-3.2 |
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base_model: |
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- ibm-granite/granite-3.2-8b-instruct |
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model_creator: ibm-granite |
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model_name: granite-3.2-8b-instruct |
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model_type: granite |
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datasets: |
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- m-a-p/CodeFeedback-Filtered-Instruction |
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quantized_by: CISC |
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--- |
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# granite-3.2-8b-instruct - SOTA GGUF |
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- Model creator: [IBM](https://huggingface.co/ibm-granite) |
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- Original model: [granite-3.2-8b-instruct](https://huggingface.co/ibm-granite/granite-3.2-8b-instruct) |
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<!-- description start --> |
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## Description |
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This repo contains State Of The Art quantized GGUF format model files for [granite-3.2-8b-instruct](https://huggingface.co/ibm-granite/granite-3.2-8b-instruct). |
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Quantization was done with an importance matrix that was trained for ~1M tokens (256 batches of 4096 tokens) of answers from the [CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) dataset. |
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Fill-in-Middle tokens are automatically detected and supported as of commit [0d2ec43](https://github.com/ggerganov/llama.cpp/commit/11ac9800aff532715a5bc7991062c68ba3472e6e), see [example](#simple-llama-cpp-python-example-fill-in-middle-code). |
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<!-- description end --> |
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<!-- compatibility_gguf start --> |
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## Compatibility |
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These quantised GGUFv3 files are compatible with llama.cpp from September 17th 2024 onwards, as of commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0d2ec438330271d201c2e9224aca23d0d5c908bf) |
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They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp. |
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Usage with llama-cpp-python based frameworks require [PR#1486](https://github.com/abetlen/llama-cpp-python/pull/1486) patched in for the chat template to work correctly. |
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## Explanation of quantisation methods |
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<details> |
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<summary>Click to see details</summary> |
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The new methods available are: |
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* GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw) |
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* GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw |
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* GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw |
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* GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw |
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* GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw |
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* GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw |
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* GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw |
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* GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw |
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* GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw |
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* GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw |
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* GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw |
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* GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw |
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Refer to the Provided Files table below to see what files use which methods, and how. |
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</details> |
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<!-- compatibility_gguf end --> |
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<!-- README_GGUF.md-provided-files start --> |
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## Provided files |
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| Name | Quant method | Bits | Size | Max RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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| [granite-3.2-8b-instruct.IQ1_S.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ1_S.gguf) | IQ1_S | 1 | 1.7 GB| 1.9 GB | smallest, significant quality loss | |
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| [granite-3.2-8b-instruct.IQ1_M.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ1_M.gguf) | IQ1_M | 1 | 1.8 GB| 2.1 GB | very small, significant quality loss | |
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| [granite-3.2-8b-instruct.IQ2_XXS.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ2_XXS.gguf) | IQ2_XXS | 2 | 2.1 GB| 2.3 GB | very small, high quality loss | |
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| [granite-3.2-8b-instruct.IQ2_XS.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ2_XS.gguf) | IQ2_XS | 2 | 2.3 GB| 2.5 GB | very small, high quality loss | |
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| [granite-3.2-8b-instruct.IQ2_S.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ2_S.gguf) | IQ2_S | 2 | 2.4 GB| 2.7 GB | small, substantial quality loss | |
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| [granite-3.2-8b-instruct.IQ2_M.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ2_M.gguf) | IQ2_M | 2 | 2.6 GB| 2.9 GB | small, greater quality loss | |
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| [granite-3.2-8b-instruct.IQ3_XXS.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ3_XXS.gguf) | IQ3_XXS | 3 | 3.0 GB| 3.2 GB | very small, high quality loss | |
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| [granite-3.2-8b-instruct.IQ3_XS.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ3_XS.gguf) | IQ3_XS | 3 | 3.2 GB| 3.4 GB | small, substantial quality loss | |
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| [granite-3.2-8b-instruct.IQ3_S.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ3_S.gguf) | IQ3_S | 3 | 3.4 GB| 3.6 GB | small, greater quality loss | |
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| [granite-3.2-8b-instruct.IQ3_M.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ3_M.gguf) | IQ3_M | 3 | 3.5 GB| 3.7 GB | medium, balanced quality - recommended | |
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| [granite-3.2-8b-instruct.IQ4_XS.gguf](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.IQ4_XS.gguf) | IQ4_XS | 4 | 4.1 GB| 4.3 GB | small, substantial quality loss | |
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Generated importance matrix file: [granite-3.2-8b-instruct.imatrix.dat](https://huggingface.co/CISCai/granite-3.2-8b-instruct-SOTA-GGUF/blob/main/granite-3.2-8b-instruct.imatrix.dat) |
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**Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. |
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<!-- README_GGUF.md-provided-files end --> |
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<!-- README_GGUF.md-how-to-run start --> |
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## Example `llama.cpp` command |
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Make sure you are using `llama.cpp` from commit [6171c9d](https://github.com/ggerganov/llama.cpp/commit/6171c9d25820ccf676b243c172868819d882848f) or later for jinja2 chat template support. |
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```shell |
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./llama-cli -ngl 41 -m granite-3.2-8b-instruct.IQ4_XS.gguf --color -c 131072 -cnv --jinja" |
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``` |
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Change `-ngl 41` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. |
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Change `-c 131072` to the desired sequence length. |
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If you are low on V/RAM try quantizing the K-cache with `-ctk q8_0` or even `-ctk q4_0` for big memory savings (depending on context size). |
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There is a similar option for V-cache (`-ctv`), only available if you enable Flash Attention (`-fa`) as well. |
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For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) |
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## How to run from Python code |
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You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) module. |
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### How to load this model in Python code, using llama-cpp-python |
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For full documentation, please see: [llama-cpp-python docs](https://llama-cpp-python.readthedocs.io/en/latest/). |
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#### First install the package |
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Run one of the following commands, according to your system: |
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```shell |
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# Prebuilt wheel with basic CPU support |
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pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu |
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# Prebuilt wheel with NVidia CUDA acceleration |
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pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.) |
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# Prebuilt wheel with Metal GPU acceleration |
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pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal |
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# Build base version with no GPU acceleration |
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pip install llama-cpp-python |
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# With NVidia CUDA acceleration |
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CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python |
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# Or with OpenBLAS acceleration |
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CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python |
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# Or with AMD ROCm GPU acceleration (Linux only) |
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CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python |
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# Or with Metal GPU acceleration for macOS systems only |
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CMAKE_ARGS="-DGGML_METAL=on" pip install llama-cpp-python |
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# Or with Vulkan acceleration |
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CMAKE_ARGS="-DGGML_VULKAN=on" pip install llama-cpp-python |
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# Or with SYCL acceleration |
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CMAKE_ARGS="-DGGML_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install llama-cpp-python |
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# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: |
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$env:CMAKE_ARGS = "-DGGML_CUDA=on" |
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pip install llama-cpp-python |
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``` |
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#### Simple llama-cpp-python example code |
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```python |
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from llama_cpp import Llama |
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# Chat Completion API |
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llm = Llama(model_path="./granite-3.2-8b-instruct.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072) |
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print(llm.create_chat_completion( |
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repeat_penalty = 1.0, |
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messages = [ |
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{ |
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"role": "user", |
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"content": "Pick a LeetCode challenge and solve it in Python." |
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} |
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] |
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)) |
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``` |
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#### Simple llama-cpp-python example fill-in-middle code |
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```python |
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from llama_cpp import Llama |
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# Completion API |
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prompt = "def add(" |
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suffix = "\n return sum\n\n" |
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llm = Llama(model_path="./granite-3.2-8b-instruct.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072) |
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output = llm.create_completion( |
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temperature = 0.0, |
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repeat_penalty = 1.0, |
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prompt = prompt, |
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suffix = suffix |
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) |
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# Models sometimes repeat suffix in response, attempt to filter that |
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response = output["choices"][0]["text"] |
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response_stripped = response.rstrip() |
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unwanted_response_suffix = suffix.rstrip() |
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unwanted_response_length = len(unwanted_response_suffix) |
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filtered = False |
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if unwanted_response_suffix and response_stripped[-unwanted_response_length:] == unwanted_response_suffix: |
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response = response_stripped[:-unwanted_response_length] |
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filtered = True |
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print(f"Fill-in-Middle completion{' (filtered)' if filtered else ''}:\n\n{prompt}\033[32m{response}\033[{'33' if filtered else '0'}m{suffix}\033[0m") |
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``` |
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#### Simple llama-cpp-python example function calling code |
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```python |
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from llama_cpp import Llama |
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# Chat Completion API |
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grammar = LlamaGrammar.from_json_schema(json.dumps({ |
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"type": "array", |
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"items": { |
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"type": "object", |
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"required": [ "name", "arguments" ], |
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"properties": { |
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"name": { |
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"type": "string" |
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}, |
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"arguments": { |
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"type": "object" |
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} |
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} |
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} |
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})) |
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llm = Llama(model_path="./granite-3.2-8b-instruct.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072) |
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response = llm.create_chat_completion( |
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temperature = 0.0, |
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repeat_penalty = 1.0, |
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messages = [ |
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{ |
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"role": "user", |
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"content": "What's the weather like in Oslo and Stockholm?" |
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} |
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], |
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tools=[{ |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, CA" |
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}, |
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"unit": { |
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"type": "string", |
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"enum": [ "celsius", "fahrenheit" ] |
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} |
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}, |
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"required": [ "location" ] |
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} |
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} |
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}], |
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grammar = grammar |
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) |
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print(json.loads(response["choices"][0]["text"])) |
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print(llm.create_chat_completion( |
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temperature = 0.0, |
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repeat_penalty = 1.0, |
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messages = [ |
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{ |
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"role": "user", |
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"content": "What's the weather like in Oslo?" |
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}, |
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{ # The tool_calls is from the response to the above with tool_choice active |
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"role": "assistant", |
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"content": None, |
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"tool_calls": [ |
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{ |
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"id": "call__0_get_current_weather_cmpl-...", |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"arguments": { "location": "Oslo, Norway" , "unit": "celsius" } |
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} |
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} |
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] |
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}, |
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{ # The tool_call_id is from tool_calls and content is the result from the function call you made |
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"role": "tool", |
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"content": "20", |
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"tool_call_id": "call__0_get_current_weather_cmpl-..." |
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} |
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], |
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tools=[{ |
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"type": "function", |
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"function": { |
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"name": "get_current_weather", |
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"description": "Get the current weather in a given location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, CA" |
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}, |
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"unit": { |
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"type": "string", |
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"enum": [ "celsius", "fahrenheit" ] |
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} |
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}, |
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"required": [ "location" ] |
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} |
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} |
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}], |
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#tool_choice={ |
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# "type": "function", |
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# "function": { |
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# "name": "get_current_weather" |
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# } |
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#} |
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)) |
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``` |
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<!-- README_GGUF.md-how-to-run end --> |
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<!-- original-model-card start --> |
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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, |
|
|
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}, |
|
|
} |
|
|
``` --> |
|
|
|
