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@@ -35,3 +35,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 granite-3.2-8b-instruct-bf16-00001-of-00002.gguf filter=lfs diff=lfs merge=lfs -text
 granite-3.2-8b-instruct-bf16-00002-of-00002.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.imatrix.dat filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ1_M.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ1_S.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ2_S.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ2_XS.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ2_XXS.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ3_S.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ3_XS.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text
+granite-3.2-8b-instruct.IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,677 @@
----
-license: apache-2.0
----
+---
+pipeline_tag: text-generation
+inference: false
+license: apache-2.0
+tags:
+- code
+- language
+- granite-3.2
+base_model:
+- ibm-granite/granite-3.2-8b-instruct
+model_creator: ibm-granite
+model_name: granite-3.2-8b-instruct
+model_type: granite
+datasets:
+- m-a-p/CodeFeedback-Filtered-Instruction
+quantized_by: CISC
+---
+
+# granite-3.2-8b-instruct - SOTA GGUF
+- Model creator: [IBM](https://huggingface.co/ibm-granite)
+- Original model: [granite-3.2-8b-instruct](https://huggingface.co/ibm-granite/granite-3.2-8b-instruct)
+
+<!-- description start -->
+## Description
+
+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).
+
+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.
+
+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).
+
+<!-- description end -->
+
+<!-- compatibility_gguf start -->
+## Compatibility
+
+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)
+
+They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.
+
+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.
+
+## Explanation of quantisation methods
+
+<details>
+  <summary>Click to see details</summary>
+
+The new methods available are:
+
+* GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw)
+* GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw
+* GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw
+* GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw
+* GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw
+* GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw
+* GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw
+* GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw
+* GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw
+* GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw
+* GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw
+* GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw
+
+Refer to the Provided Files table below to see what files use which methods, and how.
+</details>
+<!-- compatibility_gguf end -->
+
+<!-- README_GGUF.md-provided-files start -->
+## Provided files
+
+| Name | Quant method | Bits | Size | Max RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+| [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 |
+
+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)
+
+**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.
+
+<!-- README_GGUF.md-provided-files end -->
+
+<!-- README_GGUF.md-how-to-run start -->
+## Example `llama.cpp` command
+
+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.
+
+```shell
+./llama-cli -ngl 41 -m granite-3.2-8b-instruct.IQ4_XS.gguf --color -c 131072 -cnv --jinja"
+```
+
+Change `-ngl 41` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
+
+Change `-c 131072` to the desired sequence length.
+
+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).
+There is a similar option for V-cache (`-ctv`), only available if you enable Flash Attention (`-fa`) as well.
+
+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)
+
+## How to run from Python code
+
+You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) module.
+
+### How to load this model in Python code, using llama-cpp-python
+
+For full documentation, please see: [llama-cpp-python docs](https://llama-cpp-python.readthedocs.io/en/latest/).
+
+#### First install the package
+
+Run one of the following commands, according to your system:
+
+```shell
+# Prebuilt wheel with basic CPU support
+pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
+# Prebuilt wheel with NVidia CUDA acceleration
+pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.)
+# Prebuilt wheel with Metal GPU acceleration
+pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
+# Build base version with no GPU acceleration
+pip install llama-cpp-python
+# With NVidia CUDA acceleration
+CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python
+# Or with OpenBLAS acceleration
+CMAKE_ARGS="-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
+# Or with AMD ROCm GPU acceleration (Linux only)
+CMAKE_ARGS="-DGGML_HIPBLAS=on" pip install llama-cpp-python
+# Or with Metal GPU acceleration for macOS systems only
+CMAKE_ARGS="-DGGML_METAL=on" pip install llama-cpp-python
+# Or with Vulkan acceleration
+CMAKE_ARGS="-DGGML_VULKAN=on" pip install llama-cpp-python
+# Or with SYCL acceleration
+CMAKE_ARGS="-DGGML_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install llama-cpp-python
+
+# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
+$env:CMAKE_ARGS = "-DGGML_CUDA=on"
+pip install llama-cpp-python
+```
+
+#### Simple llama-cpp-python example code
+
+```python
+from llama_cpp import Llama
+
+# Chat Completion API
+
+llm = Llama(model_path="./granite-3.2-8b-instruct.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072)
+print(llm.create_chat_completion(
+    repeat_penalty = 1.0,
+    messages = [
+        {
+            "role": "user",
+            "content": "Pick a LeetCode challenge and solve it in Python."
+        }
+    ]
+))
+```
+
+#### Simple llama-cpp-python example fill-in-middle code
+
+```python
+from llama_cpp import Llama
+
+# Completion API
+
+prompt = "def add("
+suffix = "\n    return sum\n\n"
+
+llm = Llama(model_path="./granite-3.2-8b-instruct.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072)
+output = llm.create_completion(
+    temperature = 0.0,
+    repeat_penalty = 1.0,
+    prompt = prompt,
+    suffix = suffix
+)
+
+# Models sometimes repeat suffix in response, attempt to filter that
+response = output["choices"][0]["text"]
+response_stripped = response.rstrip()
+unwanted_response_suffix = suffix.rstrip()
+unwanted_response_length = len(unwanted_response_suffix)
+
+filtered = False
+if unwanted_response_suffix and response_stripped[-unwanted_response_length:] == unwanted_response_suffix:
+    response = response_stripped[:-unwanted_response_length]
+    filtered = True
+
+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")
+```
+
+#### Simple llama-cpp-python example function calling code
+
+```python
+from llama_cpp import Llama
+
+# Chat Completion API
+
+grammar = LlamaGrammar.from_json_schema(json.dumps({
+    "type": "array",
+    "items": {
+        "type": "object",
+        "required": [ "name", "arguments" ],
+        "properties": {
+            "name": {
+                "type": "string"
+            },
+            "arguments": {
+                "type": "object"
+            }
+        }
+    }
+}))
+
+llm = Llama(model_path="./granite-3.2-8b-instruct.IQ4_XS.gguf", n_gpu_layers=41, n_ctx=131072)
+response = llm.create_chat_completion(
+      temperature = 0.0,
+      repeat_penalty = 1.0,
+      messages = [
+        {
+          "role": "user",
+          "content": "What's the weather like in Oslo and Stockholm?"
+        }
+      ],
+      tools=[{
+        "type": "function",
+        "function": {
+          "name": "get_current_weather",
+          "description": "Get the current weather in a given location",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA"
+              },
+              "unit": {
+                "type": "string",
+                "enum": [ "celsius", "fahrenheit" ]
+              }
+            },
+            "required": [ "location" ]
+          }
+        }
+      }],
+      grammar = grammar
+)
+print(json.loads(response["choices"][0]["text"]))
+
+print(llm.create_chat_completion(
+      temperature = 0.0,
+      repeat_penalty = 1.0,
+      messages = [
+        {
+          "role": "user",
+          "content": "What's the weather like in Oslo?"
+        },
+        { # The tool_calls is from the response to the above with tool_choice active
+          "role": "assistant",
+          "content": None,
+          "tool_calls": [
+            {
+              "id": "call__0_get_current_weather_cmpl-...",
+              "type": "function",
+              "function": {
+                "name": "get_current_weather",
+                "arguments": { "location": "Oslo, Norway" , "unit": "celsius" }
+              }
+            }
+          ]
+        },
+        { # The tool_call_id is from tool_calls and content is the result from the function call you made
+          "role": "tool",
+          "content": "20",
+          "tool_call_id": "call__0_get_current_weather_cmpl-..."
+        }
+      ],
+      tools=[{
+        "type": "function",
+        "function": {
+          "name": "get_current_weather",
+          "description": "Get the current weather in a given location",
+          "parameters": {
+            "type": "object",
+            "properties": {
+              "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA"
+              },
+              "unit": {
+                "type": "string",
+                "enum": [ "celsius", "fahrenheit" ]
+              }
+            },
+            "required": [ "location" ]
+          }
+        }
+      }],
+      #tool_choice={
+      #  "type": "function",
+      #  "function": {
+      #    "name": "get_current_weather"
+      #  }
+      #}
+))
+```
+
+<!-- README_GGUF.md-how-to-run end -->
+
+<!-- original-model-card start -->
+# Granite-3.2-8B-Instruct
+
+**Model Summary:**
+Granite-3.2-8B-Instruct is an 8-billion-parameter, long-context AI model fine-tuned for thinking capabilities. Built on top of [Granite-3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct), it has been trained using a mix of permissively licensed open-source datasets and internally generated synthetic data designed for reasoning tasks. The model allows controllability of its thinking capability, ensuring it is applied only when required.
+
+
+- **Developers:** Granite Team, IBM
+- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
+- **Release Date**: February 26th, 2025
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
+
+**Supported Languages:** 
+English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may finetune this Granite model for languages beyond these 12 languages.
+
+**Intended Use:** 
+This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.
+
+**Capabilities**
+* **Thinking**
+* Summarization
+* Text classification
+* Text extraction
+* Question-answering
+* Retrieval Augmented Generation (RAG)
+* Code related tasks
+* Function-calling tasks
+* Multilingual dialog use cases
+* Long-context tasks including long document/meeting summarization, long document QA, etc.
+
+
+
+**Generation:** 
+This is a simple example of how to use Granite-3.2-8B-Instruct model.
+
+Install the following libraries:
+
+```shell
+pip install torch torchvision torchaudio
+pip install accelerate
+pip install transformers
+```
+Then, copy the snippet from the section that is relevant for your use case.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+import torch
+
+model_path="ibm-granite/granite-3.2-8b-instruct"
+device="cuda"
+model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        device_map=device,
+        torch_dtype=torch.bfloat16,
+    )
+tokenizer = AutoTokenizer.from_pretrained(
+        model_path
+)
+
+conv = [{"role": "user", "content":"You have 10 liters of a 30% acid solution. How many liters of a 70% acid solution must be added to achieve a 50% acid mixture?"}]
+
+input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)
+
+set_seed(42)
+output = model.generate(
+    **input_ids,
+    max_new_tokens=8192,
+)
+
+prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
+print(prediction)
+```
+
+**Example Outputs**
+- thinking=True
+```md
+Here is my thought process:
+
+1. We know we start with 10 liters of a 30% acid solution, so there's 3 liters of pure acid in it (10 liters * 0.3).
+2. We need to determine how much of the 70% acid solution to add such that the final mixture is 50% acid.
+3. Let's say x liters of the 70% acid solution are added. This will contribute 0.7x liters of pure acid.
+4. The total volume of the new mixture will be 10 + x liters, and the total amount of pure acid will be 3 + 0.7x liters.
+5. We need to set up an equation where (3 + 0.7x) / (10 + x) = 0.5 (since we want a 50% acid mixture).
+6. Solving this equation for x will give us the volume of 70% solution needed.
+
+Here is my response:
+
+To solve this, we need to set up an equation based on the concentration of acid before and after mixing. 
+
+1. **Initial Acid Content**: 
+   - We start with 10 liters of a 30% acid solution. 
+   - Pure acid in this = 10 * 0.3 = 3 liters.
+
+2. **Variable for Addition**:
+   - Let x be the liters of 70% acid solution to be added.
+   - Pure acid in x liters of 70% solution = 0.7x liters.
+
+3. **Final Mixture Concentration**:
+   - Total volume of mixture = 10 + x liters.
+   - Total pure acid in the mixture = 3 (from initial solution) + 0.7x (from added solution).
+
+4. **Setting up the equation for 50% acid concentration**:
+   - We require that the ratio of pure acid to total volume is 0.5 (50%).
+   - Therefore, the equation is:
+
+   \[
+   \frac{3 + 0.7x}{10 + x} = 0.5
+   \]
+
+5. **Solving the equation**:
+
+   Multiply both sides by (10 + x) to clear the fraction:
+
+   \[
+   3 + 0.7x = 0.5(10 + x)
+   \]
+
+   Expanding the right side:
+
+   \[
+   3 + 0.7x = 5 + 0.5x
+   \]
+
+   Subtract 0.5x from both sides:
+
+   \[
+   3 + 0.2x = 5
+   \]
+
+   Subtract 3 from both sides:
+
+   \[
+   0.2x = 2
+   \]
+
+   Divide by 0.2:
+
+   \[
+   x = 10
+   \]
+
+So, 10 liters of the 70% acid solution must be added to the initial 10 liters of 30% solution to get a 50% acid mixture. 
+
+Let's verify: 
+
+- Initial acid: 3 liters
+- Acid from added solution: 0.7 * 10 = 7 liters
+- Total acid: 3 + 7 = 10 liters
+- Total volume: 10 + 10 = 20 liters
+- Acid percentage: \(\frac{10}{20} = 0.5 = 50\%\)
+
+This confirms our solution. 
+
+Final answer: 10 liters of 70% acid solution should be added.
+```
+- thinking=False
+```md
+Let's denote the amount of 70% acid solution to be added as x liters.
+
+We start with 10 liters of a 30% acid solution, which means there are 0.3 * 10 = 3 liters of pure acid in it.
+
+We want to end up with a 50% acid solution after adding x liters of 70% acid solution. The total volume of the mixture will be 10 + x liters, and we want half of this volume to be pure acid.
+
+So, the amount of pure acid in the final mixture should be 0.5 * (10 + x) liters.
+
+The amount of pure acid in the final mixture comes from two sources: the original 30% solution and the added 70% solution. So, we can set up the following equation:
+
+3 (from the original solution) + 0.7x (from the added solution) = 0.5 * (10 + x)
+
+Now, let's solve for x:
+
+3 + 0.7x = 5 + 0.5x
+0.7x - 0.5x = 5 - 3
+0.2x = 2
+x = 2 / 0.2
+x = 10
+
+So, you need to add 10 liters of a 70% acid solution to the 10 liters of a 30% acid solution to get a 50% acid mixture.
+```
+
+**Evaluation Results:**
+<table>
+  
+<thead>
+  <tr>
+    <th style="text-align:left; background-color: #001d6c; color: white;">Models</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">ArenaHard</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">Alpaca-Eval-2</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">MMLU</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">PopQA</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">TruthfulQA</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">BigBenchHard</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">DROP</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">GSM8K</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">HumanEval</th>
+   <th style="text-align:center; background-color: #001d6c; color: white;">HumanEval+</th>
+  <th style="text-align:center; background-color: #001d6c; color: white;">IFEval</th>
+    <th style="text-align:center; background-color: #001d6c; color: white;">AttaQ</th>
+  </tr></thead>
+  <tbody>
+  <tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: black;">Llama-3.1-8B-Instruct</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">36.43</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">27.22</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">69.15</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">28.79</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">52.79</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">72.66</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">61.48</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">83.24</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">85.32</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">80.15</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">79.10</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">83.43</td>
+  </tr>
+           
+  <tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: black;">DeepSeek-R1-Distill-Llama-8B</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">17.17</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">21.85</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">45.80</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">13.25</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">47.43</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">65.71</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">44.46</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">72.18</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">67.54</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">62.91</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">66.50</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">42.87</td>
+  </tr>
+      
+  <tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: black;">Qwen-2.5-7B-Instruct</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">25.44</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">30.34</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">74.30</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">18.12</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">63.06</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">70.40</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">54.71</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">84.46</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">93.35</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">89.91</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">74.90</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">81.90</td>
+  </tr>
+      
+  <tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: black;">DeepSeek-R1-Distill-Qwen-7B</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">10.36</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">15.35</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">50.72</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">9.94</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">47.14</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">65.04</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">42.76</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">78.47</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">79.89</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">78.43</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">59.10</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">42.45</td>
+  </tr>
+
+  <tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.1-8B-Instruct</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">37.58</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">30.34</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">66.77</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">28.7</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">65.84</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">68.55</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">50.78</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">79.15</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">89.63</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">85.79</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">73.20</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">85.73</td>
+  </tr>
+      
+      
+<tr>
+    <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.1-2B-Instruct</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">23.3</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">27.17</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">57.11</td> 
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">20.55</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">59.79</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">54.46</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">18.68</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">67.55</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">79.45</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">75.26</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">63.59</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">84.7</td>
+  </tr>
+      
+ 
+  <tr>
+      <td style="text-align:left; background-color: #DAE8FF; color: black;">Granite-3.2-2B-Instruct</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">24.86</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">34.51</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">57.18</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">20.56</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">59.8</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">52.27</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">21.12</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">67.02</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">80.13</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">73.39</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">61.55</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">83.23</td>
+  </tr> 
+      
+<tr>
+      <td style="text-align:left; background-color: #DAE8FF; color: black;"><b>Granite-3.2-8B-Instruct</b></td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">55.25</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">61.19</td>
+   <td style="text-align:center; background-color: #DAE8FF; color: black;">66.79</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">28.04</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">66.92</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">64.77</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">50.95</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">81.65</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">89.35</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">85.72</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">74.31</td>
+    <td style="text-align:center; background-color: #DAE8FF; color: black;">85.42</td>
+ 
+  </tr>
+
+     
+      
+</tbody></table>
+
+**Training Data:** 
+Overall, our training data is largely comprised of two key sources: (1) publicly available datasets with permissive license, (2) internal synthetically generated data targeted to enhance reasoning capabilites. 
+<!-- A detailed attribution of datasets can be found in [Granite 3.2 Technical Report (coming soon)](#), and [Accompanying Author List](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/author-ack.pdf). -->
+
+**Infrastructure:**
+We train Granite-3.2-8B-Instruct using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
+
+**Ethical Considerations and Limitations:** 
+Granite-3.2-8B-Instruct builds upon Granite-3.1-8B-Instruct, leveraging both permissively licensed open-source and select proprietary data for enhanced performance. Since it inherits its foundation from the previous model, all ethical considerations and limitations applicable to [Granite-3.1-8B-Instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) remain relevant.
+
+
+**Resources**
+- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
+- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
+- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
+
+<!-- ## Citation
+```
+@misc{granite-models,
+  author = {author 1, author2, ...},
+  title = {},
+  journal = {},
+  volume = {},
+  year = {2024},
+  url = {https://arxiv.org/abs/0000.00000},
+}
+``` -->

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