---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.0
- TensorBlock
- GGUF
base_model: ibm-granite/granite-3.0-3b-a800m-instruct
model-index:
- name: granite-3.0-2b-instruct
  results:
  - task:
      type: text-generation
    dataset:
      name: IFEval
      type: instruction-following
    metrics:
    - type: pass@1
      value: 42.49
      name: pass@1
    - type: pass@1
      value: 7.02
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: AGI-Eval
      type: human-exams
    metrics:
    - type: pass@1
      value: 25.7
      name: pass@1
    - type: pass@1
      value: 50.16
      name: pass@1
    - type: pass@1
      value: 20.51
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: OBQA
      type: commonsense
    metrics:
    - type: pass@1
      value: 40.8
      name: pass@1
    - type: pass@1
      value: 59.95
      name: pass@1
    - type: pass@1
      value: 71.86
      name: pass@1
    - type: pass@1
      value: 67.01
      name: pass@1
    - type: pass@1
      value: 48
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: BoolQ
      type: reading-comprehension
    metrics:
    - type: pass@1
      value: 78.65
      name: pass@1
    - type: pass@1
      value: 6.71
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: ARC-C
      type: reasoning
    metrics:
    - type: pass@1
      value: 50.94
      name: pass@1
    - type: pass@1
      value: 26.85
      name: pass@1
    - type: pass@1
      value: 37.7
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: HumanEvalSynthesis
      type: code
    metrics:
    - type: pass@1
      value: 39.63
      name: pass@1
    - type: pass@1
      value: 40.85
      name: pass@1
    - type: pass@1
      value: 35.98
      name: pass@1
    - type: pass@1
      value: 27.4
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: GSM8K
      type: math
    metrics:
    - type: pass@1
      value: 47.54
      name: pass@1
    - type: pass@1
      value: 19.86
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: PAWS-X (7 langs)
      type: multilingual
    metrics:
    - type: pass@1
      value: 50.23
      name: pass@1
    - type: pass@1
      value: 28.87
      name: pass@1
---

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</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
    <div style="display: flex; flex-direction: column; align-items: flex-start;">
        <p style="margin-top: 0.5em; margin-bottom: 0em;">
            Feedback and support: TensorBlock's  <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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## ibm-granite/granite-3.0-3b-a800m-instruct - GGUF

This repo contains GGUF format model files for [ibm-granite/granite-3.0-3b-a800m-instruct](https://huggingface.co/ibm-granite/granite-3.0-3b-a800m-instruct).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template

```
<|start_of_role|>system<|end_of_role|>{system_prompt}<|end_of_text|>
<|start_of_role|>user<|end_of_role|>{prompt}<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [granite-3.0-3b-a800m-instruct-Q2_K.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q2_K.gguf) | Q2_K | 1.266 GB | smallest, significant quality loss - not recommended for most purposes |
| [granite-3.0-3b-a800m-instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q3_K_S.gguf) | Q3_K_S | 1.489 GB | very small, high quality loss |
| [granite-3.0-3b-a800m-instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q3_K_M.gguf) | Q3_K_M | 1.644 GB | very small, high quality loss |
| [granite-3.0-3b-a800m-instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q3_K_L.gguf) | Q3_K_L | 1.774 GB | small, substantial quality loss |
| [granite-3.0-3b-a800m-instruct-Q4_0.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q4_0.gguf) | Q4_0 | 1.926 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [granite-3.0-3b-a800m-instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q4_K_S.gguf) | Q4_K_S | 1.942 GB | small, greater quality loss |
| [granite-3.0-3b-a800m-instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q4_K_M.gguf) | Q4_K_M | 2.059 GB | medium, balanced quality - recommended |
| [granite-3.0-3b-a800m-instruct-Q5_0.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q5_0.gguf) | Q5_0 | 2.338 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [granite-3.0-3b-a800m-instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q5_K_S.gguf) | Q5_K_S | 2.338 GB | large, low quality loss - recommended |
| [granite-3.0-3b-a800m-instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q5_K_M.gguf) | Q5_K_M | 2.407 GB | large, very low quality loss - recommended |
| [granite-3.0-3b-a800m-instruct-Q6_K.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q6_K.gguf) | Q6_K | 2.776 GB | very large, extremely low quality loss |
| [granite-3.0-3b-a800m-instruct-Q8_0.gguf](https://huggingface.co/tensorblock/granite-3.0-3b-a800m-instruct-GGUF/blob/main/granite-3.0-3b-a800m-instruct-Q8_0.gguf) | Q8_0 | 3.593 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/granite-3.0-3b-a800m-instruct-GGUF --include "granite-3.0-3b-a800m-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/granite-3.0-3b-a800m-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```