---
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
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</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>
</p>
</div>
</div>
## 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'
```