File size: 7,680 Bytes
ece1afc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 |
---
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'
```
|