File size: 6,724 Bytes
8372405 f37c0bb 8372405 f37c0bb 8372405 f37c0bb 8372405 |
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 |
---
pipeline_tag: text-generation
inference: true
license: apache-2.0
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
- TensorBlock
- GGUF
base_model: ibm-granite/granite-34b-code-base-8k
model-index:
- name: granite-34b-code-base-8k
results:
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 47.2
name: pass@1
- task:
type: text-generation
dataset:
name: MBPP+
type: evalplus/mbppplus
metrics:
- type: pass@1
value: 53.1
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 48.2
name: pass@1
- type: pass@1
value: 54.9
name: pass@1
- type: pass@1
value: 61.6
name: pass@1
- type: pass@1
value: 40.2
name: pass@1
- type: pass@1
value: 50.0
name: pass@1
- type: pass@1
value: 39.6
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 26.2
name: pass@1
- type: pass@1
value: 47.0
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 36.6
name: pass@1
- type: pass@1
value: 25.0
name: pass@1
- type: pass@1
value: 20.1
name: pass@1
- type: pass@1
value: 30.5
name: pass@1
- type: pass@1
value: 40.9
name: pass@1
- type: pass@1
value: 34.1
name: pass@1
- type: pass@1
value: 39.0
name: pass@1
- type: pass@1
value: 12.2
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-34b-code-base-8k - GGUF
This repo contains GGUF format model files for [ibm-granite/granite-34b-code-base-8k](https://huggingface.co/ibm-granite/granite-34b-code-base-8k).
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
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [granite-34b-code-base-8k-Q2_K.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q2_K.gguf) | Q2_K | 12.207 GB | smallest, significant quality loss - not recommended for most purposes |
| [granite-34b-code-base-8k-Q3_K_S.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q3_K_S.gguf) | Q3_K_S | 13.791 GB | very small, high quality loss |
| [granite-34b-code-base-8k-Q3_K_M.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q3_K_M.gguf) | Q3_K_M | 16.361 GB | very small, high quality loss |
| [granite-34b-code-base-8k-Q3_K_L.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q3_K_L.gguf) | Q3_K_L | 18.207 GB | small, substantial quality loss |
| [granite-34b-code-base-8k-Q4_0.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q4_0.gguf) | Q4_0 | 17.917 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [granite-34b-code-base-8k-Q4_K_S.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q4_K_S.gguf) | Q4_K_S | 18.110 GB | small, greater quality loss |
| [granite-34b-code-base-8k-Q4_K_M.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q4_K_M.gguf) | Q4_K_M | 19.915 GB | medium, balanced quality - recommended |
| [granite-34b-code-base-8k-Q5_0.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q5_0.gguf) | Q5_0 | 21.800 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [granite-34b-code-base-8k-Q5_K_S.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q5_K_S.gguf) | Q5_K_S | 21.800 GB | large, low quality loss - recommended |
| [granite-34b-code-base-8k-Q5_K_M.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q5_K_M.gguf) | Q5_K_M | 23.050 GB | large, very low quality loss - recommended |
| [granite-34b-code-base-8k-Q6_K.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q6_K.gguf) | Q6_K | 25.926 GB | very large, extremely low quality loss |
| [granite-34b-code-base-8k-Q8_0.gguf](https://huggingface.co/tensorblock/granite-34b-code-base-8k-GGUF/blob/main/granite-34b-code-base-8k-Q8_0.gguf) | Q8_0 | 33.518 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-34b-code-base-8k-GGUF --include "granite-34b-code-base-8k-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-34b-code-base-8k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|