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 ---
pipeline_tag: text-generation
base_model: ibm-granite/granite-3b-code-instruct-2k
inference: false
license: apache-2.0
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
- TensorBlock
- GGUF
model-index:
- name: granite-3b-code-instruct
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 51.2
name: pass@1
- type: pass@1
value: 43.9
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 31.7
name: pass@1
- type: pass@1
value: 40.2
name: pass@1
- type: pass@1
value: 29.3
name: pass@1
- type: pass@1
value: 39.6
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 39.0
name: pass@1
- type: pass@1
value: 14.0
name: pass@1
- type: pass@1
value: 23.8
name: pass@1
- type: pass@1
value: 12.8
name: pass@1
- type: pass@1
value: 26.8
name: pass@1
- type: pass@1
value: 28.0
name: pass@1
- type: pass@1
value: 33.5
name: pass@1
- type: pass@1
value: 27.4
name: pass@1
- type: pass@1
value: 31.7
name: pass@1
- type: pass@1
value: 16.5
name: pass@1
---
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## ibm-granite/granite-3b-code-instruct-2k - GGUF
This repo contains GGUF format model files for [ibm-granite/granite-3b-code-instruct-2k](https://huggingface.co/ibm-granite/granite-3b-code-instruct-2k).
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).
## Our projects
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">👀 See what we built 👀</a>
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</table>
## Prompt template
```
System:
{system_prompt}
Question:
{prompt}
Answer:
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [granite-3b-code-instruct-2k-Q2_K.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q2_K.gguf) | Q2_K | 1.247 GB | smallest, significant quality loss - not recommended for most purposes |
| [granite-3b-code-instruct-2k-Q3_K_S.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q3_K_S.gguf) | Q3_K_S | 1.445 GB | very small, high quality loss |
| [granite-3b-code-instruct-2k-Q3_K_M.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q3_K_M.gguf) | Q3_K_M | 1.608 GB | very small, high quality loss |
| [granite-3b-code-instruct-2k-Q3_K_L.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q3_K_L.gguf) | Q3_K_L | 1.747 GB | small, substantial quality loss |
| [granite-3b-code-instruct-2k-Q4_0.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q4_0.gguf) | Q4_0 | 1.860 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [granite-3b-code-instruct-2k-Q4_K_S.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q4_K_S.gguf) | Q4_K_S | 1.875 GB | small, greater quality loss |
| [granite-3b-code-instruct-2k-Q4_K_M.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q4_K_M.gguf) | Q4_K_M | 1.986 GB | medium, balanced quality - recommended |
| [granite-3b-code-instruct-2k-Q5_0.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q5_0.gguf) | Q5_0 | 2.251 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [granite-3b-code-instruct-2k-Q5_K_S.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q5_K_S.gguf) | Q5_K_S | 2.251 GB | large, low quality loss - recommended |
| [granite-3b-code-instruct-2k-Q5_K_M.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q5_K_M.gguf) | Q5_K_M | 2.316 GB | large, very low quality loss - recommended |
| [granite-3b-code-instruct-2k-Q6_K.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q6_K.gguf) | Q6_K | 2.666 GB | very large, extremely low quality loss |
| [granite-3b-code-instruct-2k-Q8_0.gguf](https://huggingface.co/tensorblock/granite-3b-code-instruct-2k-GGUF/blob/main/granite-3b-code-instruct-2k-Q8_0.gguf) | Q8_0 | 3.451 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-3b-code-instruct-2k-GGUF --include "granite-3b-code-instruct-2k-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-3b-code-instruct-2k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```