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granite-3b-code-instruct-2k-GGUF

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granite-3b-code-instruct-2k-GGUF
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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
<table border="1" cellspacing="0" cellpadding="10">
  <tr>
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        display: inline-block;
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        background-color: #FF7F50;
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        font-weight: bold;
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      ">🚀 Try it now! 🚀</a>
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    <th style="font-size: 25px;">Awesome MCP Servers</th>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
    <th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
  </tr>
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      <a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
        display: inline-block;
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        font-weight: bold;
        font-family: sans-serif;
      ">👀 See what we built 👀</a>
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      <a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
        display: inline-block;
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        background-color: #FF7F50;
        color: white;
        text-decoration: none;
        border-radius: 6px;
        font-weight: bold;
        font-family: sans-serif;
      ">👀 See what we built 👀</a>
    </th>
  </tr>
</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'
```