tensorblock/granite-8b-code-instruct-128k-GGUF
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2025-06-18 23:53:55 +00:00

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---
pipeline_tag: text-generation
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
- bigcode/self-oss-instruct-sc2-exec-filter-50k
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
- TensorBlock
- GGUF
base_model: ibm-granite/granite-8b-code-instruct-128k
model-index:
- name: granite-8B-Code-instruct-128k
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis (Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 62.2
name: pass@1
verified: false
- type: pass@1
value: 51.4
name: pass@1
verified: false
- type: pass@1
value: 38.9
name: pass@1
verified: false
- type: pass@1
value: 38.3
name: pass@1
verified: false
- task:
type: text-generation
dataset:
name: RepoQA (Python@16K)
type: repoqa
metrics:
- type: pass@1 (thresh=0.5)
value: 73.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 37.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 73.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 62.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 63.0
name: pass@1 (thresh=0.5)
verified: false
---
<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>
[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)
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## ibm-granite/granite-8b-code-instruct-128k - GGUF
This repo contains GGUF format model files for [ibm-granite/granite-8b-code-instruct-128k](https://huggingface.co/ibm-granite/granite-8b-code-instruct-128k).
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>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="Project A" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
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-8b-code-instruct-128k-Q2_K.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q2_K.gguf) | Q2_K | 2.852 GB | smallest, significant quality loss - not recommended for most purposes |
| [granite-8b-code-instruct-128k-Q3_K_S.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q3_K_S.gguf) | Q3_K_S | 3.304 GB | very small, high quality loss |
| [granite-8b-code-instruct-128k-Q3_K_M.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q3_K_M.gguf) | Q3_K_M | 3.674 GB | very small, high quality loss |
| [granite-8b-code-instruct-128k-Q3_K_L.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q3_K_L.gguf) | Q3_K_L | 3.993 GB | small, substantial quality loss |
| [granite-8b-code-instruct-128k-Q4_0.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q4_0.gguf) | Q4_0 | 4.276 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [granite-8b-code-instruct-128k-Q4_K_S.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q4_K_S.gguf) | Q4_K_S | 4.305 GB | small, greater quality loss |
| [granite-8b-code-instruct-128k-Q4_K_M.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q4_K_M.gguf) | Q4_K_M | 4.548 GB | medium, balanced quality - recommended |
| [granite-8b-code-instruct-128k-Q5_0.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q5_0.gguf) | Q5_0 | 5.190 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [granite-8b-code-instruct-128k-Q5_K_S.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q5_K_S.gguf) | Q5_K_S | 5.190 GB | large, low quality loss - recommended |
| [granite-8b-code-instruct-128k-Q5_K_M.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q5_K_M.gguf) | Q5_K_M | 5.330 GB | large, very low quality loss - recommended |
| [granite-8b-code-instruct-128k-Q6_K.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q6_K.gguf) | Q6_K | 6.161 GB | very large, extremely low quality loss |
| [granite-8b-code-instruct-128k-Q8_0.gguf](https://huggingface.co/tensorblock/granite-8b-code-instruct-128k-GGUF/blob/main/granite-8b-code-instruct-128k-Q8_0.gguf) | Q8_0 | 7.977 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-8b-code-instruct-128k-GGUF --include "granite-8b-code-instruct-128k-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-8b-code-instruct-128k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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