---
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
---

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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).

## 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/tree/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/tree/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/tree/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/tree/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/tree/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/tree/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/tree/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/tree/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/tree/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/tree/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/tree/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/tree/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'
```