File size: 7,502 Bytes

---
pipeline_tag: text-generation
inference:
  parameters:
    temperature: 0.2
    top_p: 0.95
widget:
- text: 'def print_hello_world():'
  example_title: Hello world
  group: Python
datasets:
- bigcode/the-stack-v2-train
license: bigcode-openrail-m
library_name: transformers
tags:
- code
- TensorBlock
- GGUF
base_model: bigcode/starcoder2-7b
model-index:
- name: starcoder2-7b
  results:
  - task:
      type: text-generation
    dataset:
      name: CruxEval-I
      type: cruxeval-i
    metrics:
    - type: pass@1
      value: 34.6
  - task:
      type: text-generation
    dataset:
      name: DS-1000
      type: ds-1000
    metrics:
    - type: pass@1
      value: 27.8
  - task:
      type: text-generation
    dataset:
      name: GSM8K (PAL)
      type: gsm8k-pal
    metrics:
    - type: accuracy
      value: 40.4
  - task:
      type: text-generation
    dataset:
      name: HumanEval+
      type: humanevalplus
    metrics:
    - type: pass@1
      value: 29.9
  - task:
      type: text-generation
    dataset:
      name: HumanEval
      type: humaneval
    metrics:
    - type: pass@1
      value: 35.4
  - task:
      type: text-generation
    dataset:
      name: RepoBench-v1.1
      type: repobench-v1.1
    metrics:
    - type: edit-smiliarity
      value: 72.07
---

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## bigcode/starcoder2-7b - GGUF

This repo contains GGUF format model files for [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b).

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).


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## Prompt template


```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [starcoder2-7b-Q2_K.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q2_K.gguf) | Q2_K | 2.641 GB | smallest, significant quality loss - not recommended for most purposes |
| [starcoder2-7b-Q3_K_S.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q3_K_S.gguf) | Q3_K_S | 2.960 GB | very small, high quality loss |
| [starcoder2-7b-Q3_K_M.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q3_K_M.gguf) | Q3_K_M | 3.410 GB | very small, high quality loss |
| [starcoder2-7b-Q3_K_L.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q3_K_L.gguf) | Q3_K_L | 3.794 GB | small, substantial quality loss |
| [starcoder2-7b-Q4_0.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q4_0.gguf) | Q4_0 | 3.820 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [starcoder2-7b-Q4_K_S.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q4_K_S.gguf) | Q4_K_S | 3.860 GB | small, greater quality loss |
| [starcoder2-7b-Q4_K_M.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q4_K_M.gguf) | Q4_K_M | 4.155 GB | medium, balanced quality - recommended |
| [starcoder2-7b-Q5_0.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q5_0.gguf) | Q5_0 | 4.628 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [starcoder2-7b-Q5_K_S.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q5_K_S.gguf) | Q5_K_S | 4.628 GB | large, low quality loss - recommended |
| [starcoder2-7b-Q5_K_M.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q5_K_M.gguf) | Q5_K_M | 4.801 GB | large, very low quality loss - recommended |
| [starcoder2-7b-Q6_K.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q6_K.gguf) | Q6_K | 5.487 GB | very large, extremely low quality loss |
| [starcoder2-7b-Q8_0.gguf](https://huggingface.co/tensorblock/starcoder2-7b-GGUF/blob/main/starcoder2-7b-Q8_0.gguf) | Q8_0 | 7.105 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/starcoder2-7b-GGUF --include "starcoder2-7b-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/starcoder2-7b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```