File size: 7,147 Bytes

---
license: apache-2.0
datasets:
- databricks/databricks-dolly-15k
- lucasmccabe-lmi/CodeAlpaca-20k
base_model: HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca
tags:
- TensorBlock
- GGUF
---

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## HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca - GGUF

This repo contains GGUF format model files for [HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca](https://huggingface.co/HenryJJ/Instruct_Yi-6B_Dolly_CodeAlpaca).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

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## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q2_K.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q2_K.gguf) | Q2_K | 2.337 GB | smallest, significant quality loss - not recommended for most purposes |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q3_K_S.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q3_K_S.gguf) | Q3_K_S | 2.709 GB | very small, high quality loss |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q3_K_M.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q3_K_M.gguf) | Q3_K_M | 2.993 GB | very small, high quality loss |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q3_K_L.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q3_K_L.gguf) | Q3_K_L | 3.237 GB | small, substantial quality loss |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q4_0.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q4_0.gguf) | Q4_0 | 3.479 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q4_K_S.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q4_K_S.gguf) | Q4_K_S | 3.503 GB | small, greater quality loss |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q4_K_M.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q4_K_M.gguf) | Q4_K_M | 3.674 GB | medium, balanced quality - recommended |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q5_0.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q5_0.gguf) | Q5_0 | 4.204 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q5_K_S.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q5_K_S.gguf) | Q5_K_S | 4.204 GB | large, low quality loss - recommended |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q5_K_M.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q5_K_M.gguf) | Q5_K_M | 4.304 GB | large, very low quality loss - recommended |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q6_K.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q6_K.gguf) | Q6_K | 4.974 GB | very large, extremely low quality loss |
| [Instruct_Yi-6B_Dolly_CodeAlpaca-Q8_0.gguf](https://huggingface.co/tensorblock/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF/blob/main/Instruct_Yi-6B_Dolly_CodeAlpaca-Q8_0.gguf) | Q8_0 | 6.442 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/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF --include "Instruct_Yi-6B_Dolly_CodeAlpaca-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/Instruct_Yi-6B_Dolly_CodeAlpaca-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```