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Swallow-MS-7b-instruct-v0.1-GGUF

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Swallow-MS-7b-instruct-v0.1-GGUF
File size: 7,080 Bytes
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---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
model_type: mistral
license: apache-2.0
base_model: tokyotech-llm/Swallow-MS-7b-instruct-v0.1
tags:
- TensorBlock
- GGUF
---

<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;">
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## tokyotech-llm/Swallow-MS-7b-instruct-v0.1 - GGUF

This repo contains GGUF format model files for [tokyotech-llm/Swallow-MS-7b-instruct-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-instruct-v0.1).

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).

## Our projects
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        display: inline-block;
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      ">🚀 Try it now! 🚀</a>
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      ">👀 See what we built 👀</a>
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</table>
## Prompt template

```
<s>[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST] 
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Swallow-MS-7b-instruct-v0.1-Q2_K.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q2_K.gguf) | Q2_K | 2.770 GB | smallest, significant quality loss - not recommended for most purposes |
| [Swallow-MS-7b-instruct-v0.1-Q3_K_S.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q3_K_S.gguf) | Q3_K_S | 3.220 GB | very small, high quality loss |
| [Swallow-MS-7b-instruct-v0.1-Q3_K_M.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q3_K_M.gguf) | Q3_K_M | 3.575 GB | very small, high quality loss |
| [Swallow-MS-7b-instruct-v0.1-Q3_K_L.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q3_K_L.gguf) | Q3_K_L | 3.878 GB | small, substantial quality loss |
| [Swallow-MS-7b-instruct-v0.1-Q4_0.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q4_0.gguf) | Q4_0 | 4.170 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Swallow-MS-7b-instruct-v0.1-Q4_K_S.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q4_K_S.gguf) | Q4_K_S | 4.202 GB | small, greater quality loss |
| [Swallow-MS-7b-instruct-v0.1-Q4_K_M.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q4_K_M.gguf) | Q4_K_M | 4.430 GB | medium, balanced quality - recommended |
| [Swallow-MS-7b-instruct-v0.1-Q5_0.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q5_0.gguf) | Q5_0 | 5.065 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Swallow-MS-7b-instruct-v0.1-Q5_K_S.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q5_K_S.gguf) | Q5_K_S | 5.065 GB | large, low quality loss - recommended |
| [Swallow-MS-7b-instruct-v0.1-Q5_K_M.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q5_K_M.gguf) | Q5_K_M | 5.198 GB | large, very low quality loss - recommended |
| [Swallow-MS-7b-instruct-v0.1-Q6_K.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q6_K.gguf) | Q6_K | 6.015 GB | very large, extremely low quality loss |
| [Swallow-MS-7b-instruct-v0.1-Q8_0.gguf](https://huggingface.co/tensorblock/Swallow-MS-7b-instruct-v0.1-GGUF/blob/main/Swallow-MS-7b-instruct-v0.1-Q8_0.gguf) | Q8_0 | 7.790 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/Swallow-MS-7b-instruct-v0.1-GGUF --include "Swallow-MS-7b-instruct-v0.1-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/Swallow-MS-7b-instruct-v0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```