---
base_model: willyli/Seed-Coder-8B-Instruct-KTO
library_name: transformers
model_name: Seed-Coder-8B-Instruct-KTO
tags:
- generated_from_trainer
- trl
- kto
- TensorBlock
- GGUF
licence: license
---

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## willyli/Seed-Coder-8B-Instruct-KTO - GGUF

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        Join our Discord to learn more about what we're building ↗
    </a>
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This repo contains GGUF format model files for [willyli/Seed-Coder-8B-Instruct-KTO](https://huggingface.co/willyli/Seed-Coder-8B-Instruct-KTO).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).

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## Prompt template

```
<[begin▁of▁sentence]>system
{system_prompt}<[end▁of▁sentence]><[begin▁of▁sentence]>user
{prompt}<[end▁of▁sentence]><[begin▁of▁sentence]>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Seed-Coder-8B-Instruct-KTO-Q2_K.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q2_K.gguf) | Q2_K | 3.304 GB | smallest, significant quality loss - not recommended for most purposes |
| [Seed-Coder-8B-Instruct-KTO-Q3_K_S.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q3_K_S.gguf) | Q3_K_S | 3.801 GB | very small, high quality loss |
| [Seed-Coder-8B-Instruct-KTO-Q3_K_M.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q3_K_M.gguf) | Q3_K_M | 4.155 GB | very small, high quality loss |
| [Seed-Coder-8B-Instruct-KTO-Q3_K_L.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q3_K_L.gguf) | Q3_K_L | 4.458 GB | small, substantial quality loss |
| [Seed-Coder-8B-Instruct-KTO-Q4_0.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q4_0.gguf) | Q4_0 | 4.812 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Seed-Coder-8B-Instruct-KTO-Q4_K_S.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q4_K_S.gguf) | Q4_K_S | 4.843 GB | small, greater quality loss |
| [Seed-Coder-8B-Instruct-KTO-Q4_K_M.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q4_K_M.gguf) | Q4_K_M | 5.071 GB | medium, balanced quality - recommended |
| [Seed-Coder-8B-Instruct-KTO-Q5_0.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q5_0.gguf) | Q5_0 | 5.764 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Seed-Coder-8B-Instruct-KTO-Q5_K_S.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q5_K_S.gguf) | Q5_K_S | 5.764 GB | large, low quality loss - recommended |
| [Seed-Coder-8B-Instruct-KTO-Q5_K_M.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q5_K_M.gguf) | Q5_K_M | 5.897 GB | large, very low quality loss - recommended |
| [Seed-Coder-8B-Instruct-KTO-Q6_K.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q6_K.gguf) | Q6_K | 6.775 GB | very large, extremely low quality loss |
| [Seed-Coder-8B-Instruct-KTO-Q8_0.gguf](https://huggingface.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF/blob/main/Seed-Coder-8B-Instruct-KTO-Q8_0.gguf) | Q8_0 | 8.773 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/willyli_Seed-Coder-8B-Instruct-KTO-GGUF --include "Seed-Coder-8B-Instruct-KTO-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/willyli_Seed-Coder-8B-Instruct-KTO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```