---
library_name: transformers
tags:
- korean
- text2sql
- TensorBlock
- GGUF
license: apache-2.0
datasets:
- sanghyun89/for_text2sql_wikiSQL_korean_by_google_translator_api
language:
- ko
- en
base_model: sanghyun89/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2
---

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## sanghyun89/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2 - GGUF

This repo contains GGUF format model files for [sanghyun89/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2](https://huggingface.co/sanghyun89/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2).

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

```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q2_K.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q2_K.gguf) | Q2_K | 1.364 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q3_K_S.gguf) | Q3_K_S | 1.543 GB | very small, high quality loss |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q3_K_M.gguf) | Q3_K_M | 1.687 GB | very small, high quality loss |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q3_K_L.gguf) | Q3_K_L | 1.815 GB | small, substantial quality loss |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q4_0.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q4_0.gguf) | Q4_0 | 1.917 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q4_K_S.gguf) | Q4_K_S | 1.928 GB | small, greater quality loss |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q4_K_M.gguf) | Q4_K_M | 2.019 GB | medium, balanced quality - recommended |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q5_0.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q5_0.gguf) | Q5_0 | 2.270 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q5_K_S.gguf) | Q5_K_S | 2.270 GB | large, low quality loss - recommended |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q5_K_M.gguf) | Q5_K_M | 2.322 GB | large, very low quality loss - recommended |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q6_K.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q6_K.gguf) | Q6_K | 2.644 GB | very large, extremely low quality loss |
| [llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q8_0.gguf](https://huggingface.co/tensorblock/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF/blob/main/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-Q8_0.gguf) | Q8_0 | 3.422 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/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF --include "llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-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/llama-3.2-3b-plantInsight_lab_v4-langchainPart2_result_ep2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```