README.md

---
library_name: transformers
license: llama3.2
base_model: wassname/llama-3-2-1b-sft
tags:
- alignment-handbook
- generated_from_trainer
- TensorBlock
- GGUF
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: llama-3-2-1b-sft
  results: []
---

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## wassname/llama-3-2-1b-sft - GGUF

This repo contains GGUF format model files for [wassname/llama-3-2-1b-sft](https://huggingface.co/wassname/llama-3-2-1b-sft).

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).


<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
    </a>
</div>

## Prompt template


```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama-3-2-1b-sft-Q2_K.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q2_K.gguf) | Q2_K | 0.541 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-3-2-1b-sft-Q3_K_S.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q3_K_S.gguf) | Q3_K_S | 0.598 GB | very small, high quality loss |
| [llama-3-2-1b-sft-Q3_K_M.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q3_K_M.gguf) | Q3_K_M | 0.643 GB | very small, high quality loss |
| [llama-3-2-1b-sft-Q3_K_L.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q3_K_L.gguf) | Q3_K_L | 0.682 GB | small, substantial quality loss |
| [llama-3-2-1b-sft-Q4_0.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q4_0.gguf) | Q4_0 | 0.718 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama-3-2-1b-sft-Q4_K_S.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q4_K_S.gguf) | Q4_K_S | 0.722 GB | small, greater quality loss |
| [llama-3-2-1b-sft-Q4_K_M.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q4_K_M.gguf) | Q4_K_M | 0.752 GB | medium, balanced quality - recommended |
| [llama-3-2-1b-sft-Q5_0.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q5_0.gguf) | Q5_0 | 0.831 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama-3-2-1b-sft-Q5_K_S.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q5_K_S.gguf) | Q5_K_S | 0.831 GB | large, low quality loss - recommended |
| [llama-3-2-1b-sft-Q5_K_M.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q5_K_M.gguf) | Q5_K_M | 0.849 GB | large, very low quality loss - recommended |
| [llama-3-2-1b-sft-Q6_K.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q6_K.gguf) | Q6_K | 0.952 GB | very large, extremely low quality loss |
| [llama-3-2-1b-sft-Q8_0.gguf](https://huggingface.co/tensorblock/llama-3-2-1b-sft-GGUF/blob/main/llama-3-2-1b-sft-Q8_0.gguf) | Q8_0 | 1.230 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-3-2-1b-sft-GGUF --include "llama-3-2-1b-sft-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-3-2-1b-sft-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```