README.md

---
library_name: transformers
tags:
- math
- lora
- science
- chemistry
- biology
- code
- text-generation-inference
- unsloth
- llama
- TensorBlock
- GGUF
license: apache-2.0
datasets:
- HuggingFaceTB/smoltalk
language:
- en
- de
- es
- fr
- it
- pt
- hi
- th
base_model: suayptalha/FastLlama-3.2-1B-Instruct
---

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## suayptalha/FastLlama-3.2-1B-Instruct - GGUF

This repo contains GGUF format model files for [suayptalha/FastLlama-3.2-1B-Instruct](https://huggingface.co/suayptalha/FastLlama-3.2-1B-Instruct).

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
<table border="1" cellspacing="0" cellpadding="10">
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      <a href="https://github.com/TensorBlock/forge" target="_blank" style="
        display: inline-block;
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    <th style="font-size: 25px;">Awesome MCP Servers</th>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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      ">👀 See what we built 👀</a>
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</table>
## Prompt template

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

Cutting Knowledge Date: December 2023
Today Date: 01 Jan 2025

{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 |
| -------- | ---------- | --------- | ----------- |
| [FastLlama-3.2-1B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q2_K.gguf) | Q2_K | 0.581 GB | smallest, significant quality loss - not recommended for most purposes |
| [FastLlama-3.2-1B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q3_K_S.gguf) | Q3_K_S | 0.642 GB | very small, high quality loss |
| [FastLlama-3.2-1B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q3_K_M.gguf) | Q3_K_M | 0.691 GB | very small, high quality loss |
| [FastLlama-3.2-1B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q3_K_L.gguf) | Q3_K_L | 0.733 GB | small, substantial quality loss |
| [FastLlama-3.2-1B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q4_0.gguf) | Q4_0 | 0.771 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [FastLlama-3.2-1B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q4_K_S.gguf) | Q4_K_S | 0.776 GB | small, greater quality loss |
| [FastLlama-3.2-1B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q4_K_M.gguf) | Q4_K_M | 0.808 GB | medium, balanced quality - recommended |
| [FastLlama-3.2-1B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q5_0.gguf) | Q5_0 | 0.893 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [FastLlama-3.2-1B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q5_K_S.gguf) | Q5_K_S | 0.893 GB | large, low quality loss - recommended |
| [FastLlama-3.2-1B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q5_K_M.gguf) | Q5_K_M | 0.912 GB | large, very low quality loss - recommended |
| [FastLlama-3.2-1B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q6_K.gguf) | Q6_K | 1.022 GB | very large, extremely low quality loss |
| [FastLlama-3.2-1B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/FastLlama-3.2-1B-Instruct-GGUF/blob/main/FastLlama-3.2-1B-Instruct-Q8_0.gguf) | Q8_0 | 1.321 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/FastLlama-3.2-1B-Instruct-GGUF --include "FastLlama-3.2-1B-Instruct-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/FastLlama-3.2-1B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```