Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


llama-3-8b-Instruct - GGUF
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/llama-3-8b-Instruct/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-3-8b-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q2_K.gguf) | Q2_K | 2.96GB |
| [llama-3-8b-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [llama-3-8b-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [llama-3-8b-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [llama-3-8b-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [llama-3-8b-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q3_K.gguf) | Q3_K | 3.74GB |
| [llama-3-8b-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [llama-3-8b-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [llama-3-8b-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [llama-3-8b-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q4_0.gguf) | Q4_0 | 4.34GB |
| [llama-3-8b-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [llama-3-8b-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [llama-3-8b-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q4_K.gguf) | Q4_K | 4.58GB |
| [llama-3-8b-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [llama-3-8b-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q4_1.gguf) | Q4_1 | 4.78GB |
| [llama-3-8b-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q5_0.gguf) | Q5_0 | 5.21GB |
| [llama-3-8b-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [llama-3-8b-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q5_K.gguf) | Q5_K | 5.34GB |
| [llama-3-8b-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [llama-3-8b-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q5_1.gguf) | Q5_1 | 5.65GB |
| [llama-3-8b-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/unsloth_-_llama-3-8b-Instruct-gguf/blob/main/llama-3-8b-Instruct.Q6_K.gguf) | Q6_K | 6.14GB |




Original model description:
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- unsloth
- transformers
- llama
- llama-3
---

# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!

Directly quantized 4bit model with `bitsandbytes`.

We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

## ✨ Finetune for Free

All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

| Unsloth supports          |    Free Notebooks                                                                                           | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3 8b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing)               | 2.4x faster | 58% less |
| **Gemma 7b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing)               | 2.4x faster | 58% less |
| **Mistral 7b**    | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)               | 2.2x faster | 62% less |
| **Llama-2 7b**      | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)               | 2.2x faster | 43% less |
| **TinyLlama**  | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)              | 3.9x faster | 74% less |
| **CodeLlama 34b** A100   | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)              | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4  | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr**     | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)               | 1.9x faster | 19% less |

- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.