Quantization made by Richard Erkhov.

llama-3-8b - GGUF

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

Name	Quant method	Size
llama-3-8b.Q2_K.gguf	Q2_K	2.96GB
llama-3-8b.IQ3_XS.gguf	IQ3_XS	3.28GB
llama-3-8b.IQ3_S.gguf	IQ3_S	3.43GB
llama-3-8b.Q3_K_S.gguf	Q3_K_S	3.41GB
llama-3-8b.IQ3_M.gguf	IQ3_M	3.52GB
llama-3-8b.Q3_K.gguf	Q3_K	3.74GB
llama-3-8b.Q3_K_M.gguf	Q3_K_M	3.74GB
llama-3-8b.Q3_K_L.gguf	Q3_K_L	4.03GB
llama-3-8b.IQ4_XS.gguf	IQ4_XS	4.18GB
llama-3-8b.Q4_0.gguf	Q4_0	4.34GB
llama-3-8b.IQ4_NL.gguf	IQ4_NL	4.38GB
llama-3-8b.Q4_K_S.gguf	Q4_K_S	4.37GB
llama-3-8b.Q4_K.gguf	Q4_K	4.58GB
llama-3-8b.Q4_K_M.gguf	Q4_K_M	4.58GB
llama-3-8b.Q4_1.gguf	Q4_1	4.78GB
llama-3-8b.Q5_0.gguf	Q5_0	5.21GB
llama-3-8b.Q5_K_S.gguf	Q5_K_S	5.21GB
llama-3-8b.Q5_K.gguf	Q5_K	5.34GB
llama-3-8b.Q5_K_M.gguf	Q5_K_M	5.34GB
llama-3-8b.Q5_1.gguf	Q5_1	5.65GB
llama-3-8b.Q6_K.gguf	Q6_K	6.14GB

Original model description:

language: - en license: llama2 library_name: transformers tags: - unsloth - transformers - llama - llama-3

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

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

✨ 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	2.4x faster	58% less
Gemma 7b	▶️ Start on Colab	2.4x faster	58% less
Mistral 7b	▶️ Start on Colab	2.2x faster	62% less
Llama-2 7b	▶️ Start on Colab	2.2x faster	43% less
TinyLlama	▶️ Start on Colab	3.9x faster	74% less
CodeLlama 34b A100	▶️ Start on Colab	1.9x faster	27% less
Mistral 7b 1xT4	▶️ Start on Kaggle	5x faster*	62% less
DPO - Zephyr	▶️ Start on Colab	1.9x faster	19% less

This conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.