Quantization made by Richard Erkhov.

gemma-2b - GGUF

Model creator: https://huggingface.co/unsloth/
Original model: https://huggingface.co/unsloth/gemma-2b/

Name	Quant method	Size
gemma-2b.Q2_K.gguf	Q2_K	1.08GB
gemma-2b.IQ3_XS.gguf	IQ3_XS	1.16GB
gemma-2b.IQ3_S.gguf	IQ3_S	1.2GB
gemma-2b.Q3_K_S.gguf	Q3_K_S	1.2GB
gemma-2b.IQ3_M.gguf	IQ3_M	1.22GB
gemma-2b.Q3_K.gguf	Q3_K	1.29GB
gemma-2b.Q3_K_M.gguf	Q3_K_M	1.29GB
gemma-2b.Q3_K_L.gguf	Q3_K_L	1.36GB
gemma-2b.IQ4_XS.gguf	IQ4_XS	1.4GB
gemma-2b.Q4_0.gguf	Q4_0	1.44GB
gemma-2b.IQ4_NL.gguf	IQ4_NL	1.45GB
gemma-2b.Q4_K_S.gguf	Q4_K_S	1.45GB
gemma-2b.Q4_K.gguf	Q4_K	1.52GB
gemma-2b.Q4_K_M.gguf	Q4_K_M	1.52GB
gemma-2b.Q4_1.gguf	Q4_1	1.56GB
gemma-2b.Q5_0.gguf	Q5_0	1.68GB
gemma-2b.Q5_K_S.gguf	Q5_K_S	1.68GB
gemma-2b.Q5_K.gguf	Q5_K	1.71GB
gemma-2b.Q5_K_M.gguf	Q5_K_M	1.71GB
gemma-2b.Q5_1.gguf	Q5_1	1.79GB
gemma-2b.Q6_K.gguf	Q6_K	1.92GB
gemma-2b.Q8_0.gguf	Q8_0	2.49GB

Original model description:

language: - en license: apache-2.0 library_name: transformers tags: - unsloth - transformers - gemma - gemma-2b

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