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RichardErkhov/
unsloth_-_codegemma-7b-it-gguf

Name: unsloth_-_codegemma-7b-it-gguf
Availability: InStock

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codegemma-7b-it - GGUF

Model creator: https://huggingface.co/unsloth/
Original model: https://huggingface.co/unsloth/codegemma-7b-it/

Name	Quant method	Size
codegemma-7b-it.Q2_K.gguf	Q2_K	3.24GB
codegemma-7b-it.IQ3_XS.gguf	IQ3_XS	3.54GB
codegemma-7b-it.IQ3_S.gguf	IQ3_S	3.71GB
codegemma-7b-it.Q3_K_S.gguf	Q3_K_S	3.71GB
codegemma-7b-it.IQ3_M.gguf	IQ3_M	3.82GB
codegemma-7b-it.Q3_K.gguf	Q3_K	4.07GB
codegemma-7b-it.Q3_K_M.gguf	Q3_K_M	4.07GB
codegemma-7b-it.Q3_K_L.gguf	Q3_K_L	4.39GB
codegemma-7b-it.IQ4_XS.gguf	IQ4_XS	4.48GB
codegemma-7b-it.Q4_0.gguf	Q4_0	4.67GB
codegemma-7b-it.IQ4_NL.gguf	IQ4_NL	4.69GB
codegemma-7b-it.Q4_K_S.gguf	Q4_K_S	4.7GB
codegemma-7b-it.Q4_K.gguf	Q4_K	4.96GB
codegemma-7b-it.Q4_K_M.gguf	Q4_K_M	4.96GB
codegemma-7b-it.Q4_1.gguf	Q4_1	5.12GB
codegemma-7b-it.Q5_0.gguf	Q5_0	5.57GB
codegemma-7b-it.Q5_K_S.gguf	Q5_K_S	5.57GB
codegemma-7b-it.Q5_K.gguf	Q5_K	5.72GB
codegemma-7b-it.Q5_K_M.gguf	Q5_K_M	5.72GB
codegemma-7b-it.Q5_1.gguf	Q5_1	6.02GB
codegemma-7b-it.Q6_K.gguf	Q6_K	6.53GB
codegemma-7b-it.Q8_0.gguf	Q8_0	8.45GB

Original model description:

language:

en library_name: transformers license: apache-2.0 tags:
unsloth
transformers
gemma
bnb

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

We have a Google Colab Tesla T4 notebook for CodeGemma 7b here: https://colab.research.google.com/drive/19lwcRk_ZQ_ZtX-qzFP3qZBBHZNcMD1hh?usp=sharing

✨ 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.

RichardErkhov/unsloth_-_codegemma-7b-it-gguf

Original model description:

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

✨ Finetune for Free

RichardErkhov/
unsloth_-_codegemma-7b-it-gguf