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rayonlabs/
gemma-2-9b-it-databricks-dolly-15k-curated-en-cc7278c9-d348-4b16-9f13-824135b59812

Name: gemma-2-9b-it-databricks-dolly-15k-curated-en-cc7278c9-d348-4b16-9f13-824135b59812
Availability: InStock

text-generation-inference

endpoints_compatible

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Reminder to use the dev version Transformers:

pip install git+https://github.com/huggingface/transformers.git

Finetune Gemma, Llama 3, Mistral 2-5x faster with 70% less memory via Unsloth!

Directly quantized 4bit model with bitsandbytes.

We have a Google Colab Tesla T4 notebook for Gemma 2 (9B) here: https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?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
Llama 3 (8B)	▶️ Start on Colab	2.4x faster	58% less
Gemma 2 (9B)	▶️ Start on Colab	2x faster	63% less
Mistral (9B)	▶️ Start on Colab	2.2x faster	62% less
Phi 3 (mini)	▶️ Start on Colab	2x faster	63% 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.

rayonlabs/gemma-2-9b-it-databricks-dolly-15k-curated-en-cc7278c9-d348-4b16-9f13-824135b59812

Reminder to use the dev version Transformers:

Finetune Gemma, Llama 3, Mistral 2-5x faster with 70% less memory via Unsloth!

✨ Finetune for Free

rayonlabs/
gemma-2-9b-it-databricks-dolly-15k-curated-en-cc7278c9-d348-4b16-9f13-824135b59812