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qnixsynapse/Gemma-V2-9B-Instruct-GGUF
Gemma-V2-9B-Instruct-GGUF is a machine learning model from qnixsynapse. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as gemma.
Using <a href="https://github.com/ggerganov/llama.cpp/"llama.cpp</a PR <a href="https://github.com/ggerganov/llama.cpp/pull/8156"8156</a for quantization.
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.gguf5.5 GB · 100%
From the Hugging Face model README
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> PR <a href="https://github.com/ggerganov/llama.cpp/pull/8156">8156</a> for quantization.
Original model: https://huggingface.co/google/gemma-2-9b-it
First, make sure you have hugginface-cli installed:
pip install -U "huggingface_hub[cli]"
Then, you can target the specific file you want:
huggingface-cli download qnixsynapse/Gemma-V2-9B-Instruct-GGUF --include "<desired model file name>" --local-dir ./
or you can download directly.
The prompt format is same as Gemma v1 however not included with gguf file. This can be edited with gguf script to add a new key chat_template later.
<bos><start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
The model should stop either at <eos> or <end_of_turn>. If it doesn't then stop tokens needs to be added to the gguf metadata.
Currently only two quants are available:
| quant | size |
|---|---|
| Q4_K_S | 5.5GB |
| Q3_K_M | 4.8GB |
If Q4_K_S is causing OOM when offloading all the layers to the GPU, consider decreasing batch size or use Q3_K_M.
Minimum VRAM needed: 8GB