Instructions to use inferencerlabs/gemma-4-12B-Q9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/gemma-4-12B-Q9 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir gemma-4-12B-Q9 inferencerlabs/gemma-4-12B-Q9
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Gemma-4-12B
See gemma-4-12B in action: demonstration video
Tested on a M3 Ultra 512GB RAM using Inferencer app
- Text inference: ~ tokens/s @ 1000 tokens ~x GiB
- Multimodal inference: ~ tokens/s ~x GiB
9bpw quant typically achieves near lossless accuracy
| Quantization (bpw) | Perplexity | Token Accuracy | Missed Divergence |
|---|---|---|---|
| q4.5 | 1.32812 | 90.5% | 26.44% |
| q5.5 | 1.23437 | 95.4% | 16.03% |
| q6.5 | 1.21875 | 96.85% | 12.55% |
| q8.5 | 1.21875 | 97.65% | 9.92% |
| q9 | 1.21093 | 97.95% | 9.61% |
| Base | 1.20312 | 100.0% | 0.000% |
- Perplexity: Measures the confidence for predicting base tokens (lower is better)
- Token Accuracy: The percentage of correctly generated base tokens
- Missed Divergence: Measures severity of misses; how much the token was missed by
Quantized with a modified version of MLX
For more details see demonstration video or visit google/gemma-4-12B-it.
Disclaimer
We are not the creator, originator, or owner of any model listed. Each model is created and provided by third parties. Models may not always be accurate or contextually appropriate. You are responsible for verifying the information before making important decisions. We are not liable for any damages, losses, or issues arising from its use, including data loss or inaccuracies in AI-generated content.
- Downloads last month
- 57
Hardware compatibility
Log In to add your hardware
Quantized
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support