Instructions to use JayhC/70B-L3.3-Cirrus-x1-4bpw-h6-exl3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JayhC/70B-L3.3-Cirrus-x1-4bpw-h6-exl3 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JayhC/70B-L3.3-Cirrus-x1-4bpw-h6-exl3", dtype="auto") - Notebooks
- Google Colab
- Kaggle
4bpw/h6 exl3 quantization of Sao10K/70B-L3.3-Cirrus-x1
ORIGINAL CARD:
70B-L3.3-Cirrus-x1
- Same data composition as Freya, applied differently, trained longer too.
- Merging with its checkpoints was also involved.
- Has a nice style, with occasional issues that can be easily fixed.
- A more stable version compared to previous runs.
My Model Settings | Feel free to use DRY or XTC or whatever meme samplers. I have zero experience with them, I can't help you there.
Prompt Format: Llama-3-Instruct
Temperature: 1.1
min_p: 0.05
Training time in total was ~22 Hours on a 8xH100 Node.
Then, ~3 Hours spent merging multiple epoch checkpoints through dare_ties and model experimentation on a 2xH200 Node.
Compute spent is on my own budget and wallet, through Runpod or Vast.
Probably my last main release in a while unless things change, spent too much on this.
https://sao10k.carrd.co/ for contact. Ideally on Discord or here on HF.
If you're interested in donations:
My Metamask Wallet: 0xb2f71C762990e1FC3353319fA63f2C65249d9379
Congrats! You win something special below, @Calidras
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Model tree for JayhC/70B-L3.3-Cirrus-x1-4bpw-h6-exl3
Base model
meta-llama/Llama-3.1-70B
