DBMe/Llama-3.1-Nemotron-70B-Instruct-HF-4.6bpw-h6-exl2

Quantized model => https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF

Quantization Details:
Quantization is done using turboderp's ExLlamaV2 v0.2.2.

I use the default calibration datasets and arguments. The repo also includes a "measurement.json" file, which was used during the quantization process.

For models with bits per weight (BPW) over 6.0, I default to quantizing the lm_head layer at 8 bits instead of the standard 6 bits.

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Who are you? What's with these weird BPWs on [insert model here]?
I specialize in optimized EXL2 quantization for models in the 70B to 100B+ range, specifically tailored for 48GB VRAM setups. My rig is built using 2 x 3090s with a Ryzen APU (APU used solely for desktop output—no VRAM wasted on the 3090s). I use TabbyAPI for inference, targeting context sizes between 32K and 64K.

Every model I upload includes a config.yml file with my ideal TabbyAPI settings. If you're using my config, don’t forget to set PYTORCH_CUDA_ALLOC_CONF=backend:cudaMallocAsync to save some VRAM.