---
license: llama3.1
language:
- en
inference: false
fine-tuning: false
tags:
- nvidia
- llama3.1
datasets:
- nvidia/HelpSteer2
base_model: meta-llama/Llama-3.1-70B-Instruct
pipeline_tag: text-generation
library_name: transformers
---

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.



---

**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.