| base_model: [] | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| license: other | |
| Quantized model => https://huggingface.co/sophosympatheia/Midnight-Miqu-103B-v1.0 | |
| **Quantization Details:** | |
| Quantization is done using turboderp's ExLlamaV2 v0.2.3. | |
| 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. | |