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--- |
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tags: |
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- chat |
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license: other |
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language: |
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- en |
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- fr |
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- de |
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- es |
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- it |
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- pt |
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- ru |
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- zh |
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- ja |
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pipeline_tag: text-generation |
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license_name: mrl |
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license_link: https://mistral.ai/licenses/MRL-0.1.md |
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base_model: mistralai/Mistral-Large-Instruct-2407 |
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datasets: |
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- Doctor-Shotgun/C2-Stheno |
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- anthracite-org/kalo-opus-instruct-22k-no-refusal |
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- anthracite-org/nopm_claude_writing_fixed |
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library_name: transformers |
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--- |
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Quantized model => https://huggingface.co/anthracite-org/magnum-v2-123b |
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**Quantization Details:** |
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Quantization is done using turboderp's ExLlamaV2 v0.2.2. |
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I use the default calibration datasets and arguments. The repo also includes a "measurement.json" file, which was used during the quantization process. |
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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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--- |
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**Who are you? What's with these weird BPWs on [insert model here]?** |
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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. |
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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. |
