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--- |
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library_name: transformers |
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license: other |
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license_name: qwen |
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license_link: https://huggingface.co./Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE |
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base_model: Qwen/Qwen2.5-72B |
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datasets: |
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- anthracite-org/kalo-opus-instruct-22k-no-refusal |
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- Nopm/Opus_WritingStruct |
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned |
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- Gryphe/Sonnet3.5-Charcard-Roleplay |
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- Gryphe/ChatGPT-4o-Writing-Prompts |
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned |
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned |
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- nothingiisreal/Reddit-Dirty-And-WritingPrompts |
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- allura-org/Celeste-1.x-data-mixture |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: EVA-Qwen2.5-72B-SFFT-v0.0 |
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results: [] |
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--- |
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Quantized model => https://huggingface.co./EVA-UNIT-01/EVA-Qwen2.5-72B-v0.0 |
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**Quantization Details:** |
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Quantization is done using turboderp's ExLlamaV2 v0.2.3. |
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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. |
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