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--- |
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base_model: mistralai/Mistral-Nemo-Instruct-2407 |
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library_name: transformers |
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quantized_by: InferenceIllusionist |
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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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tags: |
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- iMat |
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- gguf |
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- Mistral |
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license: apache-2.0 |
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--- |
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<img src="https://i.imgur.com/P68dXux.png" width="400"/> |
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# Mistral-Nemo-Instruct-12B-iMat-GGUF |
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<b>Important Note: Inferencing is *only* available on this fork of llama.cpp at the moment: https://github.com/iamlemec/llama.cpp/tree/mistral-nemo (All credits to iamlemec for his work on Mistral-Nemo support) |
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Other front-ends like the main branch of llama.cpp, kobold.cpp, and text-generation-web-ui may not work as intended</b> |
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Quantized from Mistral-Nemo-Instruct-2407 fp16 |
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* Weighted quantizations were creating using fp16 GGUF and groups_merged.txt in 92 chunks and n_ctx=512 |
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* Static fp16 will also be included in repo |
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For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747) |
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<i>All quants are verified working prior to uploading to repo for your safety and convenience</i> |
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<b>KL-Divergence Reference Chart</b> |
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(Click on image to view in full size) |
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[<img src="https://i.imgur.com/mV0nYdA.png" width="920"/>](https://i.imgur.com/mV0nYdA.png) |
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<b>Tip:</b> If you are getting a `cudaMalloc failed: out of memory` error, try passing an argument for lower context in llama.cpp, e.g. for 8k: `-c 8192` |
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If you have all ampere generation or newer cards, you can use flash attention like so: `-fa` |
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Provided Flash Attention is enabled you can also use quantized cache to save on VRAM e.g. for 8-bit: `-ctk q8_0 -ctv q8_0` |
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Original model card can be found [here](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) |
