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Thanks for the additional quants, [DAN™](https://huggingface.co./dranger003), [Knut Jägersberg](https://huggingface.co./KnutJaegersberg), and [Michael Radermacher](https://huggingface.co./mradermacher)!

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  1. README.md +6 -2
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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6303ca537373aacccd85d8a7/vmCAhJCpF0dITtCVxlYET.jpeg)
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  - HF: [wolfram/miquliz-120b-v2.0](https://huggingface.co/wolfram/miquliz-120b-v2.0)
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- - GGUF: [IQ2_XS | IQ2_XXS | IQ3_XXS](https://huggingface.co/dranger003/miquliz-120b-v2.0-iMat.GGUF) | [Q2_K | IQ3_XXS | Q4_K_M | Q5_K_M](https://huggingface.co/wolfram/miquliz-120b-v2.0-GGUF) | [Q8_0](https://huggingface.co/dranger003/miquliz-120b-v2.0-iMat.GGUF)
 
 
 
 
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  - EXL2: [2.4bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-2.4bpw-h6-exl2) | [2.65bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-2.65bpw-h6-exl2) | [3.0bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-3.0bpw-h6-exl2) | 3.5bpw | [4.0bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-4.0bpw-h6-exl2) | [5.0bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-5.0bpw-h6-exl2)
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  - **Max Context w/ 48 GB VRAM:** (24 GB VRAM is not enough, even for 2.4bpw, use [GGUF](https://huggingface.co/wolfram/miquliz-120b-v2.0-GGUF) instead!)
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  - **2.4bpw:** 32K (32768 tokens) w/ 8-bit cache, 21K (21504 tokens) w/o 8-bit cache
@@ -31,7 +35,7 @@ Inspired by [goliath-120b](https://huggingface.co/alpindale/goliath-120b).
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  Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) – the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.
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- Thanks for the additional quants, [DAN™](https://huggingface.co/dranger003)!
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  Also available: [miqu-1-120b](https://huggingface.co/wolfram/miqu-1-120b) – Miquliz's older, purer sister; only Miqu, inflated to 120B.
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  ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6303ca537373aacccd85d8a7/vmCAhJCpF0dITtCVxlYET.jpeg)
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  - HF: [wolfram/miquliz-120b-v2.0](https://huggingface.co/wolfram/miquliz-120b-v2.0)
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+ - GGUF: [Q2_K | IQ3_XXS | Q4_K_M | Q5_K_M](https://huggingface.co/wolfram/miquliz-120b-v2.0-GGUF)
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+ - [dranger003's IQ2_XS | IQ2_XXS | IQ3_XXS | Q8_0](https://huggingface.co/dranger003/miquliz-120b-v2.0-iMat.GGUF)
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+ - [KnutJaegersberg's IQ2_XS](https://huggingface.co/KnutJaegersberg/2-bit-LLMs)
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+ - [mradermacher's i1-IQ1_S – i1-Q5_K_M](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF)
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+ - [mradermacher's Q2_K – Q8_0](https://huggingface.co/mradermacher/miquliz-120b-v2.0-GGUF)
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  - EXL2: [2.4bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-2.4bpw-h6-exl2) | [2.65bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-2.65bpw-h6-exl2) | [3.0bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-3.0bpw-h6-exl2) | 3.5bpw | [4.0bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-4.0bpw-h6-exl2) | [5.0bpw](https://huggingface.co/wolfram/miquliz-120b-v2.0-5.0bpw-h6-exl2)
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  - **Max Context w/ 48 GB VRAM:** (24 GB VRAM is not enough, even for 2.4bpw, use [GGUF](https://huggingface.co/wolfram/miquliz-120b-v2.0-GGUF) instead!)
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  - **2.4bpw:** 32K (32768 tokens) w/ 8-bit cache, 21K (21504 tokens) w/o 8-bit cache
 
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  Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) – the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.
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+ Thanks for the additional quants, [DAN™](https://huggingface.co/dranger003), [Knut Jägersberg](https://huggingface.co/KnutJaegersberg), and [Michael Radermacher](https://huggingface.co/mradermacher)!
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  Also available: [miqu-1-120b](https://huggingface.co/wolfram/miqu-1-120b) – Miquliz's older, purer sister; only Miqu, inflated to 120B.
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