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Qwen2.5-72B-Instruct-AutoRound-GPTQ-2bit

Text Generation

text-generation-inference

Inference Endpoints

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bnjmnmarie commited on 4 days ago

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 This is [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main) (symmetric quantization) and serialized with the GPTQ format in 2-bit. The model has been created, tested, and evaluated by The Kaitchup.
 Details on the quantization process and how to use the model here:
-[The Best Quantization Methods to Run Llama 3.1 on Your GPU](https://newsletter.kaitchup.com/p/the-best-quantization-methods-to)
 It is possible to fine-tune an adapter on top of it following the QLoRA methodology. More about this here:
 [QLoRA with AutoRound: Cheaper and Better LLM Fine-tuning on Your GPU](https://newsletter.kaitchup.com/p/qlora-with-autoround-cheaper-and)

 This is [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main) (symmetric quantization) and serialized with the GPTQ format in 2-bit. The model has been created, tested, and evaluated by The Kaitchup.
 Details on the quantization process and how to use the model here:
+[The Recipe for Extremely Accurate and Cheap Quantization of 70B+ LLMs](https://kaitchup.substack.com/p/the-recipe-for-extremely-accurate-quantization)
 It is possible to fine-tune an adapter on top of it following the QLoRA methodology. More about this here:
 [QLoRA with AutoRound: Cheaper and Better LLM Fine-tuning on Your GPU](https://newsletter.kaitchup.com/p/qlora-with-autoround-cheaper-and)