Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+library_name: transformers
+tags:
+- auto-gptq
+- AutoRound
+license: apache-2.0
+---
+
+
+## Model Details
+
+This is [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main) (asymmetric quantization) and serialized with the GPTQ format in 4-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)
+
+I used these hyperparameters for quantization:
+
+```
+bits, group_size = 4, 128
+
+autoround = AutoRound(model, tokenizer, nsamples=512, iters=1000, low_gpu_mem_usage=False, bits=bits, group_size=group_size)
+
+autoround.quantize()
+output_dir = "./tmp_autoround"
+autoround.save_quantized(output_dir, format='auto_gptq', inplace=True) 
+```
+
+Evaluation results:
+
+![arc_challenge, musr, gpqa, mmlu_pro, mmlu….png](https://cdn-uploads.huggingface.co/production/uploads/64b93e6bd6c468ac7536607e/ExiQHtJf981JcUsHcbZW9.png)
+
+
+- **Developed by:** [The Kaitchup](https://newsletter.kaitchup.com/)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0 license