Create README.md

README.md

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+---
+base_model:
+- openai/gpt-oss-20b
+---
+
+## Model Details
+
+This model is a gguf q4ks format of  [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)  generated by [intel/auto-round](https://github.com/intel/auto-round) algorithm. 
+
+Please follow the license of the original model.
+
+## How To Use
+
+Llamacpp inference
+
+~~~bash
+/llama-cli -hf Intel/gpt-oss-20b-gguf-q4ks-AutoRound
+~~~
+
+~~~bash
+> Write a quick sort algorithm.
+    // put pivot into final place
+    T tmp = a[left]; a[left] = a[j]; a[j] = tmp;
+    return j;
+}
+```
+
+---
+
+## 4. JavaScript (in‑place)
+
+```javascript
+function quickSort(arr, left = 0, right = arr.length - 1) {
+    if (left >= right) return;
+
+    const pivot = arr[left];
+    let i = left + 1, j = right;
+
+    while (true) {
+        while (i <= right && arr[i] < pivot) i++;
+        while (j >= left + 1 && arr[j] > pivot) j--;
+        if (i >= j) break;
+        [arr[i], arr[j]] = [arr[j], arr[i]];
+    }
+    [arr[left], arr[j]] = [arr[j], arr[left]]; // pivot in place
+
+    quickSort(arr, left, j - 1);
+    quickSort(arr, j + 1, right);
+}
+
+// ---- Example ----------------------------------------------------
+let data = [3, 6, 8, 10, 1, 2, 1];
+quickSort(data);
+console.log(data); // [1, 1, 2, 3, 6, 8, 10]
+```
+
+---
+
+### Quick‑Sort Tips
+
+| Problem | Fix |
+|---------|-----|
+| **Worst‑case O(n²)** when the pivot is always the smallest/largest element | Pick the middle element or use median‑of‑three pivot. |
+| **Stack overflow** on very deep recursion | Convert recursion to iteration (explicit stack) or switch to an iterative algorithm. |
+| **Unstable sorting** | If stability matters, use a stable algorithm (e.g., merge‑sort) or add an index to each element and compare that as a tie‑breaker. |
+| **Large duplicates** | Use “Dutch‑Flag” partitioning that groups `< pivot`, `== pivot`, `> pivot`. |
+
+---
+
+### Final Word
+
+Quick‑sort is a classic divide‑and‑conquer algorithm that works well for average‑case sorting.
+The snippets above are short, in‑place, and can be dropped into most code bases.
+
+Happy coding!
+
+~~~
+
+### Generate the model
+
+Here is the sample command to reproduce the model
+
+```bash
+auto_round --format gguf:q4_k_s --iters 0 --nsamples 512 --model openai/gpt-oss-20b --output_dir tmp_autoround
+```
+
+
+
+## Ethical Considerations and Limitations
+
+The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
+
+Therefore, before deploying any applications of the model, developers should perform safety testing.
+
+## Caveats and Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
+
+Here are a couple of useful links to learn more about Intel's AI software:
+
+- Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
+
+## Disclaimer
+
+The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
+
+## Cite
+
+@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
+
+[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)