Model Details
This model is a gguf q4ks format of openai/gpt-oss-20b generated by intel/auto-round algorithm.
Please follow the license of the original model.
How To Use
Llamacpp inference
llama-cli -hf Intel/gpt-oss-20b-gguf-q4ks-AutoRound
> Write a quick sort algorithm.
<|channel|>analysis<|message|>We need to write quick sort algorithm. The user wants a quick sort algorithm. Provide code? They didn't specify language. I can provide a general quick sort algorithm, maybe in Python, C++, Java, etc. Provide explanation. Provide typical implementation. I'll respond with explanation and code.<|start|>assistant<|channel|>final<|message|>Below is a minimal, self‑contained quick‑sort implementation in a few common languages.
Feel free to copy‑paste the snippet that matches your stack, or use it as a template and adapt it to your needs.
---
## Quick‑Sort – Concept
1. **Choose a pivot** – usually the first element (or median of three, etc.).
2. **Partition** the array into two sub‑arrays:
* elements `< pivot`
* elements `>= pivot` (or `> pivot` if you want to avoid duplicates)
3. **Recursively sort** the two sub‑arrays.
4. **Concatenate** the sorted left part, the pivot, and the sorted right part.
The algorithm is *in‑place* (no extra array for the whole list) and runs in average \(O(n \log n)\), worst‑case \(O(n^2)\) if the pivot is poorly chosen.
---
## 1. Python (in‑place)
```python
def quick_sort(arr, left=0, right=None):
"""
Sorts `arr` in place using the Quick‑Sort algorithm.
"""
if right is None:
right = len(arr) - 1
def _partition(lo, hi):
# choose the first element as pivot
pivot = arr[lo]
i, j = lo + 1, hi
while True:
while i <= hi and arr[i] < pivot:
i += 1
while j >= lo + 1 and arr[j] > pivot:
j -= 1
if i >= j:
break
arr[i], arr[j] = arr[j], arr[i]
# place pivot in its final position
arr[lo], arr[j] = arr[j], arr[lo]
return j
if left < right:
p = _partition(left, right)
quick_sort(arr, left, p - 1)
quick_sort(arr, p + 1, right)
# ---- Example ----------------------------------------------------
data = [3, 6, 8, 10, 1, 2, 1]
quick_sort(data)
print(data) # -> [1, 1, 2, 3, 6, 8, 10]
```
**Notes**
* The helper `_partition` returns the final index of the pivot.
* The algorithm is *stable only if you change the comparison* to `<=` or `>=` depending on the ordering you need.
* For very large lists you may want to switch to a non‑recursive implementation to avoid hitting Python’s recursion depth limit.
---
## 2. C++ (in‑place, STL‑like)
```cpp
#include <vector>
#include <algorithm> // for std::swap
template<typename T>
void quick_sort(std::vector<T>& a, int left = 0, int right = -1)
{
if (right == -1) right = static_cast<int>(a.size()) - 1;
auto partition = [&](int lo, int hi) -> int {
T pivot = a[lo];
int i = lo + 1, j = hi;
while (true) {
while (i <= hi && a[i] < pivot) ++i;
while (j >= lo + 1 && a[j] > pivot) --j;
if (i >= j) break;
std::swap(a[i], a[j]);
}
std::swap(a[lo], a[j]); // pivot in place
return j;
};
if (left < right) {
int p = partition(left, right);
quick_sort(a, left, p - 1);
quick_sort(a, p + 1, right);
}
}
// ---- Example ----------------------------------------------------
int main() {
std::vector<int> v{3, 6, 8, 10, 1, 2, 1};
quick_sort(v);
for (int x : v) std::cout << x << ' ';
// Output: 1 1 2 3 6 8 10
}
```
---
## 3. Java (in‑place)
```java
public static <T extends Comparable<? super T>> void quickSort(T[] a) {
quickSort(a, 0, a.length - 1);
}
private static <T extends Comparable<? super T>> void quickSort(T[] a,
int left, int right) {
if (left >= right) return;
int pivotIdx = partition(a, left, right);
quickSort(a, left, pivotIdx - 1);
quickSort(a, pivotIdx + 1, right);
}
private static <T extends Comparable<? super T>> int partition(T[] a,
int left, int right) {
T pivot = a[left];
int i = left + 1, j = right;
while (true) {
while (i <= right && a[i].compareTo(pivot) < 0) i++;
while (j >= left + 1 && a[j].compareTo(pivot) > 0) j--;
if (i >= j) break;
T tmp = a[i]; a[i] = a[j]; a[j] = tmp;
}
// 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
Requirement
pip install git+https://github.com/intel/auto-round.git@hengguo/gguf_update_0806
Here is the sample command to reproduce the model
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
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} }
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openai/gpt-oss-20b