Intel/GLM-4.6-REAP-218B-A32B-FP8-gguf-q2ks-mixed-AutoRound

Model Details

This model is a mixed gguf q2ks format of cerebras/GLM-4.6-REAP-218B-A32B-FP8 generated by intel/auto-round algorithm. Embedding layer and lm-head layer are fallback to 8 bits and non expert layers are fallback to 4 bits. Please refer to Section Generate the model for more details.

Please follow the license of the original model.

How To Use

Llamacpp inference

/llama-cli -hf Intel/GLM-4.6-REAP-218B-A32B-FP8-gguf-q2ks-mixed-AutoRound

> Write a quick sort algorithm.
<think>The user wants a quick sort algorithm. This is a very common request. I need to provide a correct, clear, and well-explained implementation of Quick Sort. I should probably choose a language like Python or JavaScript, as they are common and easy to read. I'll go with Python as it's very clean for algorithms. The user asked for a "quick sort algorithm" which implies they want the code, maybe a brief explanation, and maybe some edge cases. I should make sure to handle the common pitfalls, like in-place vs. not-in-place.

Here's a plan:
1.  **Introduction:** Briefly explain what Quick Sort is, its core idea (divide and conquer, pivot), and its time complexity.
2.  **Code Implementation:**
    *   I'll write a clean, standard Python function for Quick Sort.
    *   I'll choose the in-place version, as it's memory-efficient (O(1) extra space besides the recursion stack). This is a common interview question and a practical approach.
    *   I'll use a helper function to keep the main function signature clean (e.g., `quicksort(arr, low, high)`).
    *   The implementation should correctly handle the base case (when the sub-array has 1 or 0 elements).
    *   The `partition` function is crucial. It will place the pivot in its correct sorted position and return the pivot index.
3.  **Explanation of the Code:**
    *   I will walk through the code step-by-step.
    *   Explain the `quicksort` function's role (the main recursive function).
    *   Explain the `partition` function's logic (how it rearranges elements around the pivot).
    *   Explain the recursive calls to the left and right sub-arrays.
4.  **Example Walk-through:** I'll trace the algorithm with a small array (e.g., `[3, 6, 8, 2, 5]`) to make it concrete and easy to understand. This is very helpful for learning.
5.  **Complexity Analysis:**
    *   **Time:** Explain the best, average, and worst-case scenarios.
        *   Best/Average: O(n log n)
        *   Worst: O(n^2) (when the pivot is always the smallest or largest element, e.g., in a sorted array).
    *   **Space:** Explain the space complexity, which comes from the recursion stack depth (O(log n) on average, O(n) in the worst case).
6.  **Alternative Implementation (Optional but good):** I could also show a more "Pythonic" but less memory-efficient version that uses list comprehensions. It's often more readable for beginners, so it's a good alternative to present. It's not in-place, so I should mention that.

**Drafting the content:**

**Title: Quick Sort Algorithm**

**Introduction:**
Quick Sort is a highly efficient sorting algorithm that uses a "divide and conquer" strategy. It's a "in-place" algorithm, meaning it typically modifies the input array directly without using significant extra memory.
The core idea is:
1.  **Divide:** Pick an element from the array, called the **pivot**.
2.  **Conquer:** Reorder the array so that all elements with values less than the pivot come before the pivot, while all elements with values greater than the pivot come after it. After this partitioning, the pivot is in its final sorted position.
3.  **Recurse:** Recursively apply the above steps to the sub-array of elements with smaller values and the sub-array of elements with greater values.

**Code Implementation (In-place):**

```python
import random # To avoid worst-case pivot selection

def quicksort(arr, low, high):
    """Sorts an array using the Quick Sort algorithm (in-place)."""
    if low < high:
        # Partition the array and get the pivot index
        pivot_index = partition(arr, low, high)

        # Recursively sort the sub-array to the left of the pivot
        quicksort(arr, low, pivot_index - 1)

        # Recursively sort the sub-array to the right of the pivot
        quicksort(arr, pivot_index + 1, high)

def partition(arr, low, high):
    """
    This function takes the last element as the pivot,
    places the pivot element at its correct position in the
    sorted array, and returns the index of the pivot.
    """
    # To avoid the worst-case O(n^2), we can choose a random pivot
    pivot_index = random.randint(low, high)
    arr[pivot_index], arr[high] = arr[high], arr[pivot_index]
    pivot = arr[high]

    i = low - 1  # Index of the smaller element

    for j in range(low, high):
        # If current element is smaller than or equal to the pivot
        if arr[j] <= pivot:
            # Increment the index of the smaller element
            i += 1
            # Swap arr[i] and arr[j]
            arr[i], arr[j] = arr[j], arr[i]

    # Place the pivot at the correct position
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1

# Helper function to make the initial call cleaner
def sort(arr):
    """A wrapper function to call quicksort."""
    if not arr:
        return arr
    quicksort(arr, 0, len(arr) - 1)
    return arr

# Example usage:
my_array = [3, 6, 8, 2, 5, 12, 10, 1, 0, 9, 4]
print(f"Original array: {my_array}")
sorted_array = sort(my_array.copy()) # Using .copy() to not modify the original for demonstration
print(f"Sorted array: {sorted_array}")
```
*Self-correction:* The `partition` logic can be tricky. The `i` pointer keeps track of the boundary of the "smaller than pivot" section. The `j` pointer iterates through the rest of the sub-array. The swap `arr[i+1], arr[high]` at the end correctly places the pivot. The random pivot selection is a good practice to avoid worst-case scenarios, so I'll include that. I will also create a simple, non-randomized version for clarity, as the random part can be confusing. Let's make two versions: a standard one (which can be worst-case) and a more robust one. No, let's stick to the standard one and explain the pivot choice. It's simpler to understand the core logic.

Let's refine the code to be a more standard textbook example, which is often more what people are looking for.

**Revised Code Implementation (Simpler, more standard):**

```python
def quicksort(arr, low, high):
    """Sorts an array using the Quick Sort algorithm (in-place)."""
    if low < high:
        # Partition the array and get the pivot index
        pivot_index = partition(arr, low, high)

        # Recursively sort the sub-array to the left of the pivot
        quicksort(arr, low, pivot_index - 1)

        # Recursively sort the sub-array to the right of the pivot
        quicksort(arr, pivot_index + 1, high)

def partition(arr, low, high):
    """
    This function chooses the last element as the pivot,
    places the pivot element at its correct position in the
    sorted array, and returns the index of the pivot.
    """
    pivot = arr[high]  # Choosing the last element as the pivot
    i = low - 1        # Index of the smaller element

    for j in range(low, high):
        # If the current element is smaller than or equal to the pivot
        if arr[j] <= pivot:
            # Increment the index of the smaller element
            i += 1
            # Swap arr[i] and arr[j]
            arr[i], arr[j] = arr[j], arr[i]

    # Place the pivot at the correct position, which is i + 1
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1

# A wrapper function for a clean call
def sort(arr):
    if not arr:
        return arr
    quicksort(arr, 0, len(arr) - 1)
    return arr

# Example usage:
my_array = [3, 6, 8, 2, 5, 12, 10, 1, 0, 9, 4]
print(f"Original array: {my_array}")
sorted_array = sort(my_array.copy())
print(f"Sorted array: {sorted_array}")
```
This looks solid and clear.

**Alternative Implementation (Pythonic, Not In-place):**

```python
# A more "Pythonic" but less memory-efficient version.
# It's easier to read for beginners.
def quick_sort_pythonic(arr):
    """A simple, non-in-place Quick Sort implementation."""
    if len(arr) <= 1:
        return arr

    pivot = arr[0]
    left = [x for x in arr[1:] if x <= pivot]
    right = [x for x in arr[1:] if x > pivot]

    return quick_sort_pythonic(left) + [pivot] + quick_sort_pythonic(right)

# Example usage:
my_array = [3, 6, 8, 2, 5, 12, 10, 1, 0, 9, 4]
print(f"Original array: {my_array}")
sorted_array = quick_sort_pythonic(my_array.copy())
print(f"Sorted array: {sorted_array}")
```
This is also a good option to present, as it highlights the trade-off between readability and efficiency.

**Final Plan for the Answer:**
1.  Start with a clear, concise introduction to Quick Sort.
2.  Present the in-place implementation first, as it's the classic and memory-efficient approach. I'll explain the code step-by-step.
3.  Provide a detailed walkthrough of an example (`[3, 6, 1, 2]` or similar) to make the process concrete.
4.  Analyze the time and space complexity, covering all cases (best, average, worst).
5.  Then, offer the alternative Pythonic version and explain its pros and cons (readability vs. memory usage).
6.  Conclude with a comparison or a summary of when to use which version.

This structure covers all bases from a practical developer's point of view and a learning perspective. I will make sure the comments in the code are clear and the explanation is easy to follow. I'll use Python for the code examples.</think>
Of course! Here is a Quick Sort algorithm implementation in Python, along with a detailed explanation of how it works, its complexity, and an alternative, more "Pythonic" version.

### What is Quick Sort?

Quick Sort is a highly efficient sorting algorithm that uses a "divide and conquer" strategy. It's an "in-place" algorithm, meaning it typically modifies the input array directly without using significant extra memory.

The core idea is:
1.  **Divide:** Pick an element from the array, called the **pivot**.
2.  **Conquer:** Reorder the array so that all elements with values less than the pivot come before it, while all elements with values greater come after. The pivot is now in its final sorted position.
3.  **Recurse:** Recursively apply the same logic to the sub-array of smaller elements and the sub-array of greater elements.

---

### 1. In-Place Quick Sort Implementation

This is the standard, memory-efficient version that sorts the array directly. It's a bit more complex to understand than the alternative below, but it's what you'd typically see in interviews and system-level code.

```python
def quicksort(arr, low, high):
    """
    Sorts an array using the Quick Sort algorithm (in-place).
    This function is the recursive driver.
    """
    if low < high:
        # Partition the array and get the pivot's final index
        pivot_index = partition(arr, low, high)

        # Recursively sort the sub-array to the left of the pivot
        quicksort(arr, low, pivot_index - 1)

        # Recursively sort the sub-array to the right of the pivot
        quicksort(arr, pivot_index + 1, high)

def partition(arr, low, high):
    """
    This helper function takes the last element as the pivot,
    places the pivot element at its correct position in the
    sorted array, and returns the index of the pivot.
    """
    pivot = arr[high]  # Choosing the last element as the pivot
    i = low - 1        # Index of the smaller element

    for j in range(low, high):
        # If the current element is smaller than or equal to the pivot
        if arr[j] <= pivot:
            # Increment the index of the smaller element
            i += 1
            # Swap arr[i] and arr[j]
            arr[i], arr[j] = arr[j], arr[i]

    # Place the pivot at the correct position, which is i + 1
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1

# A wrapper function for a clean, top-level call
def sort(arr):
    """Initial call to start the Quick Sort process."""
    if not arr:
        return arr
    # Make a copy to avoid modifying the original list
    arr_copy = list(arr)
    quicksort(arr_copy, 0, len(arr_copy) - 1)
    return arr_copy

# --- Example Usage ---
my_array = [3, 6, 8, 2, 5, 12, 10, 1, 0, 9, 4]
print(f"Original array: {my_array}")
sorted_array = sort(my_array)
print(f"Sorted array:   {sorted_array}")
```

#### How It Works (Walk-through)

Let's trace `arr = [3, 6, 1]`:
1.  **`quicksort(arr, 0, 2)`**: The pivot is `1`.
    *   The `partition` function runs.
    *   `arr` becomes `[1, 3, 6]`.
    *   `pivot_index` is 0.
2.  **`quicksort(arr, 0, -1)`**: This call is for the empty left sub-array. It immediately returns.
3.  **`quicksort(arr, 1, 2)`**: This call is for the right sub-array `[3, 6]`.
    *   The pivot is `6`.
    *   The `partition` function runs on `[3, 6]`.
    *   `arr` becomes `[1, 3, 6]`.
    *   `pivot_index` is 2.
4.  **`quicksort(arr, 1, 1)`**: This call is for the sub-array `[3]`. It immediately returns.
5.  **`quicksort(arr, 3, 2)`**: This call is for an empty sub-array. It returns.

The final result is `[1, 3, 6]`.

---

### 2. Alternative "Pythonic" Implementation

This version is much easier to read and understand, as it leverages Python's list comprehensions. However, it uses more memory because it creates new lists at each step, so it's not an "in-place" sort.

```python
def quick_sort_pythonic(arr):
    """A simple, non-in-place Quick Sort implementation."""
    # Base case: a list of 0 or 1 elements is already sorted
    if len(arr) <= 1:
        return arr

    # Choose the first element as the pivot
    pivot = arr[0]

    # Partition the rest of the list into elements less than or equal to the pivot
    left = [x for x in arr[1:] if x <= pivot]
    # and elements greater than the pivot
    right = [x for x in arr[1:] if x > pivot]

    # Recursively sort the left and right partitions and combine with the pivot
    return quick_sort_pythonic(left) + [pivot] + quick_sort_pythonic(right)

# --- Example Usage ---
my_array = [3, 6, 8, 2, 5, 12, 10, 1, 0, 9, 4]
print(f"Original array: {my_array}")
sorted_array = quick_sort_pythonic(my_array)
print(f"Sorted array:   {sorted_array}")
```

---

### Complexity Analysis

| Aspect               | Analysis                                                                                                                                                                                                      |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Time Complexity**  | **Best/Average Case: O(n log n)**. This occurs when the pivot consistently divides the array into two roughly equal halves.                                                                                     |
|                      | **Worst Case: O(n²)**. This occurs when the pivot is always the smallest or largest element (e.g., sorting an already sorted array). The `partition` function above chooses the last element, which can lead to this case. This can be avoided by choosing a random pivot. |
| **Space Complexity** | **O(log n)** on average for the in-place version due to the recursive call stack. In the worst case, it's **O(n)**. The Pythonic version uses **O(n log n)** space in the average case because it creates new lists. |

### Comparison

| Feature             | In-Place Version                                   | Pythonic Version                                     |
| ------------------- | ---------------------------------------------------- | ----------------------------------------------------- |
| **Memory**          | **O(log n)** (very efficient)                       | **O(n)** (uses more memory)                          |
| **Readability**     | More complex, requires understanding `partition()`. | Very high, follows a simple, direct logic.           |
| **When to Use**     | **Performance-critical applications**, large arrays, or when memory is constrained. | **Small scripts, teaching, learning, or when code clarity is the absolute priority.** |

Generate the model

Requirement

pip install git+https://github.com/intel/auto-round.git

Here is the sample command to reproduce the model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round import AutoRound

model_name = "cerebras/GLM-4.6-REAP-218B-A32B-FP8"

model = AutoModelForCausalLM.from_pretrained(model_name,
                                             device_map="cpu", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
layer_config = {}
for n, m in model.named_modules():
    if n == "lm_head" or isinstance(m,torch.nn.Embedding):
        layer_config[n] = {"bits": 8}
    elif isinstance(m, torch.nn.Linear) and (not "expert" in n or "shared_experts" in n) and n != "lm_head":
        layer_config[n] = {"bits": 4}

autoround = AutoRound(model, tokenizer, iters=0, layer_config=layer_config, nsamples=512)
autoround.quantize_and_save("tmp_autoround", format="gguf:q2_k_s")

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} }

arxiv github