license: apache-2.0
base_model:
- Qwen/Qwen3-Coder-30B-A3B-Instruct
Model Details
This model is an int4 model with group_size 128 and symmetric quantization of Qwen/Qwen3-Coder-30B-A3B-Instruct generated by intel/auto-round algorithm. Please follow the license of the original model.
How To Use
vLLM usage
vllm serve Intel/Qwen3-Coder-30B-A3B-Instruct-int4-AutoRound --tensor-parallel-size 4 --max-model-len 65536
INT4 Inference on CPU/Intel GPU/CUDA
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Intel/Qwen3-Coder-30B-A3B-Instruct-int4-AutoRound"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Write a quick sort algorithm."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=65536
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
"""
content: Here's a quicksort algorithm implementation in Python:
```python
def quicksort(arr):
'''
Sorts an array using the quicksort algorithm.
Args:
arr: List of comparable elements
Returns:
None (sorts in-place)
'''
if len(arr) <= 1:
return
def partition(low, high):
'''Partition function using the last element as pivot'''
pivot = arr[high]
i = low - 1 # Index of smaller element
for j in range(low, high):
if arr[j] <= pivot:
i += 1
arr[i], arr[j] = arr[j], arr[i] # Swap elements
arr[i + 1], arr[high] = arr[high], arr[i + 1] # Place pivot in correct position
return i + 1
def quicksort_helper(low, high):
'''Recursive helper function'''
if low < high:
# Partition the array and get pivot index
pi = partition(low, high)
# Recursively sort elements before and after partition
quicksort_helper(low, pi - 1)
quicksort_helper(pi + 1, high)
quicksort_helper(0, len(arr) - 1)
# Example usage:
if __name__ == "__main__":
# Test the algorithm
test_array = [64, 34, 25, 12, 22, 11, 90]
print("Original array:", test_array)
quicksort(test_array)
print("Sorted array:", test_array)
# Test with other examples
test_cases = [
[5, 2, 8, 1, 9],
[1],
[],
[3, 3, 3, 3],
[5, 4, 3, 2, 1]
]
for i, case in enumerate(test_cases):
original = case.copy()
quicksort(case)
print(f"Test {i+1}: {original} → {case}")
**How it works:**
1. **Divide**: Choose a "pivot" element and partition the array so that elements smaller than the pivot are on the left, and larger elements are on the right.
2. **Conquer**: Recursively apply quicksort to the sub-arrays on both sides of the pivot.
3. **Combine**: Since we're sorting in-place, no additional combining step is needed.
**Key features:**
- **Time Complexity**: O(n log n) average case, O(n²) worst case
- **Space Complexity**: O(log n) due to recursion stack
- **In-place sorting**: Modifies the original array
- **Not stable**: Relative order of equal elements may change
**Alternative version with random pivot selection** (better average performance):
```python
import random
def quicksort_random(arr):
'''Quicksort with random pivot selection for better average performance'''
def partition(low, high):
# Randomly select pivot and swap with last element
random_index = random.randint(low, high)
arr[random_index], arr[high] = arr[high], arr[random_index]
pivot = arr[high]
i = low - 1
for j in range(low, high):
if arr[j] <= pivot:
i += 1
arr[i], arr[j] = arr[j], arr[i]
arr[i + 1], arr[high] = arr[high], arr[i + 1]
return i + 1
def quicksort_helper(low, high):
if low < high:
pi = partition(low, high)
quicksort_helper(low, pi - 1)
quicksort_helper(pi + 1, high)
if len(arr) > 1:
quicksort_helper(0, len(arr) - 1)
The algorithm efficiently sorts arrays by repeatedly dividing them into smaller subproblems, making it one of the most widely used sorting algorithms in practice.
"""
Generate the model
Here is the sample command to reproduce the model
auto-round --model Qwen/Qwen3-Coder-30B-A3B-Instruct --output_dir "./tmp_autoround" --enable_torch_compile --nsamples 512 --fp_layers mlp.gate
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} }