Add Qwen3-Coder awq moe example (#1863)

SUMMARY:
Add a `Qwen3-Coder` awq example based on
https://huggingface.co/nm-testing/Qwen3-Coder-30B-A3B-Instruct-W4A16-awq.


TEST PLAN:
Ran script locally and verified resulting model outputs were reasonable.

Prompt: "Write a binary search function"
Output:
```python
def binary_search(arr, target):
    left = 0
    right = len(arr) - 1

    while left <= right:
        mid = (left + right) // 2

        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            left = mid + 1
        else:
            right = mid - 1

    return -1
```

---------

Signed-off-by: Fynn Schmitt-Ulms <[email protected]>

Author: fynnsu

examples/awq/qwen3_coder_moe_example.py

@@ -0,0 +1,81 @@
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.awq import AWQModifier
+from llmcompressor.utils import dispatch_for_generation
+
+MODEL_ID = "Qwen/Qwen3-Coder-30B-A3B-Instruct"
+SAVE_DIR = MODEL_ID.split("/")[-1] + "-W4A16-awq"
+
+# Configure the quantization algorithm to run.
+recipe = [
+    AWQModifier(
+        duo_scaling=False,
+        ignore=["lm_head", "re:.*mlp.gate$", "re:.*mlp.shared_expert_gate$"],
+        scheme="W4A16",
+        targets=["Linear"],
+    ),
+]
+
+# Select calibration dataset.
+DATASET_ID = "codeparrot/self-instruct-starcoder"
+DATASET_SPLIT = "curated"
+
+# Select number of samples. 256 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 256
+MAX_SEQUENCE_LENGTH = 2048
+
+
+def get_calib_dataset(tokenizer):
+    ds = load_dataset(
+        DATASET_ID,
+        split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES*10}]",
+    )
+
+    def preprocess(example):
+        chat_messages = [
+            {"role": "user", "content": example["instruction"].strip()},
+            {"role": "assistant", "content": example["output"].strip()},
+        ]
+        tokenized_messages = tokenizer.apply_chat_template(chat_messages, tokenize=True)
+        return {"input_ids": tokenized_messages}
+
+    ds = (
+        ds.shuffle(seed=42)
+        .map(preprocess, remove_columns=ds.column_names)
+        .select(range(NUM_CALIBRATION_SAMPLES))
+    )
+    return ds
+
+
+if __name__ == "__main__":
+    model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+    ###
+    ### Apply algorithms.
+    ###
+    oneshot(
+        model=model,
+        dataset=get_calib_dataset(tokenizer),
+        recipe=recipe,
+        max_seq_length=MAX_SEQUENCE_LENGTH,
+        num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+        log_dir=None,
+    )
+
+    # Confirm generations of the quantized model look sane.
+    print("========== SAMPLE GENERATION ==============")
+    dispatch_for_generation(model)
+    input_ids = tokenizer(
+        "Write a binary search function", return_tensors="pt"
+    ).input_ids.to(model.device)
+    output = model.generate(input_ids, max_new_tokens=150)
+    print(tokenizer.decode(output[0]))
+    print("==========================================\n\n")
+
+    # Save model to disk
+    model.save_pretrained(SAVE_DIR)
+    tokenizer.save_pretrained(SAVE_DIR)