Update README.md

README.md

@@ -45,4 +45,96 @@ If you are running `vllm > 0.15.0`, you will likely have the bug fixes already a
 | Toxic Chat                | 0.433        | 0.436        | 100.69        | 0.519        | 0.525        | 101.16            |
 | ToxiGen                   | 0.46         | 0.465        | 101.09        | 0.315        | 0.32         | 101.59            |
 | XSTest                    | 0.834        | 0.836        | 100.24        | 0.78         | 0.78         | 100               |
-| Average Score             | 0.6711282051 | 0.6687692308 | 99.42051282   | 0.5706410256 | 0.5682051282 | 99.30282051       |
+| Average Score             | 0.6711282051 | 0.6687692308 | 99.42051282   | 0.5706410256 | 0.5682051282 | 99.30282051       |
+
+
+## Model creation
+
+This model is created with `compressed-tensors==0.13.0` and `llmcompressor==0.9.0.1`, and the following LLM-Compressor quantization script:
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python quantize.py --model_path meta-llama/Llama-Guard-4-12B --quant_path RedHatAI/Llama-Guard-4-12B-quantized.w8a8 --calib_size 512 --dampening_frac 0.03 --pipeline independent
+```
+
+```python
+from datasets import load_dataset
+from transformers import AutoProcessor, Llama4ForConditionalGeneration
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor import oneshot
+import argparse
+from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_path', type=str, required=True)
+parser.add_argument('--quant_path', type=str, required=True)
+parser.add_argument('--calib_size', type=int, required=True)
+parser.add_argument('--dampening_frac', type=float, required=True)
+parser.add_argument('--pipeline', type=str, default="basic") #['basic', 'datafree', 'sequential', independent]
+
+args = parser.parse_args()
+print(f"[DEBUGGING ARGS] {args}")
+
+model = Llama4ForConditionalGeneration.from_pretrained(
+    args.model_path,
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+processor = AutoProcessor.from_pretrained(args.model_path, trust_remote_code=True)
+
+def preprocess_fn(example):
+    # prepare for multimodal processor
+    for msg in example["messages"]:
+        msg["content"] = [{'type': 'text', 'text': msg['content']}]
+
+    return {"text": processor.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
+
+ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
+ds = ds.map(preprocess_fn)
+
+print(f"================================================================================")
+print(f"[For debugging] Calibration data sample is:\n{repr(ds[0]['text'])}")
+print(f"================================================================================")
+
+quant_scheme = QuantizationScheme(
+    targets=["Linear"],
+    weights=QuantizationArgs(
+        num_bits=8,
+        type=QuantizationType.INT,
+        symmetric=True,
+        strategy=QuantizationStrategy.CHANNEL,
+    ),
+    input_activations=QuantizationArgs(
+        dynamic=True,
+        num_bits=8,
+        strategy=QuantizationStrategy.TOKEN,
+        symmetric=True,
+        type=QuantizationType.INT,
+    ),
+    output_activations=None,
+)
+
+recipe = [
+    GPTQModifier(
+        targets=["Linear"],
+        ignore=[
+            "re:.*lm_head",
+            "re:.*multi_modal_projector",
+            "re:.*vision_model",
+        ],
+        dampening_frac=args.dampening_frac,
+        config_groups={"group_0": quant_scheme},
+    )
+]
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    num_calibration_samples=args.calib_size,
+    max_seq_length=2048,
+    pipeline=args.pipeline,
+)
+
+SAVE_DIR = args.quant_path
+model.save_pretrained(SAVE_DIR)
+```