Update README.md

README.md

@@ -44,4 +44,116 @@ Evaluations are obtained with `vllm==0.15.0` and bug fixes from this [PR](https:
 | Toxic Chat                | 0.433        | 0.433                                                                                                           | 100           | 0.519        | 0.508        | 97.88             |
 | ToxiGen                   | 0.46         | 0.444                                                                                                           | 96.52         | 0.315        | 0.3          | 95.24             |
 | XSTest                    | 0.834        | 0.832                                                                                                           | 99.76         | 0.78         | 0.765        | 98.08             |
-| Average Score             | 0.6711282051 | 0.6654871795                                                                                                    | 99.12538462   | 0.5706410256 | 0.5629487179 | 98.45897436       |
+| Average Score             | 0.6711282051 | 0.6654871795                                                                                                    | 99.12538462   | 0.5706410256 | 0.5629487179 | 98.45897436       |
+
+
+## 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.w4a16 --group_size 128 --calib_size 1024 --dampening_frac 0.01 --observer minmax --sym True --actorder False --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
+
+def parse_actorder(value):
+    # Interpret the input value for --actorder
+    if value.lower() == "false":
+        return False
+    elif value.lower() == "group":
+        return "group"
+    elif value.lower() == "weight":
+        return "weight"
+    else:
+        raise argparse.ArgumentTypeError("Invalid value for --actorder. Use 'group', 'weight', or 'False'.")
+
+def parse_sym(value):
+    if value.lower() == "false":
+        return False
+    elif value.lower() == "true":
+        return True
+    else:
+        raise argparse.ArgumentTypeError(f"Invalid value for --sym. Use false or true, but got {value}")
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model_path', type=str, required=True)
+parser.add_argument('--quant_path', type=str, required=True)
+parser.add_argument('--group_size', type=int, required=True)
+parser.add_argument('--calib_size', type=int, required=True)
+parser.add_argument('--dampening_frac', type=float, required=True)
+parser.add_argument('--observer', type=str, required=True) # mse or minmax
+parser.add_argument('--sym', type=parse_sym, required=True) # true or false
+parser.add_argument('--actorder', type=parse_actorder, required=True) # group or weight or false
+parser.add_argument('--pipeline', type=str, default="basic") # ['basic', 'datafree', 'sequential', independent]
+
+args = parser.parse_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=4,
+        type=QuantizationType.INT,
+        symmetric=args.sym,
+        group_size=args.group_size,
+        strategy=QuantizationStrategy.GROUP,
+        observer=args.observer,
+        actorder=args.actorder
+    ),
+    input_activations=None,
+    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=4096,
+    pipeline=args.pipeline,
+)
+
+SAVE_DIR = args.quant_path
+model.save_pretrained(SAVE_DIR)
+print(f"Model saved to {SAVE_DIR}. Please manually copy other files like tokenizer, proprocessors, etc.")
+```
+