diff --git a/scripts/hf_eval.py b/scripts/hf_eval.py
index 48fb7fdf0d..ce2a106a37 100644
--- a/scripts/hf_eval.py
+++ b/scripts/hf_eval.py
@@ -45,7 +45,7 @@ def run_evaluation(repo_id, tasks, limit, device, precision, quantization, compi
     model = AutoModelForCausalLM.from_pretrained(repo_id).to(device="cpu", dtype=precision)
     
     if compile:
-        model = torch.compile(model, mode="max-autotune", fullgraph=True)
+        model = torch.compile(model, fullgraph=True)
 
     if quantization == "int8dq":
         change_linear_weights_to_int8_dqtensors(model)
@@ -56,6 +56,13 @@ def run_evaluation(repo_id, tasks, limit, device, precision, quantization, compi
         change_linear_weights_to_int4_woqtensors(model.to(device=device))
     elif quantization == "autoquant":
         model = autoquant(model.to(device=device))
+    elif quantization == "fp8":
+        from float8_experimental.inference import quantize_to_float8, ActivationCasting, QuantConfig, ScalingGranularity
+        model.to(device)
+        quantize_to_float8(model, QuantConfig(ActivationCasting.DYNAMIC), scaling_granularity=ScalingGranularity.TensorWise)
+
+        pass  # no quantization applied, model is already on device and precision dtype.
+
     with torch.no_grad():
         result = evaluate(
             HFLM(
@@ -78,7 +85,7 @@ def run_evaluation(repo_id, tasks, limit, device, precision, quantization, compi
     parser.add_argument('--limit', type=int, default=None, help='Number of eval samples to evaluate')
     parser.add_argument('--precision', type=lambda x: getattr(torch, x.split(".")[-1]), default=torch.bfloat16, help='dtype precision to use')
     parser.add_argument('--device', type=str, default="cuda", help='Device to use for evaluation')
-    parser.add_argument('-q', '--quantization', default = "None", choices=["int8dq", "int8wo", "int4wo","autoquant", "None"], help='Which quantization technique to apply')
+    parser.add_argument('-q', '--quantization', default = "None", choices=["int8dq", "int8wo", "int4wo","autoquant", "fp8", "None"], help='Which quantization technique to apply')
     parser.add_argument('--compile', action='store_true', help='Whether to compile the model.')
     parser.add_argument('--batch_size', type=int, default=1, help='Batch size to use for evaluation, note int8wo and int4wo work best with small batchsizes, int8dq works better with large batchsizes')
     parser.add_argument('--max_length', type=int, default=None, help='Length of text to process at one time')