diff --git a/examples/qwq.py b/examples/qwq.py
new file mode 100644
index 0000000000..7c71fec049
--- /dev/null
+++ b/examples/qwq.py
@@ -0,0 +1,117 @@
+import argparse
+import os
+import sys
+
+from transformers import AutoTokenizer
+from pathlib import Path
+from typing import Dict, Union
+
+from extra.models.llama import Transformer, convert_from_huggingface, fix_bf16
+from examples.llama3 import load
+from tinygrad import nn, Tensor
+from tinygrad.helpers import fetch, colored, GlobalCounters, Timing, DEBUG
+from tinygrad.nn.state import load_state_dict, get_parameters
+
+MODELS = {
+  "32B": {
+    "model_params": {"dim": 5120, "n_heads": 40, "n_kv_heads": 8, "n_layers": 64, "norm_eps": 1e-5, "rope_theta": 1000000, "vocab_size": 152064, "hidden_dim": 27648},
+    "total_num_weights": 17,
+    "tokenizer": "Qwen/QwQ-32B-Preview"
+  }
+}
+
+def download_weights(total_num_weights:int) -> Path:
+  model = fetch("https://huggingface.co/Qwen/QwQ-32B-Preview/resolve/main/model.safetensors.index.json?download=true", "model.safetensors.index.json", subdir=(subdir:="qwq_32b_preview"))
+
+  for i in range(1, total_num_weights + 1):
+    filename = f"model-{i:05d}-of-{total_num_weights:05d}.safetensors"
+    fetch(f"https://huggingface.co/Qwen/QwQ-32B-Preview/resolve/main/{filename}?download=true", filename, subdir=subdir)
+
+  return Path(os.path.dirname(model))
+
+def load_model(model_path:Path, model_params:Dict[str, Union[int, float]]) -> Transformer:
+  # build model
+  model = Transformer(**model_params, linear=nn.Linear)
+
+  # update layers to add bias
+  updated_layers = []
+  for layer in model.layers:
+    head_dim = model_params["dim"] // model_params["n_heads"]
+    layer.attention.wq = nn.Linear(model_params["dim"], model_params["n_heads"] * head_dim, bias=True)
+    layer.attention.wk = nn.Linear(model_params["dim"], model_params["n_kv_heads"] * head_dim, bias=True)
+    layer.attention.wv = nn.Linear(model_params["dim"], model_params["n_kv_heads"] * head_dim, bias=True)
+    updated_layers.append(layer)
+  model.layers = updated_layers
+
+  # load weights
+  weights = fix_bf16(convert_from_huggingface(load(str(model_path / "model.safetensors.index.json")), model, model_params["n_heads"], model_params["n_kv_heads"], permute_layers=False))
+
+  # replace weights in model
+  load_state_dict(model, weights, strict=False, consume=True)
+  return model
+
+
+if __name__ == "__main__":
+  Tensor.no_grad = True
+
+  parser = argparse.ArgumentParser(description="Run QwQ in tinygrad", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument("--size", choices=["32B"], default="32B", help="Model size")
+  parser.add_argument("--count", type=int, default=30, help="Max number of tokens to generate")
+  parser.add_argument("--temperature", type=float, default=0.7, help="Temperature in the softmax")
+  parser.add_argument("--prompt", type=str, default="Hello.", help="Phrase to start with")
+  parser.add_argument("--weights", type=str, default=None, help="Path to the downloaded weights")
+  parser.add_argument("--timing", action="store_true", help="Print timing per token")
+  args = parser.parse_args()
+
+  model_info = MODELS[args.size]
+
+  model_path = Path(args.weights) if args.weights else download_weights(model_info["total_num_weights"])
+  transformer = load_model(model_path, model_info["model_params"])
+  tokenizer = AutoTokenizer.from_pretrained(model_info["tokenizer"])
+  param_bytes = sum(x.lazydata.size * x.dtype.itemsize for x in get_parameters(transformer))
+
+  outputted = args.prompt
+  start_pos, toks = 0, tokenizer(outputted)["input_ids"]
+  print(outputted, end="", flush=True)
+
+  tok_tensor = None
+  for i in range(args.count):
+    GlobalCounters.reset()
+
+    if args.timing: print("")
+    st = GlobalCounters.time_sum_s
+    next_tok = Tensor([toks[start_pos:]]) if tok_tensor is None or (len(toks)-start_pos) > 1 else tok_tensor.reshape(1, 1)
+    with Timing("total ", enabled=args.timing, on_exit=lambda x: f", {1e9/x:.2f} tok/s, {GlobalCounters.global_mem/x:.2f} GB/s, param {param_bytes/x:.2f} GB/s"):
+      with Timing("enqueue in ", on_exit=(lambda et: (f", {(GlobalCounters.time_sum_s-st)*1e3:.2f} ms on GPU" if DEBUG>=2 else "") +
+                  f", {GlobalCounters.global_ops*1e-9:.2f} GOPS, {GlobalCounters.global_mem*1e-9:.2f} GB" +
+                  (f", {GlobalCounters.global_mem*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s, param {param_bytes*1e-9/(GlobalCounters.time_sum_s-st):.2f} GB/s" if DEBUG>=2 else "")) if DEBUG else None, enabled=args.timing):
+        tok_tensor = transformer(next_tok, start_pos, args.temperature)
+      tok = tok_tensor.item()
+
+    # use the kv cache
+    start_pos = len(toks)
+
+    # add the new token
+    toks.append(tok)
+
+    cur = tokenizer.decode(toks, skip_special_tokens=True)
+    sys.stdout.write(cur[len(outputted):])
+    sys.stdout.flush()
+    outputted = cur
+
+  if args.temperature == 0:
+    text = tokenizer.decode(toks)
+    key = (args.size, args.count, args.prompt)
+    expected = {
+      ("32B", 10, "Hello."): "Hello. I'm trying to make a program that will read",
+      (
+        "32B",
+        50,
+        "Can you tell me more about machine learning?"
+      ): "Can you tell me more about machine learning? Sure, I'd be happy to help! Machine learning is a subset of artificial intelligence that focuses on building systems that can learn from data and make predictions or decisions without being explicitly programmed to do so. It's a fascinating field with a lot of real"
+    }
+    try:
+      assert text == expected[key], f"invalid output: `{colored(text, 'red')}` != `{expected[key]}`"
+      print("\n" + colored("output validated", "green"))
+    except KeyError:
+      pass
diff --git a/extra/models/llama.py b/extra/models/llama.py
index deaf1ac3ec..cd5ca1f179 100644
--- a/extra/models/llama.py
+++ b/extra/models/llama.py
@@ -181,7 +181,7 @@ class Transformer:
 
 # *** helpers ***
 
-def convert_from_huggingface(weights:Dict[str, Tensor], model: Transformer, n_heads: int, n_kv_heads: int):
+def convert_from_huggingface(weights:Dict[str, Tensor], model: Transformer, n_heads: int, n_kv_heads: int, permute_layers: bool = True):
   def permute(v: Tensor, n_heads: int):
     return v.reshape(n_heads, 2, v.shape[0] // n_heads // 2, v.shape[1]).transpose(1, 2).reshape(*v.shape[:2])
 
@@ -189,6 +189,7 @@ def convert_from_huggingface(weights:Dict[str, Tensor], model: Transformer, n_he
     "model.embed_tokens.weight": "tok_embeddings.weight",
     **{f"model.layers.{l}.input_layernorm.weight": f"layers.{l}.attention_norm.weight" for l in range(len(model.layers))},
     **{f"model.layers.{l}.self_attn.{x}_proj.weight": f"layers.{l}.attention.w{x}.weight" for x in ["q", "k", "v", "o"] for l in range(len(model.layers))},
+    **{f"model.layers.{l}.self_attn.{x}_proj.bias": f"layers.{l}.attention.w{x}.bias" for x in ["q", "k", "v", "o"] for l in range(len(model.layers))},
     **{f"model.layers.{l}.post_attention_layernorm.weight": f"layers.{l}.ffn_norm.weight" for l in range(len(model.layers))},
     **{f"model.layers.{l}.mlp.{x}_proj.weight": f"layers.{l}.feed_forward.w{y}.weight" for x, y in {"gate": "1", "down": "2", "up": "3"}.items() for l in range(len(model.layers))},
     "model.norm.weight": "norm.weight",
@@ -199,9 +200,9 @@ def convert_from_huggingface(weights:Dict[str, Tensor], model: Transformer, n_he
     if ".rotary_emb." in k: continue
     v = v.to(Device.DEFAULT)
     if "model.layers" in k:
-      if "q_proj" in k:
+      if "q_proj" in k and permute_layers:
         v = permute(v, n_heads)
-      elif "k_proj" in k:
+      elif "k_proj" in k and permute_layers:
         v = permute(v, n_kv_heads)
     sd[keymap[k]] = v
   return sd