Fix transformers 5.14+ compatibility and remove dead import

convert.py

@@ -8,7 +8,6 @@
     (".q.", ".query_proj."),
     (".v.", ".value_proj."),
     ("shared.", "wte."),
-    ("lm_head.", "lm_head.linear."),
     (".layer.0.layer_norm.", ".ln1."),
     (".layer.1.layer_norm.", ".ln2."),
     (".layer.2.layer_norm.", ".ln3."),

mlx_bitnet.py

@@ -846,6 +846,7 @@ def sanitize_config(_config: BitnetConfig) -> MinimalBitnetConfig:
         intermediate_size=_config.intermediate_size,
         max_position_embeddings=_config.max_position_embeddings,
         num_attention_heads=_config.num_attention_heads,
+        num_hidden_layers=_config.num_hidden_layers,
         num_key_value_heads=_config.num_key_value_heads,
         pad_token_id=_config.pad_token_id,
         rms_norm_eps=_config.rms_norm_eps,

run_mlx.py

@@ -0,0 +1,31 @@
+import argparse
+import mlx.core as mx
+from mlx_bitnet import load_causal_model
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model", default="1bitLLM/bitnet_b1_58-xl", type=str)
+parser.add_argument("--prompt", default="The meaning of life is", type=str)
+parser.add_argument("--max_tokens", default=50, type=int)
+parser.add_argument("--temp", default=0.0, type=float)
+args = parser.parse_args()
+
+print(f"Loading model {args.model} ...")
+model, tokenizer = load_causal_model(args.model)
+
+tokens = tokenizer.encode(args.prompt)
+input_ids = mx.array([tokens])
+attention_mask = mx.ones_like(input_ids)
+
+generated = list(tokens)
+decoded_so_far = tokenizer.decode(tokens)
+print(args.prompt, end="", flush=True)
+
+for token in model.generate(input_ids, attention_mask, temp=args.temp):
+    generated.append(token.item())
+    new_text = tokenizer.decode(generated)
+    print(new_text[len(decoded_so_far):], end="", flush=True)
+    decoded_so_far = new_text
+    if len(generated) - len(tokens) >= args.max_tokens:
+        break
+
+print()

test_interop.py

@@ -26,7 +26,6 @@
 from torch_bitnet import BitnetForCausalLM as TorchBitnetForCausalLM
 from torch_bitnet import BitnetDecoderLayer as TorchBitnetDecoderLayer
 from transformers.activations import silu as torch_silu
-from training.bit_linear import weight_quant as bit_linear_weight_quant
 
 class TestBitLinearInterop(unittest.TestCase):
     def setUp(self):

torch_bitnet.py

@@ -585,7 +585,7 @@ def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
 
 
 class BitnetForCausalLM(BitnetPreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
 
     def __init__(self, config):
         super().__init__(config)