Support Hugging Face models (#215)

* support hf direct models

llms/deepseek-coder/convert.py

@@ -44,7 +44,9 @@ def convert(args):
     config = model.config.to_dict()
 
     state_dict = model.state_dict()
-    tokenizer = AutoTokenizer.from_pretrained(str(hf_path), trust_remote_code=True, use_fast=False)
+    tokenizer = AutoTokenizer.from_pretrained(
+        str(hf_path), trust_remote_code=True, use_fast=False
+    )
 
     # things to change
     # 1. there's no "model." in the weight names
@@ -84,7 +86,9 @@ def convert(args):
 
     weights = {k: v.numpy() for k, v in state_dict.items()}
 
-    config["rope_scaling_factor"] = config["rope_scaling"]["factor"] if config["rope_scaling"] is not None else 1.0
+    config["rope_scaling_factor"] = (
+        config["rope_scaling"]["factor"] if config["rope_scaling"] is not None else 1.0
+    )
     keep_keys = set(
         [
             "vocab_size",
@@ -96,7 +100,7 @@ def convert(args):
             "rms_norm_eps",
             "intermediate_size",
             "rope_scaling_factor",
-            "rope_theta"
+            "rope_theta",
         ]
     )
     for k in list(config.keys()):

llms/deepseek-coder/deepseek_coder.py

@@ -285,7 +285,11 @@ if __name__ == "__main__":
 
     model, tokenizer = load_model(args.model_path)
 
-    prompt = tokenizer(args.prompt, return_tensors="np", return_attention_mask=False,)[
+    prompt = tokenizer(
+        args.prompt,
+        return_tensors="np",
+        return_attention_mask=False,
+    )[
         "input_ids"
     ][0]