diff --git a/torchchat/cli/convert_hf_checkpoint.py b/torchchat/cli/convert_hf_checkpoint.py
index f428e4cc6..122ab0f28 100644
--- a/torchchat/cli/convert_hf_checkpoint.py
+++ b/torchchat/cli/convert_hf_checkpoint.py
@@ -39,19 +39,14 @@ def convert_hf_checkpoint(
     config = TransformerArgs.from_params(config_args)
     print(f"Model config {config.__dict__}")
 
-    # Load the json file containing weight mapping
+    # Find all candidate weight mapping index files
     model_map_json_matches = [Path(m) for m in glob.glob(str(model_dir / "*.index.json"))]
-    assert len(model_map_json_matches) <= 1, "Found multiple weight mapping files"
-    if len(model_map_json_matches):
-        model_map_json = model_map_json_matches[0]
-    else:
-        model_map_json = model_dir / "pytorch_model.bin.index.json"
 
     # If there is no weight mapping, check for a consolidated model and
     # tokenizer we can move. Llama 2 and Mistral have weight mappings, while
     # Llama 3 has a consolidated model and tokenizer.
     # Otherwise raise an error.
-    if not model_map_json.is_file():
+    if not model_map_json_matches:
         consolidated_pth = model_dir / "original" / "consolidated.00.pth"
         tokenizer_pth = model_dir / "original" / "tokenizer.model"
         if consolidated_pth.is_file() and tokenizer_pth.is_file():
@@ -68,11 +63,30 @@ def convert_hf_checkpoint(
             return
         else:
             raise RuntimeError(
-                f"Could not find {model_map_json} or {consolidated_pth} plus {tokenizer_pth}"
+                f"Could not find a valid model weight map or {consolidated_pth} plus {tokenizer_pth}"
             )
 
-    with open(model_map_json) as json_map:
-        bin_index = json.load(json_map)
+    # Load the json file(s) containing weight mapping
+    #
+    # NOTE: If there are multiple index files, there are two possibilities:
+    #   1. The files could be mapped to different weight format files (e.g. .bin
+    #       vs .safetensors)
+    #   2. The files could be split subsets of the mappings that need to be
+    #       merged
+    #
+    # In either case, we can simply keep the mappings where the target file is
+    # valid in the model dir.
+    bin_index = {}
+    for weight_map_file in model_map_json_matches:
+        with open(weight_map_file, "r") as handle:
+            weight_map = json.load(handle)
+        valid_mappings = {
+            k: model_dir / v
+            for (k, v) in weight_map.get("weight_map", {}).items()
+            if (model_dir / v).is_file()
+        }
+        bin_index.update(valid_mappings)
+    bin_files = set(bin_index.values())
 
     weight_map = {
         "model.embed_tokens.weight": "tok_embeddings.weight",
@@ -96,7 +110,6 @@ def convert_hf_checkpoint(
         "model.norm.weight": "norm.weight",
         "lm_head.weight": "output.weight",
     }
-    bin_files = {model_dir / bin for bin in bin_index["weight_map"].values()}
 
     def permute(w, n_heads):
         return (
diff --git a/torchchat/cli/download.py b/torchchat/cli/download.py
index f334eb555..4da2bc390 100644
--- a/torchchat/cli/download.py
+++ b/torchchat/cli/download.py
@@ -35,11 +35,12 @@ def _download_hf_snapshot(
         model_info = model_info(model_config.distribution_path, token=hf_token)
         model_fnames = [f.rfilename for f in model_info.siblings]
 
-        # Check the model config for preference between safetensors and pth
+        # Check the model config for preference between safetensors and pth/bin
         has_pth = any(f.endswith(".pth") for f in model_fnames)
+        has_bin = any(f.endswith(".bin") for f in model_fnames)
         has_safetensors = any(f.endswith(".safetensors") for f in model_fnames)
 
-        # If told to prefer safetensors, ignore pth files
+        # If told to prefer safetensors, ignore pth/bin files
         if model_config.prefer_safetensors:
             if not has_safetensors:
                 print(
@@ -47,10 +48,10 @@ def _download_hf_snapshot(
                     file=sys.stderr,
                 )
                 exit(1)
-            ignore_patterns = "*.pth"
+            ignore_patterns = ["*.pth", "*.bin"]
 
         # If the model has both, prefer pth files over safetensors
-        elif has_pth and has_safetensors:
+        elif (has_pth or has_bin) and has_safetensors:
             ignore_patterns = "*safetensors*"
 
         # Otherwise, download everything