Added support for Multimodal eval

install/install_requirements.sh

@@ -136,5 +136,5 @@ if [[ -x "$(command -v nvidia-smi)" ]]; then
 fi
 (
   set -x
-  $PIP_EXECUTABLE install evaluate=="0.4.3" lm-eval=="0.4.2" psutil=="6.0.0"
+  $PIP_EXECUTABLE install evaluate=="0.4.3" lm-eval=="0.4.7" psutil=="6.0.0"
 )

install/requirements.txt

@@ -34,4 +34,4 @@ streamlit
 flask
 
 # eval
-lm_eval==0.4.2
+lm_eval==0.4.7

torchchat/cli/builder.py

@@ -794,4 +794,4 @@ def tokenizer_setting_to_name(tiktoken: bool, tokenizers: bool) -> str:
         return "TikToken"
     if tokenizers:
         return "Tokenizers"
-    return "SentencePiece"
+    return "SentencePiece"

torchchat/cli/cli.py

@@ -432,6 +432,14 @@ def _add_evaluation_args(parser) -> None:
         help="Maximum length sequence to evaluate",
     )
 
+    eval_parser.add_argument(
+        "--modality",
+        type=str,
+        default="text",
+        choices=["text", "text-image"],
+        help="Modality of the model. Options: text, text-image",
+    )
+
 
 # Add CLI Args related to distributed inference
 # This feature is currently a [WIP] and hidden from --help

torchchat/model.py

@@ -608,6 +608,12 @@ def setup_caches(self, batch_size, dtype, encoder_max_seq_len, decoder_max_seq_l
             decoder_max_seq_len=decoder_max_seq_len,
         )
 
+    def caches_are_setup(self) -> bool:
+        return self.model.caches_are_setup()
+
+    def caches_are_enabled(self) -> bool:
+        return self.model.caches_are_enabled()
+
     def reset_caches(self):
         self.model.reset_caches()