New model_type: language adapt

src/display/about.py

@@ -63,7 +63,8 @@ Side note on the baseline scores:
 
 ## Icons
 - {ModelType.PT.to_str(" : ")} model: new, base models, trained on a given corpora
-- {ModelType.FT.to_str(" : ")} model: pretrained models finetuned on more data
+- {ModelType.LA.to_str(" : ")} model: pretained models adapted to another language with more further-pretraining or finetuning
+- {ModelType.FT.to_str(" : ")} model: pretrained models finetuned or further-pretrained on more data
 - {ModelType.chat.to_str(" : ")} model: chat like fine-tunes, either using IFT (datasets of task instruction), RLHF or DPO (changing the model loss a bit with an added policy), etc
 - {ModelType.merges.to_str(" : ")} model: merges or MoErges, models which have been merged or fused without additional fine-tuning. 
 If there is no icon, we have not uploaded the information on the model yet, feel free to open an issue with the model information!

src/display/utils.py

@@ -165,7 +165,8 @@ class ModelDetails:
 
 class ModelType(Enum):
     PT = ModelDetails(name="pretrained", symbol="🟢")
-    FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="🔶")
+    LA = ModelDetails(name="language adapted models (FP, FT, ...)", symbol="🆎")
+    FT = ModelDetails(name="fine-tuned/fp on domain-specific datasets", symbol="🔶")
     chat = ModelDetails(name="chat models (RLHF, DPO, IFT, ...)", symbol="💬")
     merges = ModelDetails(name="base merges and moerges", symbol="🤝")
     Unknown = ModelDetails(name="", symbol="?")
@@ -177,6 +178,8 @@ class ModelType(Enum):
     def from_str(type):
         if "fine-tuned" in type or "🔶" in type:
             return ModelType.FT
+        if "language" in type or "🆎" in type:
+            return ModelType.PT
         if "pretrained" in type or "🟢" in type:
             return ModelType.PT
         if any([k in type for k in ["instruction-tuned", "RL-tuned", "chat", "🟦", "⭕", "💬"]]):