Allow setting seq_len/size/dim for gated models

app.py

@@ -143,6 +143,10 @@ def get_dim_seq_size(model):
         if not dim:
             dim = config.get("hidden_dim", config.get("hidden_size", config.get("d_model", "")))
         seq = config.get("n_positions", config.get("max_position_embeddings", config.get("n_ctx", config.get("seq_length", ""))))
+    
+    if dim == "" or seq == "":
+        raise Exception(f"Could not find dim or seq for model {model.modelId}")
+    
     # Get model file size without downloading. Parameters in million parameters and memory in GB
     parameters, memory = get_model_parameters_memory(model)
     return dim, seq, parameters, memory
@@ -244,13 +248,19 @@ def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_
         # Model & at least one result
         if len(out) > 1:
             if add_emb_dim:
+                # The except clause triggers on gated repos, we can use external metadata for those
                 try:
-                    # Fails on gated repos, so we only include scores for them
                     if "dim_seq_size" not in MODEL_INFOS[model.modelId] or refresh:
                         MODEL_INFOS[model.modelId]["dim_seq_size"] = list(get_dim_seq_size(model))
-                    out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (Million Parameters)"], out["Memory Usage (GB, fp32)"] = tuple(MODEL_INFOS[model.modelId]["dim_seq_size"])
                 except:
-                    MODEL_INFOS[model.modelId]["dim_seq_size"] = "", "", "", ""
+                    name_without_org = model.modelId.split("/")[-1]
+                    MODEL_INFOS[model.modelId]["dim_seq_size"] = (
+                        EXTERNAL_MODEL_TO_DIM.get(name_without_org, ""),
+                        EXTERNAL_MODEL_TO_SEQLEN.get(name_without_org, ""),
+                        EXTERNAL_MODEL_TO_SIZE.get(name_without_org, ""),
+                        round(EXTERNAL_MODEL_TO_SIZE.get(name_without_org, "") * 1e6 * 4 / 1024**3, 2),
+                    )
+                out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (Million Parameters)"], out["Memory Usage (GB, fp32)"] = tuple(MODEL_INFOS[model.modelId]["dim_seq_size"])
             df_list.append(out)
         if model.library_name == "sentence-transformers" or "sentence-transformers" in model.tags or "modules.json" in {file.rfilename for file in model.siblings}:
             SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"])

model_meta.yaml

@@ -1211,6 +1211,22 @@ model_meta:
     is_external: true
     is_proprietary: false
     is_sentence_transformers_compatible: true
+  NV-Embed-v1:
+    link: https://huggingface.co/nvidia/NV-Embed-v1
+    seq_len: 32768
+    size: 7851
+    dim: 4096
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
+  Linq-Embed-Mistral:
+    link: https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral
+    seq_len: 32768
+    size: 7111
+    dim: 4096
+    is_external: false
+    is_proprietary: false
+    is_sentence_transformers_compatible: false
 models_to_skip:
 - michaelfeil/ct2fast-e5-large-v2
 - McGill-NLP/LLM2Vec-Sheared-LLaMA-mntp-unsup-simcse

utils/model_size.py

@@ -40,4 +40,4 @@ def get_model_parameters_memory(model_info: ModelInfo):
         if ("metadata" in size) and ("total_size" in size["metadata"]):
             return round(size["metadata"]["total_size"] / bytes_per_param / 1e6), round(size["metadata"]["total_size"] / 1024**3, 2)
 
-    return None, None
+    raise Exception(f"Could not find the model parameters for {model_info.id}")