Upload embeddinggemma-300m.toml with huggingface_hub

embeddinggemma-300m.toml

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+# EmbeddingGemma 300M LiteRT-LM Configuration
+# Use this with the litertlm_builder_cli to create .litertlm file
+#
+# Usage:
+# cd deps/LiteRT-LM
+# bazel run //schema/py:litertlm_builder_cli -- \
+#   toml --path ../../models/embeddinggemma-300m.toml \
+#   output --path ../../models/embeddinggemma-300m.litertlm
+
+[system_metadata]
+entries = [
+  { key = "model_name", value_type = "String", value = "EmbeddingGemma-300M" },
+  { key = "model_version", value_type = "String", value = "1.0" },
+  { key = "embedding_dim", value_type = "Int32", value = 256 },
+  { key = "author", value_type = "String", value = "Google" }
+]
+
+# Section 1: TFLite Embedder Model
+[[section]]
+section_type = "TFLiteModel"
+model_type = "EMBEDDER"
+# Use the seq512 version (best for tool descriptions)
+data_path = "/home/mac/git/mcp-agent/models/embeddinggemma-300M_seq512_mixed-precision.tflite"
+additional_metadata = [
+  { key = "embedding_dimensions", value_type = "Int32", value = 256 },
+  { key = "max_seq_length", value_type = "Int32", value = 512 }
+]
+
+# Section 2: HuggingFace Tokenizer (if available)
+# Uncomment if you have the tokenizer.json
+# [[section]]
+# section_type = "HF_Tokenizer"
+# data_path = "tokenizer.json"