fix: add voyage quantization models

mteb/models/voyage_models.py

@@ -16,6 +16,12 @@
     # synthetic data
 }
 
+# The missing values are translated to themselves
+VOYAGE_DTYPE_TRANSLATION = {
+    "float32": "float",
+    "bf16": "float",
+}
+
 # Total token limits per model based on VoyageAI documentation
 VOYAGE_TOTAL_TOKEN_LIMITS = {
     "voyage-3.5-lite": 1_000_000,
@@ -95,6 +101,7 @@ def __init__(
         max_tpm: int = 1_000_000,
         max_tokens: int | None = None,
         model_prompts: dict[str, str] | None = None,
+        output_dtype: str | None = None,
         **kwargs,
     ) -> None:
         requires_package(self, "voyageai", model_name, "pip install 'mteb[voyageai]'")
@@ -106,6 +113,7 @@ def __init__(
         self._max_tpm = max_tpm
         self._max_tokens = max_tokens
         self.model_prompts = self.validate_task_to_prompt_name(model_prompts)
+        self.output_dtype = output_dtype
 
     def _calculate_default_batch_size(self) -> int:
         """Calculate the default batch size based on total token limit and context length.
@@ -143,6 +151,10 @@ def _batched_encode(
     ) -> np.ndarray:
         embeddings, index = [], 0
 
+        output_dtype = VOYAGE_DTYPE_TRANSLATION.get(
+            self.output_dtype, self.output_dtype
+        )
+
         while index < len(sentences):
             batch, batch_tokens = [], 0
             while (
@@ -164,10 +176,31 @@ def _batched_encode(
                     texts=batch,
                     model=self._model_name,
                     input_type=input_type,
+                    output_dtype=output_dtype,
                 ).embeddings
             )
 
-        return np.array(embeddings)
+        embeddings_array = np.array(embeddings)
+
+        if output_dtype == "binary":
+            # Unpack bit-packed embeddings: each byte contains 8 embedding values
+            unpacked_embeddings = []
+            for embedding in embeddings_array:
+                # Convert bytes to bits and unpack
+                unpacked = []
+                for byte_val in embedding:
+                    # Extract 8 bits from each byte (LSB first)
+                    for bit_pos in range(8):
+                        bit_val = (byte_val >> bit_pos) & 1
+                        # Convert 0/1 to -1/1 for binary (signed)
+                        unpacked.append(1.0 if bit_val else -1.0)
+                unpacked_embeddings.append(unpacked)
+            embeddings_array = np.array(unpacked_embeddings, dtype=np.float32)
+        elif output_dtype != "float":
+            # Convert int8/uint8 embeddings to float32
+            embeddings_array = embeddings_array.astype(np.float32)
+
+        return embeddings_array
 
 
 model_prompts = {
@@ -201,6 +234,60 @@ def _batched_encode(
     public_training_data=None,
 )
 
+voyage_3_5_int8 = ModelMeta(
+    name="voyageai/voyage-3.5 (output_dtype=int8)",
+    revision="1",
+    release_date="2025-01-21",
+    languages=None,  # supported languages not specified
+    loader=partial(
+        VoyageWrapper,
+        model_name="voyage-3.5",
+        model_prompts=model_prompts,
+        output_dtype="int8",
+    ),
+    max_tokens=32000,
+    embed_dim=1024,
+    open_weights=False,
+    n_parameters=None,
+    memory_usage_mb=None,
+    license=None,
+    reference="https://docs.voyageai.com/docs/flexible-dimensions-and-quantization",
+    similarity_fn_name="cosine",
+    framework=["API"],
+    use_instructions=True,
+    training_datasets=VOYAGE_TRAINING_DATA,
+    public_training_code=None,
+    public_training_data=None,
+    adapted_from="voyageai/voyage-3.5",
+)
+
+voyage_3_5_binary = ModelMeta(
+    name="voyageai/voyage-3.5 (output_dtype=binary)",
+    revision="1",
+    release_date="2025-01-21",
+    languages=None,  # supported languages not specified
+    loader=partial(
+        VoyageWrapper,
+        model_name="voyage-3.5",
+        model_prompts=model_prompts,
+        output_dtype="binary",
+    ),
+    max_tokens=32000,
+    embed_dim=1024,  # Same as original after unpacking from bits
+    open_weights=False,
+    n_parameters=None,
+    memory_usage_mb=None,
+    license=None,
+    reference="https://docs.voyageai.com/docs/flexible-dimensions-and-quantization",
+    similarity_fn_name="cosine",
+    framework=["API"],
+    use_instructions=True,
+    training_datasets=VOYAGE_TRAINING_DATA,
+    public_training_code=None,
+    public_training_data=None,
+    adapted_from="voyageai/voyage-3.5",
+)
+
 voyage_large_2_instruct = ModelMeta(
     name="voyageai/voyage-large-2-instruct",
     revision="1",