Add quantization / model size section to README

README.md

@@ -89,6 +89,27 @@ instruction shown above. Documents are encoded without an instruction.
 Pooling is mean pooling over the content tokens; the instruction and the
 beginning-of-text token are excluded — exactly as in LLM2Vec.
 
+### Model size and quantization
+
+The model has ~8B parameters and loads in roughly 14 GB (bfloat16). To run on
+smaller GPUs it can be loaded quantized via `bitsandbytes`:
+
+```python
+from sentence_transformers import SentenceTransformer
+from transformers import BitsAndBytesConfig
+
+model = SentenceTransformer(
+    "jealk/TTC-L2V-supervised-2", trust_remote_code=True,
+    model_kwargs={"quantization_config": BitsAndBytesConfig(load_in_8bit=True)},
+)
+```
+
+Approximate memory footprint: **bfloat16 ≈ 14 GB**, **8-bit ≈ 8 GB**,
+**4-bit ≈ 5 GB** (`load_in_4bit=True`).
+
+> The quantized variants have not been evaluated on benchmarks — embedding
+> quality may differ from the full-precision model.
+
 ### Notes
 
 - `trust_remote_code=True` is required: it loads the bidirectional Llama