Upload README.md with huggingface_hub

README.md

@@ -37,3 +37,52 @@ Evaluation set: STS-B test (1,379 sentence pairs, human similarity 0-5). Baselin
 - Q8_0, Q6_K, Q5_K_M and IQ4_XS show negligible quality loss (|Δρ| < 0.05%, Emb Cosine > 0.985) and are safe drop-in replacements.
 - Q4_K_M (4.85 bpw) shows a small but visible drop (Δρ ≈ +0.60%, Emb Cosine 0.985) — notably worse than the equally-sized IQ4_XS, so prefer IQ4_XS or Q5_K_M over Q4_K_M when size is comparable.
 - IQ3_M (3.76 bpw) is the only variant with a clearly measurable drop (Δρ ≈ +0.89%, Emb Cosine 0.93); use only when storage is critical.
+
+## Usage (llama.cpp GGUF)
+
+All files here are GGUF and run with [llama.cpp](https://github.com/ggml-org/llama.cpp). Replace the model file with the quant you downloaded. Use `-ngl 999` to offload layers to GPU (omit or `-ngl 0` for CPU-only).
+
+### Text embedding — command line
+
+```bash
+llama-embedding \
+  -m WeMM-Embedding-2B-Q5_K_M.gguf \
+  -p "Represent the meaning of this sentence." \
+  --pooling last
+```
+
+### Text embedding — HTTP server
+
+```bash
+llama-server \
+  -m WeMM-Embedding-2B-Q5_K_M.gguf \
+  --embedding \
+  -ngl 999 --host 0.0.0.0 --port 8080
+```
+
+Then request embeddings via the OpenAI-compatible endpoint:
+
+```bash
+curl http://localhost:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{"input": "Represent the meaning of this sentence.", "model": "WeMM-Embedding-2B-Q5_K_M"}'
+```
+
+### Multimodal (image / video) — HTTP server
+
+The visual projector (`mmproj-WeMM-Embedding-2B-BF16.gguf`) is required for image and video inputs:
+
+```bash
+llama-server \
+  -m WeMM-Embedding-2B-Q5_K_M.gguf \
+  --mmproj mmproj-WeMM-Embedding-2B-BF16.gguf \
+  --embedding \
+  -ngl 999 --host 0.0.0.0 --port 8080
+```
+
+Send image/video inside the chat content the same way as the base model (interleave `image`/`video` before `text`).
+
+### Notes
+
+- Output is a 2048-dim L2-normalized vector; matryoshka truncation (e.g. `--embd-normalize` + slicing) follows the base model's `matryoshka_dimensions` [64, 128, 256, 512, 1024, 2048].
+- Q8_0 / Q4_K_M / BF16 are mirrored from `DreamBlooms/WeMM-Embedding-2B-GGUF`; Q6_K / Q5_K_M / IQ4_XS / IQ3_M were produced for this repo with `llama-quantize` from the same BF16 master.