bidirlm-omni-2.5b-GGUF

bidirlm-omni-2.5b GGUF

GGUF format of BidirLM/BidirLM-Omni-2.5B-Embedding for use with CrispEmbed.

BidirLM-Omni 2.5B — Qwen3-derived bidirectional encoder, 2048-d shared embedding space, 90+ languages. Includes text + audio + vision paths (audio via the shared CrispAudio library; vision via the BidirLM ViT + DeepStack hierarchy).

Modalities

The upstream model is omnimodal (text + image + audio). This GGUF includes:

• Text — bidirectional Qwen3 body with mean pooling. Validated against the upstream reference at cosine ≥ 0.999 across the test set.
• Audio — Whisper-shape audio tower (Conv2D stem + 24-layer encoder + 1024→2048 projection). Encodes raw 16 kHz mono PCM to the same 2048-d shared embedding space as text, enabling cross-modal cosine similarity.
• Vision — 24-layer ViT with 4-corner bilinear pos interp, 2D rotate-half RoPE, and DeepStack hierarchy (3 hooks at config-listed layers). Encodes preprocessed image patches into the same 2048-d shared space as text, validated at cosine ≥ 0.9999 per-token vs the HF reference.

CLI usage

# Text
./crispembed -m bidirlm-omni-2.5b "your query"

# Audio (raw f32le 16 kHz mono PCM)
ffmpeg -i clip.wav -ar 16000 -ac 1 -f f32le clip.raw
./crispembed -m bidirlm-omni-2.5b --audio clip.raw

# Image (Python — preprocessor needs Pillow + transformers)
python -c "from crispembed import CrispEmbed; ce=CrispEmbed('bidirlm-omni-2.5b'); print(ce.encode_image('photo.jpg').shape)"

Build requirements

The audio path is provided by the shared CrispAudio library (lives in CrispASR/crisp_audio). CrispEmbed's CMake auto-discovers it at the sibling-repo path ../CrispASR/crisp_audio (overridable via -DCRISP_AUDIO_DIR=...). If that directory is not present at configure time, crispembed_has_audio() returns 0 and the --audio flag fails — text encoding still works.

The vision tower is built unconditionally (no sibling-repo dependency). Image preprocessing in Python uses HF's Qwen2VLImageProcessorFast — pip install transformers torchvision pillow.

Files

Parity vs HuggingFace reference

Cosine similarity vs the upstream sentence-transformers reference on a fixed test set (text + audio (jfk.wav) + vision (cat.jpg)):

Note: below the 0.99 retrieval-quality bar — text: q5_k (0.983), q4_k (0.937); vision: q5_k (0.988), q4_k (0.966). Embeddings are still functionally usable (>0.9 = directionally correct for similarity ranking) but expect small differences in nearest-neighbor results vs the upstream f32 reference.

Quick Start

# Download
huggingface-cli download cstr/bidirlm-omni-2.5b-GGUF bidirlm-omni-2.5b-f16.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m bidirlm-omni-2.5b-f16.gguf "Hello world"

# Or with auto-download
./crispembed -m bidirlm-omni-2.5b "Hello world"

Model Details

Verification

Verified bit-identical to HuggingFace sentence-transformers (cosine similarity >= 0.999 on test texts).

Usage with CrispEmbed

CrispEmbed is a lightweight C/C++ text embedding inference engine using ggml. No Python runtime, no ONNX. Supports BERT, XLM-R, Qwen3, and Gemma3 architectures.

# Build CrispEmbed
git clone https://github.com/CrispStrobe/CrispEmbed
cd CrispEmbed
cmake -S . -B build && cmake --build build -j

# Encode
./build/crispembed -m bidirlm-omni-2.5b-f16.gguf "query text"

# Server mode
./build/crispembed-server -m bidirlm-omni-2.5b-f16.gguf --port 8080
curl -X POST http://localhost:8080/v1/embeddings \
    -d '{"input": ["Hello world"], "model": "bidirlm-omni-2.5b"}'

Credits

• Original model: BidirLM/BidirLM-Omni-2.5B-Embedding
• Inference engine: CrispEmbed (ggml-based)
• Conversion: convert-decoder-embed-to-gguf.py

Provenance and EU AI Act Art. 53 note

• Upstream model: BidirLM/BidirLM-Omni-2.5B-Embedding — published by BidirLM.
• Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.