VLM2Vec-Full
VLM2Vec-Full is a 4.1B-parameter multimodal embedding model built on Microsoft's Phi-3.5-vision backbone. It converts a vision-language model into an embedding model trained on contrastive learning tasks. The model handles cross-modal retrieval (image+text search, text+image search) and produces dense vector representations for similarity matching across 36 evaluation datasets.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | TIGER-Lab |
| Parameters | 4.1B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 555.2k |
| Likes | 29 |
| Last updated | 2025-04-07 |
| Source | TIGER-Lab/VLM2Vec-Full |
What VLM2Vec-Full is
VLM2Vec-Full is a fine-tuned vision-language model (based on Phi-3.5-vision-instruct) adapted for multimodal embedding generation via contrastive learning. It uses in-batch negatives for training and was evaluated on the MMEB benchmark (Massive Multimodal Embedding Benchmark). The model accepts both image and text inputs, normalizes embeddings, and supports pooling strategies. Inference requires CUDA and operates in bfloat16 precision. No quantization or LoRA weights are explicitly mentioned for this checkpoint; the card references alternative LoRA and full-model checkpoints.
Run VLM2Vec-Full locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="TIGER-Lab/VLM2Vec-Full")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: ~16–24 GB VRAM for bfloat16 inference on a single batch of 1–4 samples (4.1B params + vision encoder overhead). Training was conducted at batch size 2048 (full model) / 1024 (LoRA), suggesting A100 or H100 class GPUs. Exact throughput, latency, and quantization thresholds are Unknown.
The card references a LoRA checkpoint (MMEB.lora8.bs1024) but does not detail LoRA config, rank, or target modules for VLM2Vec-Full itself. Full-model training was done with batch size 2048 at bfloat16. Fine-tuning feasibility for custom tasks is Unknown; would require review of GitHub code. No QLoRA guidance provided.
When to avoid it — and what to weigh
- Real-time, Ultra-low-latency Applications — 4.1B parameters and vision processing require non-trivial GPU memory and inference time. Unsuitable for single-digit millisecond response requirements without aggressive batching or distillation.
- Edge Devices or CPU-only Environments — Model requires CUDA, bfloat16 precision, and substantial VRAM. Edge deployment would need quantization, distillation, or model pruning—not discussed in the card.
- Task-Specific Fine-tuning Without Sufficient Data — The model is trained on a specific contrastive learning objective over MMEB-train. Heavy domain shift (e.g., medical imagery) may require task-specific tuning, feasibility of which is not documented.
- Proprietary or Closed-source Downstream Systems — Apache-2.0 license permits redistribution but requires license reproduction. If your system cannot disclose dependencies, integration requires legal review.
License & commercial use
Apache-2.0 license. This is a permissive OSI-approved license permitting commercial use, modification, and distribution under the condition that the license and copyright notice are retained.
Commercial use is permitted under Apache-2.0. You must retain the license and attribution. The base model (Phi-3.5-vision-instruct) is Microsoft's; verify its license and any commercial use terms independently. No commercial support, SLA, or indemnity is implied by this open-source release.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Model accepts arbitrary image and text inputs; no input validation, rate limiting, or adversarial robustness measures are documented. Users should validate inputs in production. No security audit or responsible disclosure policy mentioned. Custom code (tag: custom_code) is loaded from HuggingFace; review code before deployment in sensitive environments.
Alternatives to consider
CLIP (OpenAI)
Established image–text embedding model; smaller, faster, well-documented. Trade-off: does not support vision-language reasoning beyond basic image classification.
LLaVA (LoRA-based multimodal model)
Open-source vision-language model; smaller variants available. Trade-off: optimized for captioning/QA, not embedding tasks; requires separate embedding extraction.
Qwen-VL or InternVL
Alternative VLM backbones for embedding tasks. Trade-off: separate ecosystem, potentially different embedding quality; licensing and support vary.
Ship VLM2Vec-Full with senior software developers
VLM2Vec-Full offers strong cross-modal retrieval out of the box. Start with the HuggingFace model card or GitHub repo. For production deployment, benchmark on your hardware, plan for 16–24 GB VRAM, and review the Apache-2.0 license requirements.
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VLM2Vec-Full FAQ
Can I use VLM2Vec-Full in a commercial product?
What GPU do I need to run this model?
Can I fine-tune VLM2Vec-Full for my specific domain?
What is the context length for text inputs?
Custom software development services
From first prototype to production, DEV.co delivers software development services around tools like VLM2Vec-Full. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Ready to Deploy Multimodal Search?
VLM2Vec-Full offers strong cross-modal retrieval out of the box. Start with the HuggingFace model card or GitHub repo. For production deployment, benchmark on your hardware, plan for 16–24 GB VRAM, and review the Apache-2.0 license requirements.