StreamEmbed models

LoRA adapters for StreamEmbed, which searches your own screen history by describing what you saw. Only the adapters are stored here; each base model comes from its own repository, pinned to the commit the adapters were trained against.

Model	Base model (commit)	Size	StreamEmbed support
qwen3-vl-2b (default)	Qwen/Qwen3-VL-Embedding-2B (9f2f7e7)	2B	yes
qwen3-vl-8b	Qwen/Qwen3-VL-Embedding-8B (2c45655)	8B	yes
siglip2-so400m	google/siglip2-so400m-patch16-naflex (cc24074)	1.1B	weights only
vultron-0.8b	vultr/VultronRetrieverFlash-Qwen3.5-0.8B (6f6b675)	0.8B	weights only
siglip2-base	google/siglip2-base-patch16-naflex (b53b807)	0.4B	weights only

StreamEmbed's stream encoder runs Qwen3-VL models. The SigLIP2 and Vultron adapters are published for use with your own code; StreamEmbed lists them but does not run them yet. The SigLIP2 adapters were trained with NaFlex inputs of at most 1024 patches (max_num_patches in models.json).

Each model folder has two LoRAs (rank 32, alpha 32) on the attention layers of the image and text (or language model) encoders:

• query_lora.safetensors: the retrieval LoRA, trained to find screenshots from a description. It encodes questions, and full screenshots when nothing is skipped.
• frame_adapter.safetensors: encodes screenshots when repeated blocks are skipped and unchanged ones are reused across frames, so that these vectors stay close to full-screenshot vectors.

models.json lists every model with its base repository, commit and files.

Use

import streamembed as se

se.configure(model="qwen3-vl-2b")
se.build_index_from_directory("screenshots")
results = se.query_index("the flight search page with fares to Denver")
se.visualize_results(results)

To apply an adapter yourself, load it into a PEFT model of its base:

from peft import LoraConfig, get_peft_model, set_peft_model_state_dict
from safetensors.torch import load_file

state = load_file("qwen3-vl-8b/query_lora.safetensors")
targets = sorted({k.split("base_model.model.")[1].rsplit(".lora_", 1)[0] for k in state})
model = get_peft_model(base_model, LoraConfig(r=32, lora_alpha=32, target_modules=targets))
set_peft_model_state_dict(model, state)

The files are PEFT LoRA state dicts (keys base_model.model.<module>.lora_A.weight and lora_B.weight) in float32.