Marketplace · 25+ models
Recommendation What to show, who to show it to
Recommender systems, personalization engines, content matching, collaborative filtering, and ranking models — for product discovery, feeds, and next-best-action.
Editor's pick
The recommendation model most teams reach for first.
DLRM
Deep Learning Recommendation Model — the production reference for ads/feed ranking.
Spec sheet
- Family
- Meta
- Parameters
- —
- License
- MIT
- Status
- Live
- Best for
- What to show, who to show it to
- Sits in
- Recommendation
Pricing and routing rank visible on InferenceBench. Variants and quantisations appear in the Yobibyte deploy console.
The rest of the lineup
5 more in Recommendation. All deployable in one click.
Self-attentive sequential recommendation; transformer baseline for sequences.
Dual-encoder retrieval architecture — embed user and item separately for fast ANN.
Hybrid collaborative + content recommender; strong baseline with cold-start support.
Showing 6 of 25+. The full catalog (with quantisations, hardware variants, and per-region pricing) lives in the Yobibyte console.
Quick start
Five lines to your first recommendation call.
Every model in this category is reachable from the same Yobitel SDK. Swap the model name; the rest of the call shape stays identical. Authenticated via your workspace key.
from yobitel import Inference
# DLRM — Meta's production recommender
client = Inference(model="meta/dlrm")
recs = client.recommend(
user_id="u_12345",
context={
"page": "homepage",
"time_of_day": "evening",
"device": "mobile",
},
top_k=10,
diversity=0.3,
)
for r in recs:
print(f"{r.item_id} score={r.score:.3f} reason={r.reason}")Where teams ship this
Real recommendation. In production.
Four use cases that customers run today. Pick a model from the lineup above, deploy on Yobibyte, plug it into the surrounding stack. Done.
- 01
Product discovery and merchandising
- 02
Content feeds and engagement ranking
- 03
Next-best-action for sales and marketing
- 04
Cold-start recommendation
Frameworks
Bring what your team already knows
Yobitel handles the serving layer (GPU scheduling, KV cache, autoscaling, request batching) so your team focuses on the model and the product.
Learn about YobibyteExplore the rest
Other categories in the marketplace
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Object detection, image classification, segmentation
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Generative AI
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Predictive analytics, forecasting, anomaly detection
Automation & RPA
Process automation, workflow AI, document processing
Industry-Specific
Vertical-specific models by industry
Speech & Audio
ASR, TTS, speaker diarization, audio classification
Don't see what you need?
Bring your own model or fine-tune one of ours. Yobitel engineers can sit with your team and ship the right stack.