Model reference · open weights
OneRec is an open-weight language model from OpenOneRec. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | OpenOneRec |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 2.1B |
| Context | 40k tokens |
| Runs with | transformers |
| Released | 2025-12-30 |
| Popularity | 105k downloads / month |
| Licence | Open weights |
About
OpenOneRec is an open-source framework designed to bridge the gap between traditional recommendation systems and Large Language Models (LLMs). While Generative Recommendation has shown promise, existing models often struggle with isolated data silos and a lack of reasoning capabilities.
To address this, we introduce a unified framework that comprises:
We propose RecIF-Bench to rigorously assess the synergy between instruction following and domain-specific recommendation. It organizes 8 distinct tasks into a four-layer capability hierarchy:
The benchmark aggregates data from three domains: Short Video (Content), Ads (Commercial), and Product (E-commerce).
The OpenOneRec-Foundation series is built upon the Qwen architecture, enhanced with Itemic Tokens for modality alignment and trained via a multi-stage protocol.
| Model | Backbone | Parameters | Description | Link |
|---|---|---|---|---|
| OneRec-1.7B | Qwen3-1.7B | 1.7B | Standard version trained on open-source data (~33B tokens) | HuggingFace |
| OneRec-8B | Qwen3-8B | 8B | Standard version trained on open-source data (~33B tokens) | HuggingFace |
| OneRec-1.7B-Pro | Qwen3-1.7B | 1.7B | Scaled-up version with expanded datasets (~130B tokens) | HuggingFace |
| OneRec-8B-Pro | Qwen3-8B | 8B | Scaled-up version with expanded datasets (~130B tokens) | HuggingFace |
OpenOneRec reframes recommendation as a general-purpose sequence modeling paradigm.
To bridge the modality gap, we treat items as a distinct modality using Itemic Tokens derived from hierarchical vector quantization. This allows the LLM to process interaction history as a cohesive context sequence.
Our framework utilizes the following recipe:
OpenOneRec-Foundation achieves State-of-the-Art (SOTA) results across RecIF-Bench tasks, significantly outperforming baselines like LC-Rec and TIGER.
| Task | Metric | SASRec | TIGER | LC-Rec | OneRec-1.7B | OneRec-8B | OneRec-1.7B-Pro | OneRec-8B-Pro |
|---|---|---|---|---|---|---|---|---|
| Short Video Rec | Recall@32 | 0.0119 | 0.0132 | 0.0180 | 0.0272 | 0.0355 | 0.0274 | 0.0369 |
| Ad Rec | Recall@32 | 0.0293 | 0.0581 | 0.0723 | 0.0707 | 0.0877 | 0.0735 | 0.0964 |
| Product Rec | Recall@32 | 0.0175 | 0.0283 | 0.0416 | 0.0360 | 0.0470 | 0.0405 | 0.0538 |
| Label-Cond. Rec | Recall@32 | 0.0140 | 0.0123 | 0.0170 | 0.0184 | 0.0228 | 0.0182 | 0.0235 |
| Label Pred. | AUC | 0.6244 | 0.6675 | 0.6139 | 0.6184 | 0.6615 | 0.6071 | 0.6912 |
| Interactive Rec | Recall@32 | -- | -- | 0.2394 | 0.1941 | 0.3032 | 0.2024 | 0.3458 |
| Item Und. | LLM Score | -- | -- | 0.2517 | 0.3175 | 0.3202 | 0.3133 | 0.3209 |
| Rec. Explanation | LLM Score | -- | -- | 3.9350 | 3.3540 | 3.6774 | 3.5060 | 4.0381 |
On the Amazon Benchmark (10 datasets), OpenOneRec demonstrates exceptional zero-shot/few-shot transfer capabilities, achieving an average 26.8% improvement in Recall@10 over the second-best method.
| Domain | SASRec | TIGER | LC-Rec | Ours |
|---|---|---|---|---|
| Baby | 0.0381 | 0.0318 | 0.0344 | 0.0513 |
| Beauty | 0.0639 | 0.0628 | 0.0764 | 0.0924 |
| Cell Phones | 0.0782 | 0.0786 | 0.0883 | 0.1036 |
| Grocery | 0.0789 | 0.0691 | 0.0790 | 0.1029 |
| Health | 0.0506 | 0.0534 | 0.0616 | 0.0768 |
| Home | 0.0212 | 0.0216 | 0.0293 | 0.0390 |
| Pet Supplies | 0.0607 | 0.0542 | 0.0612 | 0.0834 |
| Sports | 0.0389 | 0.0331 | 0.0418 | 0.0547 |
| Tools | 0.0437 | 0.0344 | 0.0438 | 0.0593 |
| Toys | 0.0658 | 0.0527 | 0.0549 | 0.0953 |
Metric: Recall@10. Ours refers to OneRec-Foundation with text-augmented itemic tokens strategy.
Code release and detailed usage instructions are coming soon.
Currently, you can load our models using `transformers>=4.51
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys onerec for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (onerec below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"onerec","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.