Files
osim-8b/README.md
ModelHub XC 41f7473778 初始化项目,由ModelHub XC社区提供模型
Model: cmu-lti/osim-8b
Source: Original Platform
2026-07-25 13:52:16 +08:00

36 lines
2.2 KiB
Markdown
Raw Permalink Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

---
license: mit
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- human-simulation
- user-simulation
- behavioral-modeling
- social-simulation
---
# OSim-8B (post-trained, text)
**OSim-8B** is the post-trained (text) checkpoint of **OSim** (OdysSim), a foundation model for **human behavior simulation** — trained to imitate the *human / user* side of interactions rather than to behave as a helpful assistant. It is the text counterpart of the midtrained [`cmu-lti/osim-8b-mid`](https://huggingface.co/cmu-lti/osim-8b-mid): [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) midtrained on the OdysSim corpus (62 behavioral datasets along the five **Soul** axes — CONV/SS/COG/ROLE/EVAL) and then post-trained (task-specific RL + expert consolidation).
*(Mirror of `sunweiwei/OSim-8B`, the text post model. For the VL variant see `sunweiwei/OSim-Inst-8B`.)*
## Intended use
Simulating the human/user side of conversations — user simulation for agent evaluation, social simulation, persona / role-play. Conditioned on a "social-context" system prompt (who is speaking: role, goal, background, style); given the other party's turns it generates the next human turn.
## Results
Evaluated **out-of-distribution** as the user simulator in the τ-USI agentic benchmark (τ-bench airline+retail, 165 tasks, fixed GPT-5.2 agent), OSim-8B reaches **USI 75.6** — the **best behavioral / specialized user simulator**, surpassing same-size general instruct models and every prior specialized simulator (CoSER-8B 67.2, UserLM-8B 62.0). It is distinctively human-like in **reactivity** (Sørensen–Dice D4 ≈ 93, matching the human inter-annotator level) and in **outcome calibration** (best ECE among compared models), with essentially none of the long-horizon agentic failure modes (timeouts/perseveration) seen in non-behavioral baselines.
## Training
- **Base:** Qwen3-8B
- **Stages:** midtraining on the OdysSim corpus → task-specific reinforcement learning + expert consolidation.
## Citation
If you use this model, please cite the OdysSim paper (*Building Foundation Models for Human Behavior Simulation*). Code: https://github.com/sunnweiwei/OdysSim