初始化项目，由ModelHub XC社区提供模型

Model: cmu-lti/osim-8b
Source: Original Platform

README.md

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+---
+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