# Reward Models These models output a scalar reward score or classification result, often used in reinforcement learning or content moderation tasks. ```{important} They are executed with `--is-embedding` and some may require `--trust-remote-code`. ``` ## Example launch Command ```shell python3 -m sglang.launch_server \ --model-path Qwen/Qwen2.5-Math-RM-72B \ # example HF/local path --is-embedding \ --host 0.0.0.0 \ --tp-size=4 \ # set for tensor parallelism --port 30000 \ ``` ## Supported models | Model Family (Reward) | Example HuggingFace Identifier | Description | |---------------------------------------------------------------------------|-----------------------------------------------------|---------------------------------------------------------------------------------| | **Llama (3.1 Reward / `LlamaForSequenceClassification`)** | `Skywork/Skywork-Reward-Llama-3.1-8B-v0.2` | Reward model (preference classifier) based on Llama 3.1 (8B) for scoring and ranking responses for RLHF. | | **Gemma 2 (27B Reward / `Gemma2ForSequenceClassification`)** | `Skywork/Skywork-Reward-Gemma-2-27B-v0.2` | Derived from Gemma‑2 (27B), this model provides human preference scoring for RLHF and multilingual tasks. | | **InternLM 2 (Reward / `InternLM2ForRewardMode`)** | `internlm/internlm2-7b-reward` | InternLM 2 (7B)–based reward model used in alignment pipelines to guide outputs toward preferred behavior. | | **Qwen2.5 (Reward - Math / `Qwen2ForRewardModel`)** | `Qwen/Qwen2.5-Math-RM-72B` | A 72B math-specialized RLHF reward model from the Qwen2.5 series, tuned for evaluating and refining responses. | | **Qwen2.5 (Reward - Sequence / `Qwen2ForSequenceClassification`)** | `jason9693/Qwen2.5-1.5B-apeach` | A smaller Qwen2.5 variant used for sequence classification, offering an alternative RLHF scoring mechanism. |