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These models output a scalar reward score or classification result, often used in reinforcement learning or content moderation tasks.
They are executed with --is-embedding and some may require --trust-remote-code.

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Example launch Command

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 \

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Supported models

Model Family (Reward)Example HuggingFace IdentifierDescription
Llama (3.1 Reward / LlamaForSequenceClassification)Skywork/Skywork-Reward-Llama-3.1-8B-v0.2Reward 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-v0.2Derived from Gemma‑2 (27B), this model provides human preference scoring for RLHF and multilingual tasks.
InternLM 2 (Reward / InternLM2ForRewardMode)internlm/internlm2-7b-rewardInternLM 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-72BA 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-apeachA smaller Qwen2.5 variant used for sequence classification, offering an alternative RLHF scoring mechanism.