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How to rerun NeoHorse-1 with the reported sampling settings with Neo Horse

Serve a NeoHorse-1 checkpoint with the exact SGLang settings listed for the technical report.

Who it is forA researcher or evaluator who wants to check NeoHorse-1 numbers against the reported protocol before comparing it with other models.

The problemBenchmark scores change with sampling settings, so a comparison is only fair if the same settings are used.

The outcomeA local SGLang server that matches the reported protocol settings, ready for your own benchmark runs.

What you need first

  • A machine with enough GPU memory for the 4B or 9B BF16 weights, or a quantized GGUF or MLX build for smaller hardware
  • Python with pip available
  • Disk space for the downloaded checkpoint from Hugging Face or ModelScope

Steps

  1. Run pip install "sglang==0.5.17"
  2. Set MODEL_PATH to the NeoHorse-1-9B directory, then run python3 -m sglang.launch_server --model-path "$MODEL_PATH" --served-model-name neohorse-1-9B --host 0.0.0.0 --port 30000 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder (inferred, check the README)
  3. Set the client to temperature=1.0, top_p=0.95, top_k=20, min_p=0.0, presence_penalty=1.5, repetition_penalty=1.0.
  4. Turn on thinking mode with enable_thinking=true and force_nonempty_content=true.
  5. Run your chosen benchmark tasks and record the runs, noting that the README uses three runs for some benchmarks and one run for others.

Prompt for your agent

In the TokenRhythm/NeoHorse repo, serve the NeoHorse-1-9B checkpoint at <model-directory> with SGLang v0.5.17 on port 30000 using the reported protocol settings in the Evaluation section. Send the benchmark prompt <benchmark-item> and return the answer and the settings used. Verify that the request sets temperature=1.0, top_p=0.95, and top_k=20 before recording any score.

Caveat. The README gives the sampling settings but not the benchmark datasets or scripts, so you must obtain the official protocols yourself to reproduce the reported scores.

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