# NeoHorse 1 9B

URL: https://interfaze.ai/models/tokenrhythmneohorse-1-9b

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NeoHorse 1 9B by TokenRhythm, a text-generation model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | NeoHorse 1 9B | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | Yes | Yes |
| Language Support | 201 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 1M | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | NeoHorse 1 9B | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

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View model card on [Hugging Face](https://huggingface.co/TokenRhythm/NeoHorse-1-9B)

NeoHorse-1-9B is a 9B causal language model and an initial prototype on the path toward **recursive self-improvement (RSI)**. It is post-trained from Qwen3.5-9B for text-based agent harnesses, tool use, coding, and instruction following.

Derived from [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) and fine-tuned by TokenRhythm. This release contains **language-model weights only** and is repackaged for text-only inference. Vision weights are not included. Repackaging changes configuration and tensor key names, without changing the fine-tuned tensor values.

## Highlights

-   **Path toward RSI:** the routing harness assigns tasks to a heterogeneous model pool, records tool interactions and outcomes, estimates capability demand, and uses capability-level feedback to shape the next training mixture. Updated models can return to the harness, closing a prototype evaluation–selection–update loop; extending this loop across successive iterations is the next step toward RSI.
-   **Agentic post-training framework:** the associated research explores routing-guided curriculum SFT and routing-guided on-policy distillation to turn execution trajectories into training signal while preserving execution and harness context around each response.
-   **Data quality:** exact and near-duplicate removal, evaluation decontamination, structural validation, six-dimensional semantic evaluation, and subscene-level Scene/Goal/Outcome labeling.
-   **Broad gains:** 69.04 macro average across ten benchmarks versus 65.60 for Qwen3.5-9B (**+3.44**).

## Model Details

## Evaluation

The 9B track compares NeoHorse-1-9B with five representative open-weight baselines: Granite-4.2-8B, Qwen3.5-9B, Ornith-1.5-9B, Gemma-4-12B-it, and Muse-Glimmer-30B. Results cover ten benchmarks and are grouped by capability. Higher is better; `Δ` is NeoHorse-1-9B minus Qwen3.5-9B. **Bold** and underline mark the best and second-best results in each benchmark row, respectively; ties share the same formatting.

> **Reported protocol:** SGLang v0.5.17 · `temperature=1.0` · `top_p=0.95` · `top_k=20` · `min_p=0.0` · `presence_penalty=1.5` · `repetition_penalty=1.0` · thinking mode enabled with `enable_thinking=true` and `force_nonempty_content=true`. QwenClawBench, WorkBuddy Bench, and tau2-Bench use three runs; PinchBench and VitaBench use one run; the remaining benchmarks follow their official protocols. VitaBench uses the DeepSeek-V4-Flash simulator and judge.

## Deployment

The examples below are for self-hosted deployment from a downloaded local checkpoint.

### Local checkpoint path

The examples below assume the checkpoint has already been downloaded to local disk. Set `MODEL_PATH` to the directory containing `config.json`, tokenizer files, and model weights.

```
MODEL_PATH="/path/to/NeoHorse-1-9B"
```

The OpenAI-compatible requests below use the server's `--served-model-name` (for example, `neohorse-1-9b`), not the filesystem path.

### SGLang

The technical report uses SGLang v0.5.17.

```
pip install "sglang==0.5.17"
MODEL_PATH="/path/to/NeoHorse-1-9B"
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
```

Send an OpenAI-compatible request after the server starts:

```
curl http://localhost:30000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"neohorse-1-9b","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'
```

### vLLM

```
pip install -U vllm
MODEL_PATH="/path/to/NeoHorse-1-9B"
vllm serve "$MODEL_PATH" \
  --served-model-name neohorse-1-9b \
  --host 0.0.0.0 \
  --port 8000 \
  --max-model-len 262144 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder
```

The server exposes an OpenAI-compatible `/v1/chat/completions` endpoint. Send a request after the server starts:

```
curl http://localhost:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"neohorse-1-9b","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'
```

The example uses the configured 262,144-token context limit. Actual capacity depends on GPU memory and serving settings; reduce the context limit if needed.

## License

NeoHorse-1-9B is released under the **Apache License 2.0**.

The upstream model is [Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B). Its original copyright notice, Copyright 2026 Alibaba Cloud, is retained in the license file. TokenRhythm has modified the model through fine-tuning and repackaging for text-only inference. Modification notices are included in this model card and the released configuration, weight index, and Safetensors metadata.

## Citation

`@misc{neohorse2026, title = {NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness}, author = {NeoHorse Team}, year = {2026}, howpublished = {arXiv preprint}, eprint = {2609.08183}, archivePrefix = {arXiv}, primaryClass = {cs.CL}, url = {https://arxiv.org/abs/2609.08183} }`

For questions or issue reports, use the [NeoHorse project repository](https://github.com/TokenRhythm/NeoHorse).

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