base_model:

• Qwen/Qwen3.5-9B
• TokenRhythm/NeoHorse-1-9B library_name: transformers tags:
• noesis
• noesis-repack
• bf16
• mtp
• qwen3_5
• text-generation-inference
• reasoning
• distillation
• deepseek
• sft
• rl
• gspo
• math
• stem
• tool-use
• function-calling
• lora-merge
• dare-ties
• agentic
• tool-use
• coding
• reasoning
• instruction-following
• heretic
• uncensored
• decensored
• abliterated

license: apache-2.0 language:

• en
• ru
• zh
• vi
• kk
• ja
• af
• am
• ar
• as
• ast
• az
• be
• bg
• bn
• bs
• ca
• ceb
• ckb
• cs
• cy
• da
• de
• el
• es
• et
• eu
• fa
• ff
• fi
• fil
• fr
• ga
• gl
• gn
• gu
• ha
• he
• hi
• hr
• hu
• hy
• id
• ig
• is
• it
• jv
• ka
• kam
• kea
• km
• kmr
• kn
• ko
• ky
• lb
• lg
• ln
• lo
• lt
• luo
• lv
• mi
• mk
• ml
• mn
• mr
• ms
• mt
• mvy
• my
• ne
• nl
• "no"
• nso
• ny
• oc
• om
• "or"
• pa
• pl
• ps
• pt
• qxp
• ro
• rw
• sd
• sk
• skr
• sl
• sn
• so
• sr
• sv
• sw
• ta
• te
• tg
• th
• ti
• tk
• tr
• ug
• uk
• umb
• ur
• uz
• wo
• xh
• yo
• yue
• zu

NOESIS / AMAImedia

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform.

• Founder: Ilia Bolotnikov
• Organization: AMAImedia.com
• X (Twitter): @AMAImediacom
• LinkedIn: Ilia Bolotnikov
• Telegram: @djbionicl
• Release date: 2026-09-07

AMAImedia

• Released Heretic repository: Dingdust/NeoHorse-1-9B-heretic

• Original repository: TokenRhythm/NeoHorse-1-9B

This is a decensored version of a model, made using Heretic v1.4.0

Abliteration parameters

Performance

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 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. These launch examples have not yet been validated on GPU for this repackaged release.

License

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

The upstream model is 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}
}

For questions or issue reports, use the NeoHorse project repository.