Instructions to use TokenRhythm/NeoHorse-1-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TokenRhythm/NeoHorse-1-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TokenRhythm/NeoHorse-1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TokenRhythm/NeoHorse-1-4B")
model = AutoModelForCausalLM.from_pretrained("TokenRhythm/NeoHorse-1-4B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TokenRhythm/NeoHorse-1-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TokenRhythm/NeoHorse-1-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TokenRhythm/NeoHorse-1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TokenRhythm/NeoHorse-1-4B

SGLang

How to use TokenRhythm/NeoHorse-1-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TokenRhythm/NeoHorse-1-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TokenRhythm/NeoHorse-1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TokenRhythm/NeoHorse-1-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TokenRhythm/NeoHorse-1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TokenRhythm/NeoHorse-1-4B with Docker Model Runner:
```
docker model run hf.co/TokenRhythm/NeoHorse-1-4B
```

NeoHorse-1-4B

Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness.

Technical Report

NeoHorse-1-4B is a 4B causal language model and an initial prototype on the path toward recursive self-improvement (RSI). It is post-trained from Qwen3.5-4B for text-based agent harnesses, tool use, coding, and instruction following.

Derived from Qwen/Qwen3.5-4B 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: 64.87 macro average across ten benchmarks versus 58.94 for Qwen3.5-4B (+5.93).

Model Details

Property	Value
Model family	NeoHorse Agent-Native Causal Language Model
Parameters	Approximately 4B
Base model	Qwen3.5-4B
Post-training	Routing-guided agentic post-training
Interface	Text input and text output
Context length	262,144 natively and extensible up to 1,010,000 tokens.
Weight format / precision	Safetensors / BF16

Evaluation

The 4B track compares NeoHorse-1-4B with five representative open-weight models. Results are grouped by capability in the table below. Higher is better; Δ is NeoHorse-1-4B minus Qwen3.5-4B. Bold marks the best available result; underlining marks the second-best.

Benchmark	Qwen3.5-4B	Gemma-4-E4B-it	Nanbeige-4.2-3B	Agents-A1-4B	Spark-X2.5-4B	NeoHorse-1-4B	Δ vs Qwen3.5-4B
🤖 Agentic
QwenClawBench	38.47	22.98	40.66	43.16	43.52	44.68	+6.21
WorkBuddy Bench	24.62	11.65	21.03	33.37	26.47	34.41	+9.79
PinchBench	71.19	47.60	66.78	75.07	62.37	77.33	+6.14
VitaBench	21.50	5.00	31.50	39.25	37.00	32.00	+10.50
BFCL v4	61.02	47.18	67.28	46.60	63.71	61.79	+0.77
tau2-Bench	84.29	43.60	85.08	81.00	77.72	88.46	+4.17
💻 Coding
HumanEval	87.20	84.76	98.78	92.68	92.07	96.95	+9.75
LiveCodeBench v6	53.71	52.00	72.50^*	56.57	54.86	59.43	+5.72
📚 Instruction Following
IFBench	60.33	40.00	55.00	63.33	73.33	65.33	+5.00
IFEval	87.06	74.68	84.47	83.55	91.13	88.35	+1.29
📊 Overall
Ten-benchmark average	58.94	42.95	62.31	61.46	62.22	64.87	+5.93

* Nanbeige-4.2-3B LiveCodeBench v6 result is reported in the corresponding model's official blog post or technical report.

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-1-4B"

The OpenAI-compatible requests below use the server's --served-model-name (for example, neohorse-1-4b), not the filesystem path.

SGLang

The technical report uses SGLang v0.5.17.

pip install "sglang==0.5.17"
MODEL_PATH="/path/to/NeoHorse-1-4B"
python3 -m sglang.launch_server \
  --model-path "$MODEL_PATH" \
  --served-model-name neohorse-1-4b \
  --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-1-4b","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-1-4B"
vllm serve "$MODEL_PATH" \
  --served-model-name neohorse-1-4b \
  --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-1-4b","messages":[{"role":"user","content":"Write a Python function that returns the first n Fibonacci numbers."}],"max_tokens":512}'

The examples use 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/Qwen3.5-4B. 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.