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Cataloged from TokenRhythm/NeoHorse-1-4B-GGUF
All models by TokenRhythm →Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness.
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.
This repository provides GGUF versions of NeoHorse-1-4B for local use with llama.cpp, Ollama, and LM Studio. It includes 16-bit (BF16) weights and smaller 8-bit, 5-bit, and 4-bit quantized versions. Quantized versions take up less disk space and use less memory, making the model easier to run on your own hardware. These files contain text-only model weights, fine-tuned by TokenRhythm from Qwen3.5-4B.
The results below are from the original checkpoint, not a separate GGUF benchmark run.
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.
* 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 withenable_thinking=trueandforce_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.
FP16 option: NeoHorse-1-4B-F16.gguf (8.42 GB) is also available for runtimes or hardware that prefer FP16 over BF16. Use :F16 instead of :BF16 in the llama.cpp or Ollama commands below. This is a format-converted 16-bit option, not a quality upgrade.
4-bit is the smallest option, 5-bit sits in the middle, and 8-bit uses less compression. Choose 16-bit (BF16) to keep the original weight precision. Lower precision saves memory but can affect response quality. The sizes above are file sizes; running the model also needs memory for the conversation and other runtime data.
These standard llama.cpp quantizations were generated directly from the BF16 GGUF, without an importance matrix. All three passed eight short llama.cpp compatibility checks, including thinking, single and parallel tool calls, and tool-result continuation, at 4K context. These checks are not a quantized-quality benchmark; the benchmark scores in this card refer to the original checkpoint. Quantized variants have not yet been separately tested in Ollama or LM Studio. Use the desired filename in place of the BF16 filename in the examples below.
Use a recent runtime with Qwen3.5 support. The commands below download the model from Hugging Face and cache it locally; no manual file download is required. These are text-only GGUF files with an embedded chat template and no MTP draft head.
Download and start a local OpenAI-compatible server:
Download and run directly, without creating a Modelfile:
Paste this repository URL into LM Studio's model search and choose a GGUF file, or download through the CLI:
Select the desired file when prompted, then load it in LM Studio with MTP speculative decoding disabled. Start with a 4K context and increase it according to available memory. In LM Studio's native API, use reasoning: "off" to disable thinking.
For llama.cpp and Ollama, replace :BF16 with the tag of another variant listed in Files and versions, such as :Q8_0, :Q5_K_M, or :Q4_K_M. Only variants already present in the repository can be downloaded.
See the llama.cpp guide, Ollama–Hugging Face integration, and LM Studio download guide.
Download NeoHorse-1-4B-BF16.gguf (approximately 8.42 GB). Use a recent runtime with Qwen3.5 support. The file includes the chat template and does not include an MTP draft head.
Create a Modelfile containing:
Import the GGUF file and load it with MTP speculative decoding disabled. With the CLI:
Basic chat and tool-call smoke tests passed in llama.cpp, Ollama 0.33.3, and LM Studio's llama.cpp 2.33.0 engine. Context capacity depends on available memory; the examples start at 4K. In LM Studio's native API, use reasoning: "off" to disable thinking.
After starting llama-server as shown in Deployment, connect an OpenAI-compatible client to http://127.0.0.1:8080/v1. The examples below use the server alias neohorse-1-4b; it must match the --alias value in the launch command.
Install the Python client:
Send a message to your local server:
These short examples allow up to 2,048 output tokens within the 4K server context. For longer prompts or responses, increase -c and adjust max_tokens while leaving room for the input and chat template.
Keep --jinja enabled in the llama-server launch command so it can use the embedded tool-aware chat template and parse generated function calls into tool_calls.
Using the same client and generation_config from Basic Usage:
This example prints the requested function and its arguments. Your application executes the function and returns its result in a tool message, using the corresponding tool_call_id, to continue the conversation.
NeoHorse-1-4B 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. This repository converts the resulting checkpoint to BF16 GGUF and provides quantized variants using llama.cpp.
For questions or issue reports, use the NeoHorse project repository.
llama-server -hf TokenRhythm/NeoHorse-1-4B-GGUF:BF16 -c 4096 -ngl 99 --jinja --port 8080 --alias neohorse-1-4bollama run hf.co/TokenRhythm/NeoHorse-1-4B-GGUF:BF16lms get https://huggingface.co/TokenRhythm/NeoHorse-1-4B-GGUFllama-server -m NeoHorse-1-4B-BF16.gguf -c 4096 -ngl 99 --jinja --port 8080 --alias neohorse-1-4bFROM ./NeoHorse-1-4B-BF16.ggufPARAMETER num_ctx 4096ollama create neohorse-1-4b-bf16 -f Modelfileollama run neohorse-1-4b-bf16lms import ./NeoHorse-1-4B-BF16.gguf --copylms load neohorse-1-4b --context-length 4096 --no-speculative-draft-mtppip install openaifrom openai import OpenAI
client = OpenAI( base_url="http://127.0.0.1:8080/v1", api_key="EMPTY", # Replace if server authentication is enabled.)
generation_config = { "temperature": 1.0, "top_p": 0.95, "presence_penalty": 1.5, "max_tokens": 2048, "extra_body": { "top_k": 20, "min_p": 0.0, "repeat_penalty": 1.0, "chat_template_kwargs": {"enable_thinking": True}, },}
response = client.chat.completions.create( model="neohorse-1-4b", messages=[ { "role": "user", "content": "Write a Python function that returns the first n Fibonacci numbers.", } ], **generation_config,)
message = response.choices[0].messageprint("Reasoning:", getattr(message, "reasoning_content", None))print("Answer:", message.content)tools = [ { "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather for a specified city.", "parameters": { "type": "object", "properties": { "city": { "type": "string", "description": "Name of the city.", } }, "required": ["city"], }, }, }]
response = client.chat.completions.create( model="neohorse-1-4b", messages=[ { "role": "user", "content": "What is the weather in Beijing right now?", } ], tools=tools, tool_choice="auto", **generation_config,)
message = response.choices[0].message
if message.tool_calls: for call in message.tool_calls: print(call.function.name, call.function.arguments)else: print(message.content)@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}}