---

license: apache-2.0

base_model: TokenRhythm/NeoHorse-1-9B

base_model_relation: quantized

library_name: llama.cpp

pipeline_tag: text-generation

tags:

• gguf
• llama.cpp
• qwen3.5
• quantized
• text-generation
• agentic
• tool-use
• coding
• reasoning
• instruction-following

---

NeoHorse-1-9B GGUF

Community GGUF quantizations of TokenRhythm/NeoHorse-1-9B.

<div align="center" style="background-color:#f59e0b;color:#ffffff;padding:16px 20px;border-radius:10px;line-height:1.7;">

☕ If this GGUF made your day easier, a coffee would make mine.<br>

<a href="https://ko-fi.com/ngquocvinh" style="color:#ffffff;"><strong style="color:#ffffff;">Send a coffee ☕</strong></a><br>

I build and test these releases myself. Your coffee helps keep me going.<br>

Thank you for supporting this work.

</div>

About NeoHorse-1-9B

NeoHorse-1-9B is an approximately 9B causal language model post-trained from

Qwen3.5-9B for text-based agent harnesses, tool use, coding, reasoning, and

instruction following. This package contains text-only GGUF files; vision

weights are not included. See the official upstream model card

for the model's original details, usage, and limitations.

The upstream card describes a native 262,144-token context and extensibility up

to 1,010,000 tokens. The upstream card also reports a 69.04 ten-benchmark

average versus 65.60 for Qwen3.5-9B under its SGLang evaluation protocol. Those

figures are upstream measurements, not measurements of these GGUF files.

<p align="center">

<a href="https://huggingface.co/TokenRhythm/NeoHorse-1-9B">

<img src="./9B_head_fig.jpg" alt="NeoHorse-1-9B evaluation results" width="100%">

</a>

</p>

<p align="center"><em><a href="https://huggingface.co/TokenRhythm/NeoHorse-1-9B">NeoHorse-1-9B evaluation results</a> (upstream)</em></p>

This is a quantization-only release. No training or fine-tuning was performed

as part of this conversion.

Fidelity measurements

The table compares every published quantization with the converted BF16

reference on the same hold-out split. Evaluation used the first two chunks of

wiki.valid.raw, 2,048 tokens per chunk, and the CUDA build of llama.cpp on an

NVIDIA A10M. The BF16 baseline mean PPL was 5.641047. Values are averaged over

the two evaluated chunks. File sizes use decimal GB (bytes / 1,000,000,000).

Bold rows are practical sweet spots: Q4_K_M is the balanced default, Q5_K_M

and Q5_K_S provide more fidelity headroom, and IQ4_XS is a compact alternative.

| File | Size (GB) | Mean KLD ↓ | Top-1 vs BF16 ↑ | ΔPPL | RMS Δp |

|---|---:|---:|---:|---:|---:|

| NeoHorse-1-9B-Q8_0.gguf | 9.528 | 0.006655 | 98.436% | +0.732% | 0.811% |

| NeoHorse-1-9B-Q6_K.gguf | 7.359 | 0.009702 | 97.312% | +1.120% | 1.408% |

| NeoHorse-1-9B-Q5_K_M.gguf | 6.468 | 0.030926 | 94.673% | +2.158% | 5.841% |

| NeoHorse-1-9B-Q4_K_M.gguf | 5.629 | 0.029467 | 94.428% | +0.951% | 4.429% |

| NeoHorse-1-9B-Q3_K_M.gguf | 4.624 | 0.069233 | 88.368% | +4.573% | 7.326% |

| NeoHorse-1-9B-Q2_K.gguf | 3.827 | 0.199997 | 82.063% | +14.167% | 12.510% |

| NeoHorse-1-9B-IQ2_XS.gguf | 3.285 | 0.342380 | 77.761% | +30.698% | 18.365% |

| NeoHorse-1-9B-IQ1_M.gguf | 2.877 | 0.807561 | 64.565% | +96.977% | 28.701% |

| NeoHorse-1-9B-Q1_0.gguf | 1.966 | 15.570850 | 2.933% | +596289635.160% | 63.365% |

| NeoHorse-1-9B-Q5_K_S.gguf | 6.305 | 0.030149 | 95.112% | +1.381% | 5.817% |

| NeoHorse-1-9B-Q4_K_S.gguf | 5.352 | 0.030445 | 93.842% | +1.040% | 4.639% |

| NeoHorse-1-9B-Q3_K_L.gguf | 4.926 | 0.062186 | 89.541% | +3.793% | 6.790% |

| NeoHorse-1-9B-Q3_K_S.gguf | 4.259 | 0.131594 | 85.386% | +6.965% | 9.989% |

| NeoHorse-1-9B-Q2_K_S.gguf | 3.697 | 0.240966 | 81.183% | +21.317% | 14.223% |

| NeoHorse-1-9B-IQ4_XS.gguf | 5.196 | 0.026534 | 93.891% | +2.125% | 3.807% |

| NeoHorse-1-9B-IQ4_NL.gguf | 5.418 | 0.029899 | 93.695% | +3.189% | 4.788% |

| NeoHorse-1-9B-IQ3_M.gguf | 4.415 | 0.064802 | 88.905% | +2.843% | 7.357% |

| NeoHorse-1-9B-IQ3_S.gguf | 4.371 | 0.081996 | 89.541% | +2.072% | 7.758% |

ΔPPL is the percentage change in perplexity relative to BF16. Lower KLD,

ΔPPL, and RMS Δp, together with higher Top-1 agreement, indicate closer

next-token behavior to BF16. These are next-token fidelity measurements, not a

task benchmark; reasoning, coding, and tool-use behavior can vary by workload.

The machine-readable results are in reproducibility/quality-summary.tsv,

with the evaluation details in reproducibility/manifest.md.

Quick start

Q4_K_M is a practical starting point for local use. Q5_K_M, Q6_K, or Q8_0

provide more headroom for fidelity when memory allows. Q3_K_M and lower are

low-memory experimental options; the measurements above show increasing

distance from BF16.

./llama-cli \
  -m NeoHorse-1-9B-Q4_K_M.gguf \
  --chat-template-file chat_template.jinja \
  --jinja \
  --reasoning off \
  -p 'Answer briefly in English: What is GGUF and why is it useful for running language models locally?' \
  -n 128 -c 4096 -ngl 99

The included chat_template.jinja is retained from the upstream text-only

checkpoint. Adjust context size, GPU layers, and batch settings for the

available hardware.

Reproducibility and validation

• Source: upstream revision ba5b6e4.
• The BF16 GGUF was converted directly from the upstream BF16 safetensors with

--no-nextn, because this text-only checkpoint has no MTP tensors even though

its config retains an MTP layer-count field.

• Every published file was quantized directly from that BF16 GGUF with the

model-specific imatrix included under calibration/; no re-quantization from

another quantized file was used.

• All published files passed load and short English generation smoke tests; the BF16

source additionally passed --check-tensors. Runtime readings are summarized in

reproducibility/runtime-summary.tsv.

• Verify artifacts with SHA256SUMS.txt and reproduce the

provenance from reproducibility/manifest.md.

Raw conversion, calibration, quantization, smoke-test, fidelity, and benchmark

logs are kept local and are not part of this public model package.

License and attribution

The upstream model and these community quantizations are released under the

Apache License 2.0, with the

upstream LICENSE retained in this repository.

These are community GGUF quantizations, not an official TokenRhythm release or

endorsement.