---

license: apache-2.0

base_model: Nanbeige/Nanbeige4.2-3B

datasets:

- Glint-Research/Fable-5-traces

- Roman1111111/gpt5.5-terminal

pipeline_tag: text-generation

tags:

- gguf

- qlora

- agentic

- coding

- reasoning

- nanbeige

---

Parable-Nanbeige4.2-3B-Claude-Fable-5-GGUF

!Nanbeige4.2-3B

Part of the Parable series: small local LLMs fine-tuned on genuine agent

traces. This is the reasoning-style chat variant of Nanbeige4.2-3B (to our

knowledge the first published fine-tune of this base), tuned on real Claude

Fable 5 agent transcripts so its step-by-step reasoning voice carries into

local use.

Full-precision weights: Parable-Nanbeige4.2-3B-Claude-Fable-5

Files

| File | Quant | Size | |

|---|---|---|---|

| Parable-Nanbeige4.2-3B-Claude-Fable-5-Q4_K_M.gguf | Q4_K_M | 2.6 GB | recommended default |

| Parable-Nanbeige4.2-3B-Claude-Fable-5-Q5_K_M.gguf | Q5_K_M | 3.0 GB | |

| Parable-Nanbeige4.2-3B-Claude-Fable-5-Q6_K.gguf | Q6_K | 3.4 GB | |

| Parable-Nanbeige4.2-3B-Claude-Fable-5-Q8_0.gguf | Q8_0 | 4.4 GB | |

| Parable-Nanbeige4.2-3B-Claude-Fable-5-F16.gguf | F16 | 8.4 GB | for re-quantizing |

Usage

Nanbeige support landed in llama.cpp master on 2026-07-27, so you

currently need llama.cpp built from master. As of 2026-07-29 no packaged release has

it yet: Homebrew's bottle (build 10150) still fails with

unknown model architecture: 'nanbeige' (verified), and Ollama / LM Studio

bundle older builds too. Build from source until the next releases land:

git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j

llama-cli -m Parable-Nanbeige4.2-3B-Claude-Fable-5-Q4_K_M.gguf \
  -ngl 99 -c 4096 \
  -p "Write a python function that reverses a string." --temp 0.6

Runtime status (2026-07-29, stated plainly): the merged weights generate

correctly (verified via transformers on GPU; sample outputs in the artifacts

repo under verify/), and these GGUFs converted and quantized cleanly with

num_loops=2 in the header. However, in our tests **CPU-only inference of

this architecture hangs with current llama.cpp master builds**; the

architecture is days old and its CPU path appears immature upstream. Prefer

GPU offload (-ngl 99), or use the

full-precision repo

with transformers until upstream stabilizes. We will update this note as

llama.cpp matures.

Output begins with a <think>...</think> reasoning block, then the answer.

If you are building on top of this model, parse and strip the think block

before showing text to end users.

The architecture runs its 22-layer stack twice (num_loops: 2, effective

depth 44). This is encoded in the GGUF and handled automatically; expect

roughly 2x the per-token compute of a typical 22-layer model of this width.

Model details

• Base: Nanbeige/Nanbeige4.2-3B (4.17B params, 3B non-embedding, Apache-2.0)
• Method: QLoRA (nf4, r=16, alpha=32) on the standard 7-module target set, all 22 layers (154 adapted modules)
• Data: 845 genuine Claude Fable 5 agent-session traces (chat variant: prose answers, <think> reasoning preserved) + 362 replay rows (OpenCoder educational_instruct + tulu-3 mix) to limit forgetting; 98% of rows retain their full assistant span at the 3,072-token training window
• Schedule: 380 optimizer steps, effective batch 8, completion-only loss masking, cosine LR
• Held-out trace loss: 2.333 (base) → 2.133 (tuned), same 225-trace held-out set never used in training

Evaluation

The number above is a held-out language-modeling loss on real agent traces:

it measures how much better the model fits the Fable-5 reasoning

distribution, and it is the honest headline for what this fine-tune does.

We do not claim benchmark gains over the base model. Nanbeige4.2-3B's model

card reports strong agentic results (SWE-bench Verified 63.6, Terminal-Bench

2.0 44.1, vendor self-reported); this variant trains the reasoning voice,

not tool-calling, and prose-only SFT should be expected to trade a little

benchmark sharpness for style fidelity. If you need maximum benchmark

performance on agentic harnesses, use the base; if you want its capability

with a transparent, well-structured reasoning trace in local use, use this.

Limitations

• The base model's own benchmark figures are vendor self-reported and were

not independently reproduced here.

• Training ran on a 4-bit quantized base (QLoRA); the F16 merge cannot exceed

4-bit-base quality.

• Requires llama.cpp built from master (2026-07-27+). No packaged release

(brew, Ollama, LM Studio) loads this architecture yet; they will as their

bundled llama.cpp catches up.

Quantization

Quantized with llama.cpp llama-quantize from the F16 merge, num_loops=2

verified present in the GGUF header at conversion time.

Provenance & licensing

Fine-tuned from Nanbeige/Nanbeige4.2-3B (Apache-2.0). Training data:

Glint-Research/Fable-5-traces

(AGPL-3.0) and

Roman1111111/gpt5.5-terminal

(MIT). Because those traces originate from third-party assistants, the

providers' terms may apply to downstream training and distillation. If you

plan to build on this model commercially, confirm your use aligns with those

terms.

Citation

The recipe, evaluation methodology and failure analysis behind this model are

documented in the tech report:

> Aglawe, A. (2026). *Agent-Trace Fine-Tuning of Small Language Models under

> Constrained Compute.* Zenodo. doi:10.5281/zenodo.21676407

@misc{aglawe2026agenttrace,
  author    = {Aglawe, Ankit},
  title     = {Agent-Trace Fine-Tuning of Small Language Models under Constrained Compute},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.21676407},
  url       = {https://doi.org/10.5281/zenodo.21676407}
}

Acknowledgements

The Nanbeige team for the base model; Glint-Research and Roman1111111 for the

trace datasets; empero-ai for the recipe this series iterates on.