---

license: apache-2.0

base_model: empero-ai/Qwable-9B-Claude-Fable-5

base_model_relation: quantized

datasets:

- Glint-Research/Fable-5-traces

- Roman1111111/gpt5.5-terminal

language:

- en

pipeline_tag: image-text-to-text

library_name: gguf

tags:

- gguf

- llama.cpp

- quantized

- qwen3.5

- multimodal

- vision

- sft

- distillation

- agentic-coding

---

<p align="center">

<img src="qwable9b.jpg" alt="Qwable-9B-Claude-Fable-5" width="420"/>

</p>

Qwable-9B-Claude-Fable-5-GGUF

Developed by Empero

GGUF quantizations of empero-ai/Qwable-9B-Claude-Fable-5

for llama.cpp, Ollama, LM Studio, and other GGUF runtimes. This repo

ships a vision projector (mmproj), so the model runs as a full multimodal (image + text) assistant —

not just text.

Qwable-9B-Claude-Fable-5 is a full-parameter fine-tune of Qwen3.5-9B on agentic coding and reasoning traces

distilled from Claude Fable 5 and a GPT-5.5 terminal agent. For full training details and the complete

evaluation, see the base model card.

> Early release. Strong coding and agentic behavior out of the box; a full benchmark suite is underway and

> will be published. See Provenance & licensing.

Files

Text weights — pick one quant

| File | Quant | Size | Notes |

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

| Qwable-9B-Claude-Fable-5-Q4_K_M.gguf | Q4_K_M | 5.3 GB | recommended default — smallest, runs on ~6–8 GB VRAM |

| Qwable-9B-Claude-Fable-5-Q5_K_M.gguf | Q5_K_M | 6.1 GB | balanced quality / size |

| Qwable-9B-Claude-Fable-5-Q6_K.gguf | Q6_K | 6.9 GB | high quality |

| Qwable-9B-Claude-Fable-5-Q8_0.gguf | Q8_0 | 8.9 GB | near-lossless |

| Qwable-9B-Claude-Fable-5-bf16.gguf | BF16 | 17 GB | full precision (conversion base) |

Vision projector — for image input

| File | Size | Notes |

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

| mmproj-Qwable-9B-Claude-Fable-5-f16.gguf | 876 MB | CLIP vision encoder; required for images, pairs with any quant above |

Text-only use needs just a quant. For image understanding, download both a text quant and the mmproj.

Usage

llama.cpp — text

llama-cli -m Qwable-9B-Claude-Fable-5-Q4_K_M.gguf --jinja \
  -p "Write a Python function that merges two sorted lists." \
  --temp 0.6 --top-p 0.95 --top-k 20 --repeat-penalty 1.05 -n 2048

llama.cpp — multimodal (image + text)

llama-mtmd-cli -m Qwable-9B-Claude-Fable-5-Q4_K_M.gguf \
  --mmproj mmproj-Qwable-9B-Claude-Fable-5-f16.gguf \
  --image photo.jpg -p "Describe this image." \
  --temp 0.6 --top-p 0.95 --top-k 20 -n 512

Ollama

ollama run hf.co/empero-ai/Qwable-9B-Claude-Fable-5-GGUF:Q4_K_M

Or via a Modelfile (pulls in the vision projector for image support):

FROM ./Qwable-9B-Claude-Fable-5-Q4_K_M.gguf
FROM ./mmproj-Qwable-9B-Claude-Fable-5-f16.gguf
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER repeat_penalty 1.05

Sampling & output format

• Sampling (Qwen3.5 recommended): general tasks temp 1.0, precise coding temp 0.6; `top_p 0.95,

top_k 20, min_p 0. Use repeat_penalty 1.05` (a small bump from Qwen's default 1.0) to avoid rare

non-terminating reasoning loops, and allow generous -n / max_new_tokens.

• Reasoning model: every response opens with a <think>...</think> block before the final answer —

parse and strip that span for end users.

Model details

• Developed by: Empero
• Base model: Qwen3.5-9B — a dense, natively multimodal model with a hybrid attention stack

(3:1 Gated DeltaNet linear-attention to Gated full-attention), ~152k vocabulary, long native context.

• Fine-tune type: full parameter (all text-backbone weights trained), assistant-only loss. The vision

tower was left unchanged from the base — so vision works (via the included mmproj) but was inherited,

not specifically tuned.

• Format: GGUF (text quants + CLIP mmproj), converted and quantized with llama.cpp.
• Languages: primarily English.

Evaluation

> The evaluation below was measured on the unquantized fine-tune. Quantized variants are very close at

> Q8_0/Q6_K and degrade gradually toward Q4_K_M — expect a small quality drop at the lower quants.

Training quality was tracked via held-out validation loss / token-accuracy on a 100-example split

(80% Fable / 20% terminal), plus a qualitative generation review:

| Step | eval loss | eval token-acc |

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

| 100 | 0.743 | 0.784 |

| 300 (≈ epoch 1) | 0.714 | 0.791 |

| 500 | 0.713 | 0.791 |

No overfitting: held-out loss decreased then plateaued (~0.71) through epoch 2 — it never rose even as

train loss fell to ~0.64. In a 34-prompt qualitative review, roughly 27/34 responses were clean and

correct, strongest on coding and terminal/agentic tasks — current tooling (ss over netstat,

git-filter-repo, Argon2id) with security-aware judgment (rotating a leaked key first, constant-time

comparison). Full transcripts: sample_generations.md.

Limitations

• Reasoning model. Each response opens with a <think> block; strip it for end users and allow generous

output length. Use repeat_penalty≈1.05 for consistently crisp completions.

• Strongest within its domain (coding / agentic / reasoning). For general-knowledge or long-form factual

questions, verify specifics as with any 9B model.

• Reflects its base and teachers. A distillation fine-tune of Qwen3.5-9B on Claude Fable 5 and GPT-5.5

traces; it carries their style and limits and received no extra safety tuning. Add your own review/safety

layer for production.

• Quantization. Lower quants (esp. Q4_K_M) trade a little accuracy for size; use Q6_K/Q8_0 when quality

matters most.

Quantization

Converted from the fine-tuned weights with llama.cpp convert_hf_to_gguf.py, then quantized with

llama-quantize. The BF16 GGUF is the conversion base; the K-quants are derived from it. The mmproj is the

base Qwen3.5-VL vision encoder (unchanged by fine-tuning). All files were verified to load and generate

in llama.cpp — text (code, reasoning) and image understanding both confirmed.

Provenance & licensing

Weights are released under Apache-2.0, inherited from the Qwen3.5-9B base. The fine-tuning data comes from

generated traces of Claude Fable 5 and GPT-5.5 (via the linked public datasets). 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.

Shared with the community for research and experimentation, as-is.

Support / Donate

If this model helped you, consider supporting the project:

• BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
• LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x
• XMR: 42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJY

Acknowledgements

• Developed and released by Empero
• Base model: Qwen3.5-9B (Alibaba Qwen team)
• Datasets: Glint-Research/Fable-5-traces,

Roman1111111/gpt5.5-terminal

• Tooling: llama.cpp, TRL,

Transformers