🪶 Parable-Qwen3-8B — trained on genuine Claude Fable 5 agent traces

The largest Parable: planning, tool use, and <think> reasoning distilled from real Claude Fable 5 and GPT-5.5 agent sessions — not synthetic Q&A.

~6 GB of RAM is all you need. Laptop, mid-range GPU, yesterday's desktop — the Q4 build runs anywhere with that much headroom. One command and you have a private, offline reasoning model on your machine:

ollama run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF:Q4_K_M

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Announcements

🔮 v2 is coming. The 3B just got the v2 treatment (13× corpus, rebuilt recipe) — the same upgrade lands here next. Same links, in-place.

📦 Full family. This 8B is the largest Parable, alongside Parable-Qwen3-4B — browse the full collection for every size, quant, and eval report.

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Pick your size

File	Size	Fits in	Notes
Q4_K_M	4.8 GB	~6 GB RAM/VRAM	⭐ Recommended — best size/quality balance
Q5_K_M	5.6 GB	~7 GB	Higher quality
Q6_K	6.4 GB	~7.5 GB	Near-lossless
Q8_0	8.3 GB	~9.5 GB	Maximum quality

Full-precision safetensors (vLLM, transformers, further fine-tuning): Parable-Qwen3-8B-Claude-Fable-5

How to run it

Ollama (chat template ships inside the GGUF — zero config):

ollama run parable/qwen3-fable:8b
# or straight from this repo:
ollama run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF:Q4_K_M

llama.cpp:

llama-cli -m Parable-Qwen3-8B-Claude-Fable-5-GGUF-Q4_K_M.gguf --jinja \
  -p "Write a bash one-liner to find the 10 largest files in a directory tree."

LM Studio: lms get parable/qwen3-fable, search "parable" in-app, or paste this repo URL (parable on LM Studio Hub).

Python (llama-cpp-python):

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF",
    filename="*Q4_K_M.gguf", n_ctx=8192,
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Write a Python function that retries an HTTP request with exponential backoff."}],
    max_tokens=3000, temperature=0.7,
)
print(out["choices"][0]["message"]["content"])

Thinking mode

Every answer opens with a <think>...</think> reasoning block — native to Qwen3, reinforced by this fine-tune. llama.cpp's --jinja chat mode separates it automatically; strip it before showing replies to end users. Sampling: temperature 0.7, top_p 0.95, and budget max_tokens generously (at least 2500) — trace-trained models think at length before answering.

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How it measures

Held-out test split, identical evaluation code and context length for base and fine-tune:

Metric	Base Qwen3-8B	Parable	Δ
Test loss	2.162	0.712	−67%

Qualitative review (34 coding/terminal/debugging prompts, strictly graded by mentally executing every answer): 23/34 fully correct, 30/34 correct or partially correct — the highest fully-correct score in the series. We publish these numbers because strict qualitative grading is rare in this niche; judge accordingly.

For reference, the strongest published fine-tune on this data family (a 9B) reports 0.71 validation loss; this release measures 0.712 under our stricter 1,024-token evaluation. Cross-repo numbers are indicative only: splits, tokenizers, and context lengths differ (ours is measured at 1,024 tokens).

What it's trained on

• Glint-Research/Fable-5-traces — 4.4k real Claude Fable 5 coding-agent session traces with <think> reasoning and tool calls (AGPL-3.0)
• Roman1111111/gpt5.5-terminal — terminal-agent task solutions (MIT)

Every example passed a quality gate (schema validation, secrets scrub, length filtering) before training. QLoRA fine-tune (NF4, sequence length 1024) trained on a single 16 GB GPU, quantized with llama.cpp.

Good to know

• Weakest on config-file generation and stateful shell logic (4/34 in our eval: Makefile targets, log-watcher scripts, Dockerfile layer ordering) — review generated configs before use.
• Fine-tuned at 1,024-token sequences; the base 128K context stays fully available, so long sessions work, with the fine-tuned behavior strongest in the opening turns.
• Inherits Qwen3-8B's base behaviors and knowledge cutoff. As with any local model, treat generated commands and code as drafts to review.

Evaluation

Function calling (BFCL V3, AST subset)

Measured 2026-07-29: bfcl-eval at gorilla main, prompting mode, Q4_K_M GGUFs served by llama.cpp on a T4, base and Parable under the identical harness. Categories: simple_python / multiple / parallel / parallel_multiple (400/200/200/200 items). Raw generations and score files: parable-v2-artifacts under verify/bfcl/.

	simple_python	multiple	parallel	parallel_multiple
Qwen3-8B base	0.953	0.945	0.935	0.900
Parable-Qwen3-8B	0.930	0.900	0.905	0.850

A 2.3 to 5.0 point trade per category: prose-trace SFT costs a little function-calling sharpness, as this card's evaluation note predicts. If you need maximum tool-calling accuracy, use the base; this variant buys the reasoning voice.

Base & license

Weights: Apache-2.0 (inherited from Qwen/Qwen3-8B). Training data: Fable-5-traces AGPL-3.0, gpt5.5-terminal MIT — since 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.

Get Parable

Platform
Ollama	ollama run parable/qwen3-fable:8b · parable namespace
Ollama (family flagship, best per size)	ollama run parable/fable
Hugging Face	GGUF quants, full weights, eval reports
LM Studio	lms get parable/qwen3-fable · parable on LM Studio Hub

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

Glint-Research & Roman1111111 for the open trace data · Qwen for the base · empero-ai whose Qwable recipe the Parable series follows · llama.cpp

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Six gigabytes. Real Fable 5 reasoning. Yours, offline, right now.

ollama run hf.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF:Q4_K_M

More on the Parable models: ankitaglawe.com/parable