Parable-Qwen3-4B-Claude-Fable-5-GGUF

First release of the Parable series: small models trained on real agent behavior, every release eval-gated before publish.

Parable-Qwen3-4B is a Qwen3-4B fine-tune trained on real Claude Fable 5 and GPT-5.5 agent traces: multi-step tool use, planning, and <think> reasoning captured from actual agent sessions, not synthetic Q&A. It cuts held-out test loss by 47% against the base under matched evaluation and reaches 0.782 token accuracy, within a point of models twice its size trained on similar traces.

Files

File	Quant	Size	Notes
Q4_K_M	Q4_K_M	2.5 GB	Recommended default, fits ~4 GB RAM/VRAM
Q5_K_M	Q5_K_M	2.9 GB	Higher quality
Q6_K	Q6_K	3.3 GB	Near-lossless
Q8_0	Q8_0	4.3 GB	Maximum quality

Full-precision weights: Parable-Qwen3-4B-Claude-Fable-5 (for vLLM, transformers, further fine-tuning).

Usage

llama.cpp:

llama-cli -m Qwen3-4B-fable-agentic-GGUF-Q4_K_M.gguf --jinja \
  -p "Write a bash one-liner to find the 10 largest files in a directory tree."

Ollama (shorter command via the Parable namespace, or pull directly from this repo):

ollama run parable/qwen3-fable:4b
# or: ollama run hf.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF:Q4_K_M

Python (llama-cpp-python):

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="AnkitAI/Parable-Qwen3-4B-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=0.3,
)
print(out["choices"][0]["message"]["content"])

This is a reasoning model: output opens with a <think>...</think> block before the final answer. Strip it before showing responses to end users (llama.cpp's --jinja chat mode separates it automatically).

Sampling: temperature 0.3–0.7. Budget max_tokens generously (≥ 2500): like other trace-trained reasoning models, it thinks at length before answering, and a short budget can cut it off mid-thought.

Training data

• 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 via mlx-lm, quantized with llama.cpp.

Evaluation

Held-out test split, identical evaluation code for base and fine-tune (base measured through a zero-effect adapter for exact comparability):

Metric	Base Qwen3-4B	Parable	Δ
Test loss	1.888	0.996	−47%
Token accuracy	0.683	0.782	+10 pts

Qualitative review (34 coding/terminal/debugging prompts, judged clean-and-correct): of the prompts that produced a final answer, 92% were correct. The remainder hit reasoning-budget cutoffs rather than wrong answers (23/34 overall with a 2,600-token budget; see guidance above).

Limitations

• Like other trace-trained reasoning models, it invests heavily in thinking. With tight token budgets it can spend the whole budget reasoning; budget ≥ 2500 tokens or retry at lower temperature if a response comes back empty.
• Tuned hard toward agentic coding behavior; that focus trades some general-knowledge breadth, as with any specialized fine-tune in this class.
• Verify critical output. Small models over-commit to plausible specifics; treat generated commands and code as drafts to review.
• Inherits Qwen3-4B's base limitations and knowledge cutoff.

Provenance & licensing

Model weights: Apache-2.0 (inherited from Qwen3-4B). Training data licenses: Fable-5-traces AGPL-3.0, 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.

Get Parable

Platform	Command / Link
Ollama	ollama run parable/qwen3-fable:4b
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)

Acknowledgements

• Glint-Research and Roman1111111 for the open trace datasets
• empero-ai, whose Qwable recipe this release follows
• Qwen team for the base model
• mlx-lm and llama.cpp