---

base_model: Qwen/Qwen3-4B

base_model_relation: finetune

datasets:

• Glint-Research/Fable-5-traces
• Roman1111111/gpt5.5-terminal

license: apache-2.0

language:

• en

pipeline_tag: text-generation

library_name: gguf

tags:

• gguf
• qlora
• agentic
• coding
• reasoning
• llama.cpp

---

Parable-Qwen3-4B-fable-GGUF

!Parable

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-fable (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:

ollama run hf.co/AnkitAI/Parable-Qwen3-4B-fable-GGUF:Q4_K_M

Python (llama-cpp-python):

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="AnkitAI/Parable-Qwen3-4B-fable-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.

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