base_model, base_model_relation, datasets, license, language, pipeline_tag, library_name, tags

base_model	base_model_relation	datasets	license	language	pipeline_tag	library_name	tags
Qwen/Qwen3-8B	finetune	Glint-Research/Fable-5-traces Roman1111111/gpt5.5-terminal	apache-2.0	en	text-generation	transformers	safetensors qlora agentic coding reasoning thinking claude qwen3

Parable-Qwen3-8B-Claude-Fable-5

Qwen3-8B trained on real Claude Fable 5 and GPT-5.5 agent traces: 67% lower held-out test loss than its base, and the strongest strictly-graded qualitative score in the Parable series: 23 of 34 fully correct.

Parable-Qwen3-8B is a Qwen/Qwen3-8B fine-tune trained on real multi-step agent sessions: planning, tool use, and <think> reasoning captured from actual Claude Fable 5 and GPT-5.5 agent work, not synthetic Q&A. Highest strict-qual release in the Parable series, alongside the Granite 8B line.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "AnkitAI/Parable-Qwen3-8B-Claude-Fable-5",
    torch_dtype="auto", device_map="auto")
tok = AutoTokenizer.from_pretrained("AnkitAI/Parable-Qwen3-8B-Claude-Fable-5")

msgs = [{"role": "user", "content": "Write a Python function that retries an HTTP request with exponential backoff."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=3000, temperature=0.7, top_p=0.95, do_sample=True)
text = tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)
answer = text.split("</think>")[-1].strip()  # response opens with a <think> block
print(answer)

GGUF quants for llama.cpp / Ollama / LM Studio: Parable-Qwen3-8B-Claude-Fable-5-GGUF.

Sampling: temperature 0.7, top_p 0.95, generous max_new_tokens (at least 2500).

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 (NF4, sequence length 1024) trained on a single 16 GB GPU, quantized with llama.cpp.

Evaluation

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. Cross-repo numbers are indicative only: splits, tokenizers, and context lengths differ (ours is measured at 1,024 tokens).

Limitations

• Trained for agent work: on ops-style prompts it sometimes (2/34 in our eval) responds with structured tool-call JSON rather than prose. Useful inside agent harnesses; in plain chat, re-prompt or lower the temperature.
• Fine-tuned at 1,024-token sequences; the base model's native 128K-token context remains fully available, so long sessions work, with the fine-tuned behavior strongest in the opening turns.

As a fine-tune it inherits Qwen3-8B's base behaviors and knowledge cutoff. As with any local model, treat generated commands and code as drafts to review.

Provenance & licensing

Model weights: Apache-2.0 (inherited from Qwen3-8B). 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:8b
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
• Qwen for the base model
• empero-ai, whose Qwable recipe the Parable series follows
• llama.cpp