🪶 Parable-Granite-3B v2 — trained on genuine Claude Fable 5 agent traces

This is the full-precision safetensors repo (vLLM / transformers / fine-tuning). For llama.cpp, Ollama, and LM Studio use the GGUF repo.

A tiny local model that thinks before it answers — planning, reasoning, and terminal instincts distilled from real agent sessions.

~3 GB of RAM is all you need. Laptop, old GPU, Raspberry-Pi-class boxes with swap — the Q4 build runs anywhere. One command and you have a private, offline reasoning model on your machine:

ollama run hf.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF:Q4_K_M

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The headline — v2 is a different model

v2 is a full retrain: 13× more genuine Fable 5 trace data (11,574 sessions, 16.8M tokens — corpus published) and a rebuilt recipe (completion-masked loss, replay mixing, benchmark-gated checkpoints, seed-averaged weights).

Clean answers, structured reasoning, agent instincts — and the transcript artifacts that leaked into v1's replies are gone. One trade, made on purpose: raw HumanEval-style function synthesis stays the base model's turf (81.7 vs 70.1) — v2 spends that capacity on agent behavior instead, and spends half as much as v1 did. Measurement notes below. 👇

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Announcements

📌 Same links, new model. v2 replaces v1 in place — every existing Ollama command, script, and bookmark now serves v2. No migration, nothing to change.

🔮 v3 is already training. Rejection-sampled SFT: thousands of candidate solutions generated against executable tests, only verified passers enter the corpus. The goal is simple — above-base agent capability, not just clean behavior. Follow AnkitAI for the drop.

📦 Full family. This 3B is the smallest Parable. Need more headroom? 8B Granite, 8B Qwen, 4B Qwen — same recipe, no matter your hardware.

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How to run it

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "Write a Python function that retries an HTTP request with exponential backoff."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=3000, temperature=0.7, top_p=0.95, do_sample=True)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

GGUF quants (2.1-6.8 GB, runs in ~3 GB RAM): Parable-Granite-4.1-3B-Claude-Fable-5-GGUF

Thinking mode

Every answer opens with a <think>...</think> reasoning block — that's the Fable 5 heritage. llama.cpp's --jinja 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 (2500+) — trace-trained models think at length before answering.

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Measurement notes

All numbers: identical llama.cpp harness, greedy decoding, Q4_K_M, base model measured on the same instrument. We train multiple seeds and ship the weight-average — single-run scores at 3B swing ±3 points on GPU nondeterminism alone, so most cards report their luckiest run; we ship the average and report the shipped weights' own numbers. Raw eval outputs live in this repo.

Which model should you use? Pure single-function code completion → the base model is genuinely strong there. Explanations, debugging, terminal workflows, structured reasoning, agent-style tasks → that's what Parable is trained on, and where v2 shines.

What's new in v2 (training)

The recipe follows our ongoing tech report (in preparation):

• Completion-only loss masking (Hermes 3, Tülu 3) — loss on assistant tokens only, so the model learns to answer, not to imitate transcripts
• 30% replay mix of general instruction data (Luo et al., Biderman et al.) — the anti-forgetting lever
• Session re-segmentation + sanitization — why v1 sometimes leaked agent JSON into normal chat, and v2 never does (0/34)
• Benchmark-gated checkpoints (Dong et al.) instead of fixed epochs
• Seed-averaged weights (model soups, Wortsman et al.) — we ship the average of multiple runs, not the lottery winner

With Claude Fable 5 now retired, genuine self-authored Fable traces are a fixed, non-renewable corpus. Unlike most models in this niche, our full training corpus is public: AnkitAI/parable-corpus-v2 — deduplicated, quality-gated, provenance-tagged.

Good to know

• Fine-tuned at 2,048-token sequences; the base 128K context stays available, fine-tuned behavior is strongest in the opening turns.
• Not trained for: multi-file repo navigation, vision, non-English.
• Inherits Granite-4.1-3B's knowledge cutoff. Treat generated commands 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/.

For tool-calling workloads, use the base model; this variant is built for reasoning prose. The drop has a specific mechanism: sampled generations show the model intermittently answering with args-only tool-call JSON (for example {"base": 10, "height": 5}) instead of a function call, which the AST scorer rejects. That is trace-scaffolding format bleeding into standalone tasks, the failure mode the series paper names session leakage (Section 6 of the report). The reasoning-voice strengths this variant trains for are unaffected on prose tasks.

Base & license

Weights: Apache-2.0 (inherited from ibm-granite/granite-4.1-3b). Training data: Fable-5-traces AGPL-3.0, gpt5.5-terminal MIT — since traces originate from third-party assistants, their terms may apply to downstream training; check before commercial distillation.

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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 · IBM Granite for the base · empero-ai whose Qwable recipe inspired the series · llama.cpp

Version history

• v2 (2026-07-16) — this release. 13× corpus, rebuilt recipe, seed-averaged weights, zero leakage.
• v1 (2026-07) — initial release, 857-row corpus. Preserved as repo revision history.

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

More on the Parable models: ankitaglawe.com/parable