AnkitAI

Parable-Granite-4.1-3B-Claude-Fable-5

README

License: apache-2.0

📊 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).

Table with columns: same harness, greedy, Q4_K_M, v1, v2 (this release)
same harness, greedy, Q4_K_M	v1	v2 (this release)
Dev pass-rate (MBPP subset, n=50) — base: 0.68	—	0.82
Agent-artifact leakage (JSON blobs, phantom turns)	6/34	0/34
Strict 34-prompt coding qual — base: 27/34	~18/34	25/34
HumanEval / HumanEval+	62.8 / 57.9	70.1 / 65.9

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. 👇

🚀 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.

🚀 How to run it

python
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.

🔬 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.

📚 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.

🪶 Get Parable

Table with columns: Platform
Platform
Ollama	`ollama run parable/granite4.1-fable:3b` · parable namespace
Hugging Face	full collection
LM Studio	search "parable" in-app
ModelScope	Parable on ModelScope

🙏 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.

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

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Model Details

Model Provider

AnkitAI

Model Tree

Base

ibm-granite/granite-4.1-3b

Fine-tuned

this model

Input Modalities

Text

Output Modalities