~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
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).
| 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. 👇
📌 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.
| File | Size | Fits in | Notes |
|---|---|---|---|
| Q4_K_M | 2.1 GB | ~3 GB RAM/VRAM | ⭐ Recommended — best size/quality balance |
| Q5_K_M | 2.4 GB | ~3.5 GB | Higher quality |
| Q6_K | 2.8 GB | ~4 GB | Near-lossless |
| Q8_0 | 3.6 GB | ~5 GB | Maximum quality |
| F16 | 6.8 GB | ~8 GB | Full precision, for re-quantizing |
Intelligence per gigabyte: the Q4_K_M build scores 70.1 HumanEval in 2.1 GB — ~33 pts/GB; an 8B-class Q4 needs ~5 GB for its score. If RAM is your constraint, this is the family's density sweet spot.
Full-precision safetensors (vLLM, transformers, further fine-tuning): Parable-Granite-4.1-3B-Claude-Fable-5
Ollama (chat template ships inside the GGUF — zero config):
ollama run parable/granite4.1-fable:3b
# or straight from this repo:
ollama run hf.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF:Q4_K_M
llama.cpp:
llama-cli -m Parable-Granite-4.1-3B-Claude-Fable-5-GGUF-Q4_K_M.gguf --jinja \
-p "Write a bash one-liner to find the 10 largest files in a directory tree."
LM Studio / Jan / Open WebUI: search "parable" in-app, or paste this repo URL.
Python (llama-cpp-python):
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="AnkitAI/Parable-Granite-4.1-3B-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=0.7,
)
print(out["choices"][0]["message"]["content"])
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.
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.
Real outputs, both models at Q4_K_M, temperature 0.7 — unedited except length.
Prompt: "Make this more idiomatic:" result = []; for x in items: if x.active == True: result.append(x.name.upper())
| Base Granite-4.1-3B | Parable v2 |
|---|---|
Here's a more idiomatic version of the code using a list comprehension: result = [item.name.upper() for item in items if item.active] — Explanation: List comprehensions provide a concise way to create lists based on existing iterables. The new result list is constructed by iterating over each item… (continues for ~10 more lines) |
result = [item.name.upper() for item in items if item.active] |
Prompt: "My Python script fails with 'RecursionError: maximum recursion depth exceeded' in a JSON parser I wrote. What are the likely causes and the standard fix?"
| Base Granite-4.1-3B | Parable v2 |
|---|---|
| Opens with prose: "The RecursionError: maximum recursion depth exceeded error in a Python script, especially when dealing with a JSON parser, typically indicates that your recursive function is calling itself too many times without reaching a proper base case…" | Opens with a diagnosis table: Common culprits for this error — a cause / why-it-triggers / example table, then the fix. Structured like an agent working the problem, not an essay. |
The pattern from real agent traces: answer first, structure over prose, no padding. (Where the base is stronger — raw single-function synthesis — is stated plainly in the measurement notes above.)
The recipe follows our ongoing tech report (in preparation):
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.
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/.
| simple_python | multiple | parallel | parallel_multiple | |
|---|---|---|---|---|
| Granite-4.1-3B base | 0.848 | 0.790 | 0.710 | 0.665 |
| This model (chat variant) | 0.413 | 0.605 | 0.320 | 0.425 |
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.
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.
| 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 |
If this model is useful in your work, you can support independent research:
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}
}
Glint-Research & Roman1111111 for the open trace data · IBM Granite for the base · empero-ai whose Qwable recipe inspired the series · llama.cpp
ollama run hf.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF:Q4_K_M
More on the Parable models: ankitaglawe.com/parable
ibm-granite/granite-4.1-3b