---
base_model: ibm-granite/granite-4.1-3b
base_model_relation: finetune
datasets:
- AnkitAI/parable-corpus-v2
- Glint-Research/Fable-5-traces
- Roman1111111/gpt5.5-terminal
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- safetensors
- transformers
- qlora
- agentic
- agent
- coding
- tool-use
- function-calling
- terminal
- reasoning
- thinking
- claude
- claude-fable-5
- distillation
- trace-training
- granite
---

<picture>
  <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_header_dark.png">
  <img alt="Parable" src="https://raw.githubusercontent.com/ankit-aglawe/parable-assets/main/parable_header.png">
</picture>

# 🪶 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](https://huggingface.co/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF).*

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

---

## 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](https://huggingface.co/datasets/AnkitAI/parable-corpus-v2)) 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. 👇

---

## 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](https://huggingface.co/AnkitAI) for the drop.

**📦 Full family.** This 3B is the smallest Parable. Need more headroom? [8B Granite](https://huggingface.co/AnkitAI/Parable-Granite-4.1-8B-Claude-Fable-5-GGUF), [8B Qwen](https://huggingface.co/AnkitAI/Parable-Qwen3-8B-Claude-Fable-5-GGUF), [4B Qwen](https://huggingface.co/AnkitAI/Parable-Qwen3-4B-Claude-Fable-5-GGUF) — 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](https://huggingface.co/AnkitAI/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](https://arxiv.org/abs/2408.11857), [Tülu 3](https://arxiv.org/abs/2411.15124)) — 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.](https://arxiv.org/abs/2308.08747), [Biderman et al.](https://arxiv.org/abs/2405.09673)) — 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.](https://arxiv.org/abs/2310.05492)) instead of fixed epochs
- **Seed-averaged weights** ([model soups, Wortsman et al.](https://arxiv.org/abs/2203.05482)) — 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](https://huggingface.co/datasets/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](https://huggingface.co/AnkitAI/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.

## Base & license

Weights: **Apache-2.0** (inherited from [ibm-granite/granite-4.1-3b](https://huggingface.co/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

| Platform | |
|---|---|
| Ollama | `ollama run parable/granite4.1-fable:3b` · [parable namespace](https://ollama.com/parable) |
| Hugging Face | [full collection](https://huggingface.co/collections/AnkitAI/parable-6a4fac60f4b35afca3019621) |
| LM Studio | search "parable" in-app |
| ModelScope | [Parable on ModelScope](https://modelscope.cn/models/AnkitAI/Parable-Granite-4.1-3B-Claude-Fable-5-GGUF) |

## 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](https://doi.org/10.5281/zenodo.21676407)

```bibtex
@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](https://huggingface.co/Glint-Research) & [Roman1111111](https://huggingface.co/Roman1111111) for the open trace data · [IBM Granite](https://huggingface.co/ibm-granite) for the base · [empero-ai](https://huggingface.co/empero-ai) whose Qwable recipe inspired the series · [llama.cpp](https://github.com/ggml-org/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.

More on the Parable models: [ankitaglawe.com/parable](https://ankitaglawe.com/parable)
