AnkitAI

Parable-Granite-4.1-8B-Claude-Fable-5

README

License: apache-2.0

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "AnkitAI/Parable-Granite-4.1-8B-Claude-Fable-5",
    torch_dtype="auto", device_map="auto")
tok = AutoTokenizer.from_pretrained("AnkitAI/Parable-Granite-4.1-8B-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-Granite-4.1-8B-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 evals across the Parable family

Held-out test split, identical evaluation code and context length for base and fine-tune:

Table with columns: Metric, Base Granite-4.1-8B, Parable, Δ
Metric	Base Granite-4.1-8B	Parable	Δ
Test loss	2.030	0.617	−70%

Qualitative review (34 coding/terminal/debugging prompts, strictly graded by mentally executing every answer): 20/34 fully correct, 32/34 correct or partially correct. 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 Granite-4.1-8B'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 Granite-4.1-8B). 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

Table with columns: Platform, Command / Link
Platform	Command / Link
Ollama	`ollama run parable/granite4.1-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/granite4.1-fable` (parable on LM Studio Hub)

Acknowledgements

Glint-Research and Roman1111111 for the open trace datasets
IBM Granite for the base model
empero-ai, whose Qwable recipe the Parable series follows
llama.cpp

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Model Details

Model Provider

AnkitAI

Model Tree

Base

ibm-granite/granite-4.1-8b

Fine-tuned

this model

Input Modalities

Text

Output Modalities