Part of the Parable series: small local LLMs fine-tuned on genuine agent traces. This is HuggingFaceTB/SmolLM3-3B tuned on real Claude Fable 5 agent transcripts so its step-by-step reasoning voice carries into local use.

Quantized GGUF builds for llama.cpp / LM Studio / Ollama: Parable-SmolLM3-3B-Claude-Fable-5-GGUF

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "AnkitAI/Parable-SmolLM3-3B-Claude-Fable-5"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

msgs = [{"role": "user", "content": "Write a python function that reverses a string."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=400, temperature=0.6)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Output begins with a <think>...</think> reasoning block, then the answer. Parse and strip the think block before showing text to end users. The chat template identifies the model as "Parable, a coding assistant that reasons before it answers."

Model details

Base: HuggingFaceTB/SmolLM3-3B (3B, Apache-2.0, 64k context)
Method: MLX QLoRA on a 4-bit quantized base, 16 layers adapted, 6.7M trainable parameters (0.218%); this repo holds the dequantized F16 merge as safetensors
Data: 4,076 training rows from real Claude Fable 5 agent-session traces plus gpt5.5-terminal transcripts, prepared at a 4,096-token window (268 over-length rows dropped; 226/226 rows held out for validation/test)
Schedule: 1,200-iteration budget across a paused-and-resumed run; best checkpoint selected on validation loss (iteration 200 of the final segment, val 1.154)

Evaluation

	Held-out trace test loss
SmolLM3-3B base	1.889
This model	1.115

The tuned model fits the Fable-5 reasoning distribution 41% better by held-out loss on a 226-row test split never seen in training. That is the honest headline for what this fine-tune does; we do not claim general benchmark gains.

This lane trains on trace data without a replay mix, so impact on general coding benchmarks is unmeasured here. The series' technical report (DOI: 10.5281/zenodo.21676407) documents why that matters and what replay does about it.

Limitations

Training ran on a 4-bit quantized base (16 GB M1 constraint); the F16 merge cannot exceed 4-bit-base quality.
Modest scale: one seed, loss-based evaluation, no external benchmark run for this model yet.
Not trained for: multi-file repo navigation, vision, non-English.
Inherits SmolLM3-3B's knowledge cutoff. Treat generated commands as drafts to review.

Provenance & licensing

Fine-tuned from HuggingFaceTB/SmolLM3-3B (Apache-2.0). Training data: Glint-Research/Fable-5-traces (AGPL-3.0) and Roman1111111/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.

Citation

bibtex

@misc{aglawe2026parable,
  author = {Aglawe, Ankit},
  title  = {Agent-Trace Fine-Tuning of Small Language Models under Constrained Compute},
  year   = {2026},
  doi    = {10.5281/zenodo.21676407},
  url    = {https://doi.org/10.5281/zenodo.21676407}
}

Acknowledgements

The SmolLM3 team at Hugging Face for the base model; Glint-Research and Roman1111111 for the trace datasets; empero-ai for the recipe this series iterates on.