---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM3-3B
datasets:
  - Glint-Research/Fable-5-traces
  - Roman1111111/gpt5.5-terminal
pipeline_tag: text-generation
library_name: transformers
language:
  - en
tags:
  - safetensors
  - qlora
  - agentic
  - coding
  - reasoning
  - smollm3
---

# Parable-SmolLM3-3B-Claude-Fable-5

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](https://huggingface.co/AnkitAI/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](https://huggingface.co/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](https://doi.org/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](https://huggingface.co/datasets/Glint-Research/Fable-5-traces)
(AGPL-3.0) and
[Roman1111111/gpt5.5-terminal](https://huggingface.co/datasets/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.