README.md

---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---

# OpenFable-4B

> *"The US banned Fable 5 outside America. I'm outside America. So I made my own."*

**OpenFable-4B** is a fine-tune of [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) designed to replicate the conversational style, reasoning depth, and structured output quality of Claude Fable 5 — built entirely from scratch by a solo developer in Brazil.

This is not a generic instruction-tuned model. It's a deliberate attempt to bring Fable-style responses to the open-source ecosystem, with a custom-built dataset and a personality baked into the chat template.

---

## What makes it different

- **Style-first fine-tune** — trained to match Claude Fable 5's tone: direct, warm, structured, and non-verbose
- **Custom dataset** — ~300 hand-curated examples across coding, math, agentic planning, and cybersecurity. No public synthetic datasets that leak CoT preambles
- **Custom chat template** — default system prompt embedded in `tokenizer_config.json`: *"You are OpenFable, created by SynastrIA Networks"*
- **GGUF quantized** — Q4_K_M, ready for local inference via llama.cpp, LM Studio, PocketPal, or Jan

---

## Benchmarks

### MMLU — Zero-shot (no few-shot)

OpenFable-4B was evaluated on MMLU with zero-shot prompting, achieving an overall score of **68.48%**.

![OpenFable MMLU Benchmark](benchmark_mmlu.png)

Strongest in Social Sciences. Weakest in Humanities — expected given the dataset skew toward technical and reasoning tasks.

---

### GSM8K — Comparison with 4B-class models

OpenFable-4B holds its own against the competitive 4B landscape on grade-school math reasoning:

![4B Models GSM8K Benchmark](benchmark_gsm8k.png)

OpenFable matches top-tier 4B models on math reasoning despite being a style fine-tune, not a reasoning-optimized model. The base Qwen3-4B it's built on scores ~76% — OpenFable closes that gap significantly through LoRA training.

---

## Model details

| Property | Value |
|---|---|
| Base model | Qwen/Qwen3-4B |
| Fine-tuning method | LoRA (via Unsloth) |
| Dataset size | ~300 examples |
| Quantization | Q4_K_M (GGUF) |
| Context length | 32768 |
| Language | English |
| License | Apache 2.0 |

---

## Usage

### llama.cpp

```bash
./llama-cli \
  -m OpenFable-4B-Q4_K_M.gguf \
  -p "You are OpenFable, created by SynastrIA Networks." \
  --ctx-size 4096 \
  -i
```

### Python (llama-cpp-python)

```python
from llama_cpp import Llama

llm = Llama(
    model_path="OpenFable-4B-Q4_K_M.gguf",
    n_ctx=4096,
    chat_format="chatml",
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are OpenFable, created by SynastrIA Networks."},
        {"role": "user", "content": "Explain how LoRA fine-tuning works."},
    ]
)

print(response["choices"][0]["message"]["content"])
```

### LM Studio / Jan / PocketPal

Download the `.gguf` file and load it directly. The system prompt is already embedded in the tokenizer config — no manual setup required.

---

## Downloads

| Format | Link |
|---|---|
| GGUF (Q4_K_M) | [gustajunq/OpenFable-4B-GGUF](https://huggingface.co/gustajunq/OpenFable-4B-GGUF) |
| Org page | [SynastrIA Networks on HuggingFace](https://huggingface.co/SynastrIA-Networks) |

---

## Known limitations

- Humanities performance lags behind other categories (~59.5% MMLU) — reflective of dataset composition
- Style fine-tune, not RLHF-aligned — may occasionally drift on edge-case prompts
- Not optimized for multilingual use — English only

---

## About

Built by [Gustavo](https://huggingface.co/gustajunq) at [SynastrIA Networks](https://huggingface.co/SynastrIA-Networks) — a one-person AI startup from Brazil.

OpenFable is part of the broader SynastrIA ecosystem, which includes [Lucian](https://github.com/synastriadev), an AI agent platform for creators.

Follow the build-in-public journey: [@synastriadev](https://tiktok.com/@synastriadev) · [@openfable](https://tiktok.com/@openfable)

---

*V2 — June 2026*