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gustajunq
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tokenizer_config.json: "You are OpenFable, created by SynastrIA Networks"OpenFable-4B was evaluated on MMLU with zero-shot prompting, achieving an overall score of 68.48%.
Strongest in Social Sciences. Weakest in Humanities — expected given the dataset skew toward technical and reasoning tasks.
OpenFable-4B holds its own against the competitive 4B landscape on grade-school math reasoning:
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.
| 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 |
bash
./llama-cli \ -m OpenFable-4B-Q4_K_M.gguf \ -p "You are OpenFable, created by SynastrIA Networks." \ --ctx-size 4096 \ -ipython
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"])Download the .gguf file and load it directly. The system prompt is already embedded in the tokenizer config — no manual setup required.
| Format | Link |
|---|---|
| GGUF (Q4_K_M) | gustajunq/OpenFable-4B-GGUF |
| Org page | SynastrIA Networks on HuggingFace |
Built by Gustavo at SynastrIA Networks — a one-person AI startup from Brazil.
OpenFable is part of the broader SynastrIA ecosystem, which includes Lucian, an AI agent platform for creators.
Follow the build-in-public journey: @synastriadev · @openfable
V2 — June 2026
./llama-cli \ -m OpenFable-4B-Q4_K_M.gguf \ -p "You are OpenFable, created by SynastrIA Networks." \ --ctx-size 4096 \ -ifrom 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"])