Instructions to use SynastriaNetworks/OpenFable-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SynastriaNetworks/OpenFable-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SynastriaNetworks/OpenFable-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SynastriaNetworks/OpenFable-4B")
model = AutoModelForCausalLM.from_pretrained("SynastriaNetworks/OpenFable-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SynastriaNetworks/OpenFable-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SynastriaNetworks/OpenFable-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SynastriaNetworks/OpenFable-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SynastriaNetworks/OpenFable-4B

SGLang

How to use SynastriaNetworks/OpenFable-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SynastriaNetworks/OpenFable-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SynastriaNetworks/OpenFable-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SynastriaNetworks/OpenFable-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SynastriaNetworks/OpenFable-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use SynastriaNetworks/OpenFable-4B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SynastriaNetworks/OpenFable-4B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SynastriaNetworks/OpenFable-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SynastriaNetworks/OpenFable-4B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="SynastriaNetworks/OpenFable-4B",
    max_seq_length=2048,
)

Docker Model Runner
How to use SynastriaNetworks/OpenFable-4B with Docker Model Runner:
```
docker model run hf.co/SynastriaNetworks/OpenFable-4B
```

OpenFable-4B / README.md

gustajunq

Update README.md

38614c8 verified 4 days ago

preview code

Raw

History Blame Contribute Delete

4.13 kB

metadata

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 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%.

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:

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

./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)

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
Org page	SynastrIA Networks on HuggingFace

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 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