Instructions to use garethpaul/gpt-oss-20b-fableflux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use garethpaul/gpt-oss-20b-fableflux with Transformers:

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

pipe = pipeline("text-generation", model="garethpaul/gpt-oss-20b-fableflux")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("garethpaul/gpt-oss-20b-fableflux")
model = AutoModelForCausalLM.from_pretrained("garethpaul/gpt-oss-20b-fableflux")
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 garethpaul/gpt-oss-20b-fableflux with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "garethpaul/gpt-oss-20b-fableflux"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "garethpaul/gpt-oss-20b-fableflux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/garethpaul/gpt-oss-20b-fableflux

SGLang

How to use garethpaul/gpt-oss-20b-fableflux 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 "garethpaul/gpt-oss-20b-fableflux" \
    --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": "garethpaul/gpt-oss-20b-fableflux",
		"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 "garethpaul/gpt-oss-20b-fableflux" \
        --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": "garethpaul/gpt-oss-20b-fableflux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use garethpaul/gpt-oss-20b-fableflux with Docker Model Runner:
```
docker model run hf.co/garethpaul/gpt-oss-20b-fableflux
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🪄 GPT-OSS 20B — FableFlux (MXFP4)

This is a merged and re-exported version of gpt-oss-20b-children-qlora,
fine-tuned on garethpaul/children-stories-dataset to generate structured JSON bedtime stories.

Base model: openai/gpt-oss-20b
Format: MXFP4 quantized (safetensors)
Context length: 8192 tokens
License: MIT
Author: @garethpaul

✨ What it does

Produces structured JSON outputs in the form:

{
  "title": "string",
  "characters": ["string"],
  "setting": "string",
  "story": "string (500–800 words, bedtime tone, positive ending)",
  "moral": "string"
}

🚀 Usage

Transformers (CPU/GPU)

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "garethpaul/gpt-oss-20b-fableflux"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16"
)

messages = [
    {"role": "system", "content": "You are StoryWeaver. Always respond in valid JSON with keys: {title, characters, setting, story, moral}."},
    {"role": "user", "content": "Tell me a bedtime story about a brave little car."}
]

prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=700, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

vLLM (recommended for serving)

pip install vllm==0.10.1+gptoss --extra-index-url https://wheels.vllm.ai/gpt-oss/

vllm serve garethpaul/gpt-oss-20b-fableflux \
  --max-model-len 8192 \
  --tensor-parallel-size 1

Then query with the OpenAI API format:

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")

resp = client.chat.completions.create(
    model="garethpaul/gpt-oss-20b-fableflux",
    messages=[
        {"role": "system", "content": "You are StoryWeaver. Respond ONLY in JSON."},
        {"role": "user", "content": "Tell me a bedtime story about a ballet dancer named Jones."}
    ]
)
print(resp.choices[0].message["content"])

🛠 Training Details

Method: QLoRA → merged → MXFP4 re-export

Dataset: garethpaul/children-stories-dataset

LoRA config: rank=8, α=16, dropout=0.05

Frameworks: transformers, peft, trl

Merged to: BF16 → MXFP4 (vLLM-compatible safetensors)

📚 Related

openai/gpt-oss-20b — base model

garethpaul/gpt-oss-20b-children-qlora — adapter repo

garethpaul/children-stories-dataset — training datas

Downloads last month: 8

Safetensors

Model size

21B params

Tensor type

BF16

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