Instructions to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA

SGLang

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA 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 "TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA" \
    --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": "TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA",
		"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 "TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA" \
        --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": "TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA 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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA 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 TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA",
    max_seq_length=2048,
)

Docker Model Runner
How to use TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA with Docker Model Runner:
```
docker model run hf.co/TeichAI/Qwen3.6-27B-Fable-5-Experimental-LoRA
```

Qwen 3.6 27B - Claude Fable 5 (Experimental)

Heres Qwen3.6 slightly over-trained on a very small dataset of fable 5 traces. I finished this tune yesterday morning, realized it's good for planning but, like all fable 5 tunes right now, regressed on some tasks due to the small amount of data.

Had to use aggressive settings for the style transfer here due to the data contraints. Overall the model seems to be a better planner now and better at 3D modeling in three.js, making small games, and ML engineering.

Either way give it a shot, it really does look, talk, and plan like fable... but like all fine-tunes it comes with it's limitations.

Hopefully we can get some more well rounded data from the rest of the community to a do a much less aggressive tune on a larger dataset.

Reasoning was left untouched

Benchmarks

As always big thanks to @nightmedia for the speedy benchmarks.

                                 arc     arc/e	boolq
Qwen3.6-27B-Fable-5-Experimental 0.650	0.813	0.909
                     Qwen3.6-27B	0.637	0.798	0.911

Not really a benchmark but here's a procedurally generated duck that it zero-shotted lol (quant: q3_k_s)