Instructions to use sdavies/globe-theatre-qwen25-3b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sdavies/globe-theatre-qwen25-3b-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "sdavies/globe-theatre-qwen25-3b-lora")

Transformers

How to use sdavies/globe-theatre-qwen25-3b-lora with Transformers:

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

pipe = pipeline("text-generation", model="sdavies/globe-theatre-qwen25-3b-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sdavies/globe-theatre-qwen25-3b-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sdavies/globe-theatre-qwen25-3b-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sdavies/globe-theatre-qwen25-3b-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": "sdavies/globe-theatre-qwen25-3b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sdavies/globe-theatre-qwen25-3b-lora

SGLang

How to use sdavies/globe-theatre-qwen25-3b-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 "sdavies/globe-theatre-qwen25-3b-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": "sdavies/globe-theatre-qwen25-3b-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 "sdavies/globe-theatre-qwen25-3b-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": "sdavies/globe-theatre-qwen25-3b-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sdavies/globe-theatre-qwen25-3b-lora with Docker Model Runner:
```
docker model run hf.co/sdavies/globe-theatre-qwen25-3b-lora
```

🎭 The Tragedy of the Group Chat: LoRA Adapter

A LoRA adapter for Qwen2.5-3B-Instruct that transforms everyday modern inconveniences into short theatrical comedy scenes.

This model was created for the Hugging Face Build-Small Hackathon (June 2026) as an experiment in small-model style fine-tuning.

Rather than building a general-purpose assistant, the goal was to teach a relatively small (3B parameter) model a highly specific comedic voice using a compact hand-written training corpus and iterative evaluation.

The project explores how much personality and structure can be taught to a local model without relying on external APIs or retrieval systems.

What does it do?

Give the model a small modern annoyance:

My neighbour's cat judged me through the window.

The bakery sold out of the exact pastry I had emotionally chosen.

The automatic lights turned off while I was still in the shower.

…and it will produce a short comic scene in the style of a badly organised Elizabethan theatre company.

Typical outputs include:

TITLE
DRAMATIS PERSONAE
SCENE
THOU MUST CHOOSE

Style

The intended voice combines influences from:

Shakespeare
Blackadder
Monty Python
British sitcoms
Amateur dramatic societies

The goal is not historical accuracy, but readable modern dialogue with theatrical overreaction.

Training

This adapter was fine-tuned using LoRA with supervised fine-tuning (SFT) on a hand-curated dataset of approximately 240 examples.

The dataset was iteratively refined over multiple training runs to improve:

structural consistency
role selection
relevance to the original grievance
comedic escalation
dialogue quality

On a 10-prompt held-out manual evaluation, the fine-tuned model scored 56/80, compared with 36/80 for the base Qwen2.5-3B-Instruct model using the same prompt and scoring rubric.

Design principles

The model was trained around a few simple ideas:

Take the grievance seriously.
Make the reaction wildly disproportionate.
Keep the cast reasonably small and situation-specific.
Let the comedy emerge from the original complaint.
Never solve the problem sensibly.

Intended use

The model works best for:

everyday annoyances
social awkwardness
household disasters
transport failures
office politics
mildly haunted appliances
inexplicably judgemental animals

It is intended for entertainment and creative text generation.

Limitations

This model intentionally prioritises style over factual accuracy.

Recurring characters and running jokes are expected behaviour.

The model performs best on small everyday frustrations rather than major life events.

Project structure

The project consists of three related repositories:

LoRA adapter (this repository): The original PEFT fine-tuning.
Merged model: A standalone Transformers version of the model.
GGUF edition: A quantised deployment build for llama.cpp and local inference.

Quick start

This repository contains a PEFT LoRA adapter and should be loaded on top of Qwen2.5-3B-Instruct.

About the project

The objective was not to build a general-purpose assistant, but a small theatrical comedy troupe capable of treating everyday inconveniences as matters of grave national importance.

Training procedure

This model was trained with supervised fine-tuning (SFT) using TRL and PEFT.

Framework versions

PEFT 0.19.1
TRL 1.6.0
Transformers 5.10.2
PyTorch 2.11.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Try the model

🎭 Interactive demo

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