Instructions to use flwrlabs/Lizzy-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flwrlabs/Lizzy-7B with Transformers:

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

pipe = pipeline("text-generation", model="flwrlabs/Lizzy-7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("flwrlabs/Lizzy-7B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use flwrlabs/Lizzy-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "flwrlabs/Lizzy-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "flwrlabs/Lizzy-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/flwrlabs/Lizzy-7B

SGLang

How to use flwrlabs/Lizzy-7B 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 "flwrlabs/Lizzy-7B" \
    --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": "flwrlabs/Lizzy-7B",
		"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 "flwrlabs/Lizzy-7B" \
        --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": "flwrlabs/Lizzy-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use flwrlabs/Lizzy-7B with Docker Model Runner:
```
docker model run hf.co/flwrlabs/Lizzy-7B
```

Lizzy-7B / quickstart.py

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Initial commit

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raw

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	#!/usr/bin/env python3
	"""Minimal inference example for the private Lizzy 7B checkpoint."""

	from __future__ import annotations

	import os


	def main() -> None:
	repo_id = os.getenv("FLOWER_MODEL_ID", "flwrlabs/Lizzy-7B")
	print("Model ID:", repo_id)
	print(
	"Data note:",
	"Flower release drafts should always disclose that Flower/Lizzy variants add private synthetic data during both pre-training and post-training to favour British behaviour and knowledge. Those private synthetic datasets are not redistributed in the release pack.",
	)
	print("HF_TOKEN present:", bool(os.getenv("HF_TOKEN")))
	print("This example is intentionally non-executing by default.")
	print("Use one of the snippets below after installing transformers or vLLM:")
	print()
	print("Transformers:")
	print(
	" tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)"
	)
	print(
	" model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True, torch_dtype='auto')"
	)
	print()
	print("vLLM:")
	print(
	" python -m vllm.entrypoints.openai.api_server --model "
	"flwrlabs/Lizzy-7B --trust-remote-code --max-model-len 8192"
	)


	if __name__ == "__main__":
	main()