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

Libraries

How to use Jeethu/Lizzy-7B-PARO with Transformers:

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jeethu/Lizzy-7B-PARO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Jeethu/Lizzy-7B-PARO

SGLang

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

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

Lizzy-7B-PARO

File size: 932 Bytes

3fa47da

#!/usr/bin/env bash
set -euo pipefail

MODEL_ID="${FLOWER_MODEL_ID:-flwrlabs/Lizzy-7B}"

echo "HF_TOKEN set: ${HF_TOKEN:+yes}"
echo "Model: $MODEL_ID"
echo "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."
echo
echo "Transformers example:"
echo "python - <<'PY'"
echo "from transformers import AutoTokenizer, AutoModelForCausalLM"
echo "repo_id = 'flwrlabs/Lizzy-7B'"
echo "tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)"
echo "model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True, torch_dtype='auto')"
echo "PY"
echo
echo "vLLM serve example:"
echo "python -m vllm.entrypoints.openai.api_server --model $MODEL_ID --trust-remote-code --max-model-len 8192"