Text Generation
Transformers
Safetensors
English
lizzy
lizzy-7b
flwrlabs
british-english
conversational
custom_code
4-bit precision
paroquant
Instructions to use Jeethu/Lizzy-7B-PARO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,272 Bytes
3fa47da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | #!/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()
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