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
| from __future__ import annotations | |
| import json | |
| from pathlib import Path | |
| from typing import Any | |
| from transformers import PreTrainedTokenizerFast | |
| class LizzyTokenizerFast(PreTrainedTokenizerFast): | |
| """Family-agnostic fast tokenizer wrapper for Lizzy checkpoints.""" | |
| model_input_names = ["input_ids", "attention_mask"] | |
| def __init__(self, *args: Any, **kwargs: Any) -> None: | |
| preserved_keys = ( | |
| "add_prefix_space", | |
| "add_bos_token", | |
| "add_eos_token", | |
| "clean_up_tokenization_spaces", | |
| "use_default_system_prompt", | |
| "legacy", | |
| "fix_mistral_regex", | |
| ) | |
| preserved_init_attrs = { | |
| key: kwargs.get(key) | |
| for key in preserved_keys | |
| if key in kwargs | |
| } | |
| super().__init__(*args, **kwargs) | |
| init_kwargs = getattr(self, "init_kwargs", {}) | |
| local_payload: dict[str, Any] = {} | |
| config_path = ( | |
| Path(str(getattr(self, "name_or_path", ""))) / "tokenizer_config.json" | |
| ) | |
| if config_path.is_file(): | |
| try: | |
| local_payload = json.loads(config_path.read_text(encoding="utf-8")) | |
| except Exception: | |
| local_payload = {} | |
| for key in preserved_keys: | |
| value = preserved_init_attrs.get(key, init_kwargs.get(key)) | |
| if value is None: | |
| value = local_payload.get(key) | |
| if value is not None: | |
| setattr(self, key, value) | |
| def all_special_tokens_extended(self) -> list[str]: | |
| """Compatibility shim for runtimes still expecting the pre-5.4 API.""" | |
| return list(self.all_special_tokens) | |