Text Generation
Transformers
Safetensors
English
lizzy
lizzy-7b
flwrlabs
british-english
conversational
custom_code
Instructions to use flwrlabs/Lizzy-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
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edbbb7f | 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 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | 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)
@property
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)
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