Examples¶
This section contains different example pipelines that showcase different tasks, maybe you can take inspiration from them.
llama.cpp with outlines¶
Generate RPG characters following a pydantic.BaseModel
with outlines
in distilabel
.
See example
This script makes use of LlamaCppLLM
and the structured output capabilities thanks to outlines
to generate RPG characters that adhere to a JSON schema.
It makes use of a local model which can be downlaoded using curl (explained in the script itself), and can be exchanged with other LLMs
like vLLM
.
structured_generation_with_outlines.py
# Copyright 2023-present, Argilla, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from enum import Enum
from pathlib import Path
from distilabel.llms import LlamaCppLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import LoadDataFromDicts
from distilabel.steps.tasks import TextGeneration
from pydantic import BaseModel, StringConstraints, conint
from typing_extensions import Annotated
class Weapon(str, Enum):
sword = "sword"
axe = "axe"
mace = "mace"
spear = "spear"
bow = "bow"
crossbow = "crossbow"
class Armor(str, Enum):
leather = "leather"
chainmail = "chainmail"
plate = "plate"
mithril = "mithril"
class Character(BaseModel):
name: Annotated[str, StringConstraints(max_length=30)]
age: conint(gt=1, lt=3000)
armor: Armor
weapon: Weapon
# Download the model with
# curl -L -o ~/Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q4_K_M.gguf
model_path = "Downloads/openhermes-2.5-mistral-7b.Q4_K_M.gguf"
with Pipeline("RPG-characters") as pipeline:
system_prompt = (
"You are a leading role play gamer. You have seen thousands of different characters and their attributes."
" Please return a JSON object with common attributes of an RPG character."
)
load_dataset = LoadDataFromDicts(
name="load_instructions",
data=[
{
"system_prompt": system_prompt,
"instruction": f"Give me a character description for a {char}",
}
for char in ["dwarf", "elf", "human", "ork"]
],
)
llm = LlamaCppLLM(
model_path=str(Path.home() / model_path), # type: ignore
n_gpu_layers=-1,
n_ctx=1024,
structured_output={"format": "json", "schema": Character},
)
# Change to vLLM as such:
# llm = vLLM(
# model="teknium/OpenHermes-2.5-Mistral-7B",
# extra_kwargs={"tensor_parallel_size": 1},
# structured_output={"format": "json", "schema": Character},
# )
text_generation = TextGeneration(
name="text_generation_rpg",
llm=llm,
input_batch_size=8,
output_mappings={"model_name": "generation_model"},
)
load_dataset >> text_generation
if __name__ == "__main__":
distiset = pipeline.run(
parameters={
text_generation.name: {
"llm": {"generation_kwargs": {"max_new_tokens": 256}}
}
},
use_cache=False,
)
for num, character in enumerate(distiset["default"]["train"]["generation"]):
print(f"Character: {num}")
print(character)
# Character: 0
# {
# "name": "Gimli",
# "age": 42,
# "armor": "plate",
# "weapon": "axe" }
# Character: 1
# {"name":"Gaelen","age":600,"armor":"leather","weapon":"bow"}
# Character: 2
# {"name": "John Smith","age": 35,"armor": "leather","weapon": "sword"}
# Character: 3
# { "name": "Grug", "age": 35, "armor": "leather", "weapon": "axe"}