| import outlines | |
| import os | |
| from pydantic import BaseModel, Field | |
| from transformers import AutoTokenizer | |
| model_str = 'Qwen/Qwen2.5-7B-Instruct-1M' | |
| model = outlines.models.transformers( | |
| model_str, | |
| device='cuda' | |
| ) |
| import time | |
| from typing import Literal | |
| import outlines | |
| import re | |
| import torch | |
| from transformers import AutoTokenizer | |
| from outlines.fsm.json_schema import convert_json_schema_to_str | |
| from outlines_core.fsm.json_schema import build_regex_from_schema | |
| from pydantic import BaseModel |
| UnderstandingResponse( | |
| understanding=Understanding( | |
| chain_of_thought=[ | |
| "The user profile belongs to akhilrao.bsky.social, with a display name of 'grand theft eigenvalue 🔆'. Their description indicates that they are an economist and that it's a personal account, including their pronouns | |
| as 'he/him'.", | |
| "Their first post is about the Mobius YIMBY arc, a topic related to economics and possibly linked to geographical development or real estate markets, based on the acronym 'YIMBY' (Yes In My Back Yard), usually | |
| associated with urban development. The term 'Mobius', referencing a strip with no boundaries, might metaphorically suggest infinite or unexplored opportunities or issues in the economic landscape.", | |
| "The second post comments on the affordability and proliferation of LEO (Low Earth Orbit) satellites, critiquing the notion that because they are inexpensive, it's acceptable to deploy them en masse without concern | |
| for their potential environmental and spatial clut |
| import outlines | |
| from transformers import AutoTokenizer | |
| model_string = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-7B' | |
| # model_string = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B' # For small machines | |
| model = outlines.models.transformers( | |
| model_string, | |
| device='cuda', # also 'cpu', 'mps','auto' | |
| ) |
| import outlines | |
| from pydantic import BaseModel | |
| from transformers import AutoTokenizer | |
| from rich import print | |
| model_string = "HuggingFaceTB/SmolLM2-1.7B-Instruct" | |
| model = outlines.models.transformers(model_string) | |
| tokenizer = AutoTokenizer.from_pretrained(model_string) | |
| class Highlights(BaseModel): |
| """ | |
| pip install outlines torch==2.4.0 transformers accelerate pillow rich | |
| sudo apt-get install poppler-utils | |
| """ | |
| from enum import Enum | |
| from PIL import Image | |
| import outlines | |
| import torch |
!!! note This example was adapted from Morgan Giraud on our Discord. You can find their twitter here. Thank you Morgan!
Outlines provides experimental support for context-free grammars (CFGs) for text generation. Future versions will provide more comprehensive support for structured outputs.
SQL is a context-free language, meaning that the structure of the query is independent of the content.
| """ | |
| pip install outlines torch==2.4.0 transformers accelerate typing-extensions pillow pdf2image rich requests | |
| may need to install tkinter: https://stackoverflow.com/questions/25905540/importerror-no-module-named-tkinter | |
| sudo apt-get install poppler-utils | |
| """ | |
| from enum import Enum | |
| from io import BytesIO |
| import outlines | |
| sql_grammar = """ | |
| start: set_expr -> final | |
| set_expr: query_expr | |
| | set_expr "UNION"i ["DISTINCT"i] set_expr -> union_distinct | |
| | set_expr "UNION"i "ALL"i set_expr -> union_all | |
| | set_expr "INTERSECT"i ["DISTINCT"i] set_expr -> intersect_distinct | |
| | set_expr "EXCEPT"i ["DISTINCT"i] set_expr -> except_distinct |
| import outlines | |
| from transformers import BitsAndBytesConfig | |
| # Load the model | |
| model = outlines.models.transformers( | |
| "microsoft/Phi-3-mini-4k-instruct", | |
| model_kwargs={ | |
| 'quantization_config':BitsAndBytesConfig( | |
| # Load the model in 4-bit mode | |
| load_in_4bit=True, |