I was interested to learn more about the developer experience of Cloudflare’s D1 serverless SQL database offering. I started with this tutorial. Using wrangler you can scaffold a Worker and create a D1 database. The docs were straightforward up until the Write queries within your Worker section. For me, wrangler scaffolded a worker with a different structure than the docs discuss. I was able to progress through the rest of the tutorial by doing the following:I tried out jsonformer to see how it would perform with some of structured data use cases I’ve been exploring.
Setup python -m venv env . env/bin/activate pip install jsonformer transformers torch Code ⚠️ Running this code will download 10+ GB of model weights ⚠️
from jsonformer import Jsonformer from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b") tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b") json_schema = { "$schema": "http://json-schema.org/draft-07/schema#", "title": "RestaurantReview", "type": "object", "properties": { "review": { "type": "string" }, "sentiment": { "type": "string", "enum": ["UNKNOWN", "POSITIVE", "MILDLY_POSITIVE", "NEGATIVE", "MILDLY_NEGATIVE"] }, "likes": { "type": "array", "items": { "type": "string" } }, "dislikes": { "type": "array", "items": { "type": "string" } } }, "required": ["review", "sentiment"] } prompt = """From the provided restaurant review, respond with JSON adhering to the schema.I’ve been keeping an eye out for language models that can run locally so that I can use them on personal data sets for tasks like summarization and knowledge retrieval without sending all my data up to someone else’s cloud. Anthony sent me a link to a Twitter thread about product called deepsparse by Neural Magic that claims to offer
[a]n inference runtime offering GPU-class performance on CPUs and APIs to integrate ML into your application