hamelsmu

fastcore: An Underrated Python Library

A unique python library that extends the python programming language and provides utilities that enhance productivity.

Sep 1, 2020 • Hamel Husain • 14 min read

__fastcore fastai

Background __

hamelsmu

• claudette

  • Claudette’s source

  • Tool loop

  • The async version

On this page

hamelsmu

• claudette

  • Claudette’s source

  • Tool loop

  • The async version

On this page

hamelsmu

Export Traces

Export to Notebook

See our data API guide for function and argument level information.

After logging data to Arize, if you want to retrieve that data to use in code for evaluation, you can visit the LLM Tracing tab to see your traces and use the export button to get your export code.

hamelsmu

Add Attributes, Metadata and Tags to Span

You might want to track additional application details manually. This is particularly useful for actions outside standard frameworks or LLM clients.

Add attributes to a span

Attributes let you attach key/value pairs to a spans so it carries more information about the current operation that it's tracking.

hamelsmu

Mandatory Packages

The below script will print all the packages modal installs in every image. If these packages cannot be installed, the image will not be built and you will have a bad time.

from modal import Image, App
app = App("01a_modal_requirements")

def see_reqs():

hamelsmu

from fastapi import Request, HTTPException
from pydantic import BaseModel, BaseModel, HttpUrl
from modal import Secret, App, web_endpoint, Image
from typing import Optional, List
from example import proposal
import os

app = App(name="circleback", image=Image.debian_slim().pip_install("openai", "pydantic", "fastapi"))

class Attendee(BaseModel):

hamelsmu

Here is my personal opinion about the questions I posed in this tweet:

---

I think that fine-tuning is still very valuable in many situations. I’ve done some more digging and I find that people who say that fine-tuning isn't useful are indeed often working on products where fine-tuning isn't likely to be useful:

• They are making developer tools - foundation models have been trained extensively on coding tasks.
• They are building foundation models and testing for the most general cases. But the foundation models themselves are also being trained for the most general cases.
• They are building a personal assistant that isn’t scoped to any type of domain or use case and is essentially similar to the same folks building foundation models.

hamelsmu

base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

lora_fan_in_fan_out: false

hamelsmu

{"conversations":[{"from":"system","value":"Honeycomb is an observability platform that allows you to write queries to inspect trace data. You are an assistant that takes a natural language query (NLQ) and a list of valid columns and produce a Honeycomb query."},{"from":"human","value":"\n\nNLQ: \"How many error messages have occurred?\"\n\nColumns: ['error', 'error_count', 'error.type', 'error.msg', 'error.cause_chain', 'email', 'target', 'code.lineno', 'latency', 'id', 'name', 'http.url', 'transaction.id', 'client_reference', 'code.namespace', 'type', 'duration_ms', 'provider', 'account.id', 'internal.payment.missing_in_adapter', 'service.name', 'http.method', 'operation', 'busy_ns', 'idle_ns', 'status_code', 'library.name', 'http.host', 'classification', 'invitation_id', 'thread.id', 'entity_type', 'thread.name', 'library.version', 'code.filepath', 'customer.payment.outbound.missing_in_internal', 'level', 'claim_id', 'parent_name', 'adapter.payment.missing_in_internal', 'net.host.port', 'service.version',