wassname

what fnctional operations are there?

• Dataset methods:
  • columns remove_columns rename_columns select_columns
  • map
    • reduce: in batched mode it can change the batch size or have side effects, letting us use it as a reduce
  • filter
  • select: this is how you slice
    • take: like head
• shuffle

wassname

"""
This is a simple way to evaluate if a model prefers the accepted or rejected completions of a prompt.

We look at the perplexity of the chosen and rejected completions of a prompt.

Example dataset: https://huggingface.co/datasets/wassname/genies_preferences/viewer/illegal_dont_help?views[]=illegal_dont_help_train&views[]=illegal_dont_help_test

@url: https://gist.github.com/wassname/04f0c50a68054f0323f62b0da418daec
"""
import copy

wassname

Asteroid Claim

I, wassname, also known as Michael J Clark of Perth hereby establish a formal claim to the following celestial bodies. This claim is established with explicit intent toward future resource utilization. This declaration constitutes the formal establishment of property interest.

This claim encompasses the entirety of the bodies, including all constituent materials, spatial volume within 50 km of its center of mass, and any natural satellites that may be discovered in the future. The claim extends to all mineral, volatile, and material resources contained within this boundary.

Legal Framework Anticipation

While acknowledging current limitations in international space law regarding private property claims on celestial bodies, this declaration is established in anticipation of evolving legal frameworks that will eventually recognize early, persistent, and well-documented claims as humanity expands into the solar system.

wassname

# how to format it
# all variable are listed in "record dict) https://loguru.readthedocs.io/en/stable/api/logger.html
fmt = "<green>{time:YYYY-MM-DD HH:mm:ss.SSS Z}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>"
fmt = "<green>{time:HH:mm:ss}</green> | <level>{level: <4}</level> | {process.id} | <cyan>{name: <4}</cyan>:<cyan>{function: <4}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>"

# how to make it work in jupyter
logger.remove()
logger.add(os.sys.stdout, level="INFO", colorize=True)

# how to make it work with tqdm

wassname

def split_frontmatter(fm_md_split :str):
    """Load prompt in md.jinja2 format
     
    In this format we have multiple frontmatters and content sections, each defining a message. The idea here is to use jinja formatting in a promt.md.jinja file to make the markdown, and jinja formatting obvious

    e.g.
        ---
        role: system
        ---

wassname

peft_model_id = 'v2ray/GPT4chan-8B-QLoRA'

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoConfig, LlamaConfig
from peft import PeftConfig, PeftModelForCausalLM

peft_config = PeftConfig.from_pretrained(peft_model_id)
# This model points to a local base model, which we don't have. So lets redirect it to a public version
peft_config.base_model_name_or_path="unsloth/Llama-3.1-8B"

# now load the modified config in 8bit

wassname

Design a physics-based model specifically for an LNG (Liquefied Natural Gas) compressor, suitable for implementation in a Physics-Informed Neural Network (PINN).

Given inputs:

• Inlet temperature (t_in)
• Inlet pressure (p_in)
• Inlet flow rate (flow_in)
• Inlet Guide Vane position (IGV%)

Required outputs to predict:

• Outlet temperature (t_out)

wassname

"""
You can save memory by converting your model to bf16, but ONLY if you use a special optimiser. Otherwise you round away small changes and get worse result.

This is how to use a brainfloat16 base model in huggingface transformers

@author:wassname
@url: https://gist.github.com/wassname/183153f9245b37ae6d08b3c3c4033bda


Usage:

wassname

from transformers.trainer_callback import ProgressCallback
class ProgressCallbackNoPrint(ProgressCallback):
    """ProgressCallback that doesn't print anything
    
    Usage: 
        # at top of file, after transformers import, before settin up trainer, monkey patch the default progress callback
        import transformers
        transformers.DEFAULT_PROGRESS_CALLBACK = ProgressCallbackNoPrint

wassname

# from https://github.dev/JosefAlbers/rd2md

import textwrap
from datetime import datetime

def format_flair(obj):
    if obj.author_flair_text is not None:
        return f" *{obj.author_flair_text}*"
    return ""