joseph-crowley

Below is a compressed yet complete reference for quickly integrating each shadcn component. Assumption: you already have the files from your question in @/components/ui/*.tsx and can import them directly. All components accept typical React props plus any Radix/3rd-party props. Adjust styling and props as needed.Do not rewrite any of the code for the shadcn components.

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1. Accordion

Import

import {
  Accordion,
  AccordionItem,

kalomaze

# the "verifiers" repository is a clean implementation of templated GRPO reinforcement learning training environments
# this is a generic set of "install from scratch" commands complete with a deepspeed z3 config that i have been using when i spin up nodes
# it will run on the gsm8k example w/ default batch size & generation size (8), and the 8th GPU is used for vllm generations
# qwen 14b full finetuning will run on this configuration too without LoRA or CUDA OOM, at least for the gsm8k task's context sizes + generation lengths
# hyperparameters are controlled by `verifiers/utils/config_utils.py`; i have been preferring extreme grad clipping (between 0.001 and 0.01) and low beta (under 0.01)

# NOTE FEB 27: examples have moved into `verifiers/examples` not `/examples`

cd /root
mkdir boom

sayakpaul

from google import genai
from google.genai import types
import typing_extensions as typing
from PIL import Image
import requests
import io
import json
import os

qunash

Chillee

import torch
from torch.nn.attention.flex_attention import create_block_mask, flex_attention
torch.set_default_device('cuda')

q, k, v = [torch.randn(8, 8, 1024, 64, requires_grad=True) for _ in range(3)]

causal_mask = create_block_mask(lambda b, h, q_idx, kv_idx: q_idx >= kv_idx, None, None, 1024, 1024)
uncausal_mask = create_block_mask(lambda b, h, q_idx, kv_idx: q_idx < kv_idx, None, None, 1024, 1024)
ref_out = flex_attention(q, k, v)
causal_out, causal_lse = flex_attention(q, k, v, block_mask=causal_mask, return_lse=True)

willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer

cpfiffer

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'
)

philschmid

from typing import Dict, Union
from huggingface_hub import get_safetensors_metadata
import argparse
import sys

# Example: 
# python get_gpu_memory.py Qwen/Qwen2.5-7B-Instruct

# Dictionary mapping dtype strings to their byte sizes
bytes_per_dtype: Dict[str, float] = {

cpfiffer

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):

dwarkeshsp

#!/usr/bin/env python3
"""
Human quality transcripts from audio files using 
AssemblyAI for transcription and Google's Gemini for enhancement.

Requirements:
- AssemblyAI API key (https://www.assemblyai.com/)
- Google API key (https://aistudio.google.com/)
- Python packages: assemblyai, google-generativeai, pydub