• Telegram Bot, OpenAI, and Tinygrad

  • 2024/11/26
  • 再生時間： 3 分
  • ポッドキャスト

Telegram Bot, OpenAI, and Tinygrad

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  • サマリー

  • Welcome to The Compiler, a daily curation of tech news

    TOOLS YOU'LL ACTUALLY USE

    python-telegram-bot
    Comprehensive Python wrapper for the Telegram Bot API
    pip install python-telegram-bot
    Stars: 23.4k, Last commit: 1 day ago

    nexa-sdk
    Toolkit for GGML and ONNX models supporting text/image generation, VLM, ASR, and TTS
    pip install nexa-sdk
    Stars: 62, Last commit: 2 days ago

    openai-python
    Official Python library for the OpenAI API
    pip install openai
    Stars: 14.7k, Last commit: 4 days ago

    REPO OF THE WEEK

    tinygrad
    Lightweight deep learning framework in pure Python
    Key feature: Implements autograd and neural networks from scratch
    Recent changes: Added support for Apple Silicon, improved CUDA backend

    # Simple neural network in tinygrad from tinygrad.tensor import Tensor import tinygrad.nn.optim as optim class TinyNet: def __init__(self): self.l1 = Tensor.uniform(784, 128) self.l2 = Tensor.uniform(128, 10) def forward(self, x): return x.dot(self.l1).relu().dot(self.l2).logsoftmax() model = TinyNet() optim = optim.SGD([model.l1, model.l2], lr=0.001) # Training loop for _ in range(1000): out = model.forward(x_train) loss = out.mul(y_train).mean() optim.zero_grad() loss.backward() optim.step()

    Thanks for reading!

    Andrew Pierno
    続きを読む 一部表示

あらすじ・解説

Welcome to The Compiler, a daily curation of tech news

TOOLS YOU'LL ACTUALLY USE

python-telegram-bot
Comprehensive Python wrapper for the Telegram Bot API
pip install python-telegram-bot
Stars: 23.4k, Last commit: 1 day ago

nexa-sdk
Toolkit for GGML and ONNX models supporting text/image generation, VLM, ASR, and TTS
pip install nexa-sdk
Stars: 62, Last commit: 2 days ago

openai-python
Official Python library for the OpenAI API
pip install openai
Stars: 14.7k, Last commit: 4 days ago

REPO OF THE WEEK

tinygrad
Lightweight deep learning framework in pure Python
Key feature: Implements autograd and neural networks from scratch
Recent changes: Added support for Apple Silicon, improved CUDA backend

# Simple neural network in tinygrad from tinygrad.tensor import Tensor import tinygrad.nn.optim as optim class TinyNet: def __init__(self): self.l1 = Tensor.uniform(784, 128) self.l2 = Tensor.uniform(128, 10) def forward(self, x): return x.dot(self.l1).relu().dot(self.l2).logsoftmax() model = TinyNet() optim = optim.SGD([model.l1, model.l2], lr=0.001) # Training loop for _ in range(1000): out = model.forward(x_train) loss = out.mul(y_train).mean() optim.zero_grad() loss.backward() optim.step()

Thanks for reading!

Andrew Pierno
続きを読む 一部表示

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