How can I find the model implementation for a particular model?
- Usually it's avaiable from
transformers/models/xxxx
, e.g., forqwen2_5_vl
#!/bin/bash | |
# === Cache Cleanup Script === | |
# Cleans caches for Homebrew, UV, Hugging Face, and NPM | |
# Usage: bash cleanup.sh [options] | |
# Options: | |
# -a, --all Clean all caches | |
# -b, --brew Clean Homebrew cache | |
# -u, --uv Clean UV cache | |
# -h, --huggingface Clean Hugging Face cache |
How can I find the model implementation for a particular model?
transformers/models/xxxx
, e.g., for qwen2_5_vl
from typing import List, Tuple, Optional, Union, Callable | |
import numpy as np | |
# Define a Tensor type for clarity in this pseudocode | |
Tensor = np.ndarray # In real implementation, this would be a framework-specific tensor type | |
# Qwen2.5-VL Vision Encoder Pseudocode | |
class Qwen25VisionEncoder: | |
def __init__(self, |
Technology | Version | Purpose |
---|---|---|
Next.js | 14.2.0 | React framework with file-based routing, static site generation |
React | 18.2.0 | UI library for component-based development |
TypeScript | 5.3.3 | Static type-checking for JavaScript |
TailwindCSS | 3.4.1 | Utility-first CSS framework |
The walrus operator (:=
) was introduced in Python 3.8 as part of PEP 572. It's officially called the "assignment expression" operator, but it earned its nickname because :=
resembles a walrus with tusks when viewed sideways. This operator allows you to assign values to variables as part of an expression, rather than as a separate statement.
The syntax is:
variable := expression
Term | Description |
---|---|
SM (Streaming Multiprocessor) | Computational unit in NVIDIA GPUs; Hopper has up to 132 SMs |
Warp | Group of 32 threads that execute in lockstep |
Thread Block | Group of threads that execute on the same SM and can synchronize |
Grid | Collection of thread blocks that execute the same kernel |
FROM codercom/code-server:latest
USER root
# Install system dependencies
RUN apt-get update && apt-get install -y \
python3-pip \
python3-venv \
This is an excellent LangGraph cheat sheet! It provides a comprehensive and practical overview of LangGraph's key features and usage. Here's a slightly refined version with added clarity and formatting for better readability:
from typing import TypedDict, Dict, Any, Callable
from langchain_core.runnables import Runnable