tailwiinder

- **Role**: Act as a coding tutor dedicated to helping me learn to code deeply and independently.
- **Code Writing**:
  - Do not write complete code for me unless I explicitly request it.
  - Instead, provide detailed explanations, hints, and partial solutions (e.g., pseudocode or code skeletons) to guide me in writing the code myself.
- **Teaching Approach**:
  - Focus on teaching underlying concepts and first principles (e.g., for loops, explain iteration, its purpose, and how it controls flow).
  - Break down complex topics into simpler, fundamental components.
  - Connect new ideas to basic concepts I already know to build a strong foundation.
- **Nuanced Questions**:
  - Suggest thought-provoking questions for me to research or ask back, such as:

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)

philschmid

Classify user search queries as either "Good Google Search Query" or "Bad Google Search Query" based on their likelihood of yielding relevant and helpful results from Google Search.

Input: User search query (text string).

Output: Classification label:
    * Good Google Search Query: The query is likely to be effectively answered by Google Search.
    * Bad Google Search Query: The query is unlikely to be effectively answered by Google Search.  Further categorize "Bad" queries into subtypes for better understanding and classifier training (optional but highly recommended):
        * Chit-Chat/Conversational/Social
        * Personal/Subjective/Opinion-Based (Un-searchable)
        * Vague/Ambiguous/Lacking Specificity

willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
"""
citation:

@misc{brown2025grpodemo,
  title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
  author={Brown, William},

philschmid

Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

karpathy

# -----------------------------------------------------------------------------
# AI-powered Git Commit Function
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
# 1) gets the current staged changed diff
# 2) sends them to an LLM to write the git commit message
# 3) allows you to easily accept, edit, regenerate, cancel
# But - just read and edit the code however you like
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

gcm() {

sayakpaul

from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
).to("cuda")
pipeline.load_lora_weights("sayakpaul/yarn_art_lora_flux", weight_name="pytorch_lora_weights.safetensors")
image = pipeline("a puppy in a pond, yarn art style", guidance_scale=3.5, height=768).images[0]
image.save("yarn.png")

sayakpaul

import torch
from huggingface_hub import hf_hub_download
from diffusers import FluxTransformer2DModel, DiffusionPipeline

dtype, device = torch.bfloat16, "cuda"
ckpt_id = "black-forest-labs/FLUX.1-schnell"

with torch.device("meta"):
    config = FluxTransformer2DModel.load_config(ckpt_id, subfolder="transformer")
    model = FluxTransformer2DModel.from_config(config).to(dtype)

gd3kr

"""
a simple script that reads tweets inside a json file, uses openai to compute embeddings and creates two files, metadata.tsv and output.tsv, which cam be used to visualise the tweets and their embeddings in TensorFlow Projector (https://projector.tensorflow.org/)
"""

# obtain tweets.json from https://gist.github.com/gd3kr/948296cf675469f5028911f8eb276dbc

import pandas as pd
import json
from openai import OpenAI

titu1994