karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

mplatzer

# install Synthetic Data SDK
# see also https://github.com/mostly-ai/mostlyai
#!uv pip install "mostlyai[local]"

# load training data
import pandas as pd
trn = pd.read_csv('/Users/mplatzer/github/the-prize-data/flat/flat-training.csv')

# instantiate SDK in LOCAL mode
from mostlyai.sdk import MostlyAI

sugatoray

# /// script
# requires-python = ">=3.11,<3.12"
# dependencies = [
#     "distilabel[hf-transformers, hf-inference-endpoints]",
# ]
# ///
from distilabel.models import InferenceEndpointsLLM
from distilabel.pipeline import InstructionResponsePipeline

repo_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"

davidberenstein1957

# /// script
# requires-python = ">=3.11,<3.12"
# dependencies = [
#     "distilabel[hf-transformers, hf-inference-endpoints]",
# ]
# ///
from distilabel.models import InferenceEndpointsLLM
from distilabel.pipeline import InstructionResponsePipeline

repo_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"

sugatoray

HF Model License Top20 Dist

• source: HF Model Metadata
• author: Sugato Ray @sugatoray

tomaarsen

# requires sentence_transformers>=3.2.0
from sentence_transformers import SentenceTransformer, export_optimized_onnx_model, export_dynamic_quantized_onnx_model

# The model to export to ONNX (+ optimize, quantize), OpenVINO
model_id = "mixedbread-ai/mxbai-embed-large-v1"
# Where to save the exported models locally
output_dir = model_id.replace("/", "-")

onnx_model = SentenceTransformer(model_id, backend="onnx", model_kwargs={"export": True})
onnx_model.save_pretrained(output_dir)

sugatoray

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

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

sugatoray

#VERBOSE=0 torchrun --nproc_per_node 3 self_contained_pp_LOC.py
import os, random, numpy as np, torch, torch.nn as nn, torch.distributed as dist, torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, DistributedSampler
from datasets import load_dataset
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

STEP, local_rank, world_size, verbose = 0, int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), os.environ.get("VERBOSE", "0") == "1"

def set_all_seed(seed):

3outeille

#VERBOSE=0 torchrun --nproc_per_node 3 self_contained_pp_LOC.py
import os, random, numpy as np, torch, torch.nn as nn, torch.distributed as dist, torch.nn.functional as F
from torch.optim import AdamW
from torch.utils.data import DataLoader, DistributedSampler
from datasets import load_dataset
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

STEP, local_rank, world_size, verbose = 0, int(os.environ["LOCAL_RANK"]), int(os.environ["WORLD_SIZE"]), os.environ.get("VERBOSE", "0") == "1"

def set_all_seed(seed):