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HF Model Metadata
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Sugato Ray
@sugatoray
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sugatoray’s gists · GitHub
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I am a Physicist turned Data Scientist + ML Practitioner.
Research Interests: Data Science, ML, DL, Statistics, Math, Computing, LLMs.
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sugatoray
/ custom.css
Created
July 23, 2025 21:16
— forked from koaning/custom.css
nintendo.css for marimo
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/* Custom CSS for Marimo - Font customization */ | |
/* Import Press Start 2P font from Google Fonts */ | |
@import url('https://fonts.googleapis.com/css2?family=Press+Start+2P&display=swap'); | |
:root { | |
--marimo-monospace-font: 'Press Start 2P', 'Courier New', monospace; | |
--marimo-text-font: 'Press Start 2P', 'Courier New', monospace; | |
--marimo-heading-font: 'Press Start 2P', 'Courier New', monospace; | |
} |
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# --- Configuration --- | |
$sourcePath = "C:\Path\To\Your\SourcePpts" # Folder containing the PPTs to merge | |
$outputPath = "C:\Path\To\Your\MergedPresentation.pptx" # Full path for the merged output file | |
# Get a list of PowerPoint files to merge (e.g., all .pptx files) | |
$pptFiles = Get-ChildItem -Path $sourcePath -Filter "*.pptx" | Sort-Object Name | |
# --- Check if there are files to merge --- | |
if ($pptFiles.Count -eq 0) { | |
Write-Host "No PowerPoint files found in: $sourcePath" |
sugatoray
/ train.py
Created
February 7, 2025 14:01
— forked from ddh0/train.py
Janky pretraining script for small llama models using HF fineweb - modify according to your needs
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import os | |
import torch | |
import psutil | |
import datasets | |
import glob | |
from transformers import ( | |
AutoTokenizer, LlamaConfig, LlamaForCausalLM, Trainer, TrainingArguments, | |
DataCollatorForLanguageModeling | |
) |
sugatoray
/ sft_data_mlx.py
Created
February 2, 2025 14:13
— forked from davidberenstein1957/sft_data_mlx.py
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# /// script | |
# requires-python = ">=3.11,<3.12" | |
# dependencies = [ | |
# "distilabel[mlx]", | |
# ] | |
# /// | |
from distilabel.models import MlxLLM | |
from distilabel.pipeline import InstructionResponsePipeline | |
llm = MlxLLM( |
sugatoray
/ synthetic_data_deepseekr1_qwen_distill.py
Created
January 27, 2025 22:48
— forked from davidberenstein1957/synthetic_data_deepseekr1_qwen_distill.py
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# /// 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_models_license_top20_dist.md
Created
January 3, 2025 11:02
HF Model License Top20 Dist
sugatoray
/ export_locally.py
Created
October 15, 2024 14:32
— forked from tomaarsen/export_locally.py
Export Sentence Transformer models to ONNX (+ optimization, quantization) & OpenVINO
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# 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
/ prompt.txt
Created
October 6, 2024 14:53
— forked from philschmid/prompt.txt
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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
/ pipeline_parallel.py
Created
October 2, 2024 17:20
— forked from 3outeille/pipeline_parallel.py
Self contained example of how pipeline parallel works (AFAB and 1F1B) in 200 LOC
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#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): |
sugatoray
/ l3min.py
Created
August 16, 2024 05:18
— forked from awni/l3min.py
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""" | |
A minimal, fast example generating text with Llama 3.1 in MLX. | |
To run, install the requirements: | |
pip install -U mlx transformers fire | |
Then generate text with: | |
python l3min.py "How tall is K2?" |
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