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331.
▲
Tiny GPT in Go. Optimised for Understanding. Trained on Jules Verne Books
(github.com/zakirullin)
5 points
idontlikellm
3 days ago
discuss
332.
▲
Show HN: Train Block Diffusion Models on Consumer Hardware (RTX 4090) in Hours
(github.com/lapp0)
5 points
lappa
a year ago
discuss
333.
▲
Show HN: Byte-Pair Encoding tokenizer for training LLMs on large datasets
(github.com/jmaczan)
5 points
yu3zhou4
2 years ago
discuss
334.
▲
Show HN: Peripheral – test and train peripheral vision in browser
(github.com/gr-b)
5 points
grbsh
2 years ago
discuss
335.
▲
Show HN: Training Neural Networks with a Single Unix Pipeline
(github.com/dongyx)
5 points
dyx
3 years ago
discuss
336.
▲
Show HN: H5records – simple large dataset for pytorch training
(github.com/theblackcat102)
5 points
polymorph1sm
5 years ago
discuss
337.
▲
Show HN: TerraGoat, a vulnerable-by-design Terraform training project
(github.com/bridgecrewio)
5 points
bridgecrewio
6 years ago
discuss
338.
▲
BytePS, A high performance and general PS framework for distributed training
(github.com/bytedance)
5 points
jinqueeny
7 years ago
discuss
339.
▲
Show HN: Training classifiers with Create ML – fruits, flowers, xray, sentiment
(github.com/krishkumar)
5 points
LeicaLatte
8 years ago
discuss
340.
▲
DeepOSM – train a neural net with Openstreetmap features and orbital imagery
(github.com/trailbehind)
5 points
liotier
10 years ago
discuss
341.
▲
Show HN: MLbot – Train ML models in your cloud with a single command
(github.com/thecooltechguy)
4 points
subtech
4 years ago
4 comments
342.
▲
Ghosts of Softmax: Zeros of the partition function explain training instability
(github.com/piyush314)
4 points
g4omingron
3 months ago
3 comments
343.
▲
Show HN: OpenPace – Open-source marathon training app
(openpace.co)
4 points
edance
2 years ago
3 comments
344.
▲
VisualNexus – Training Pipeline for Visual Dataset Segmentation and Labeling
(github.com/kyegomez)
4 points
Reclaimer
3 years ago
3 comments
345.
▲
Show HN: Zugspaet – Check if your DB train was late in the past
(github.com/AlexW00)
4 points
surrTurr
a year ago
2 comments
346.
▲
YOLO models trained on DocLayNet, support document analytic intelligency
(github.com/ppaanngggg)
4 points
ppaanngggg
2 years ago
2 comments
347.
▲
Python Trainer
(github.com/aaronchall)
4 points
aaronchall
11 years ago
1 comment
348.
▲
Train and inference GPT in 243 lines of pure, dependency-free Python by Karpathy
(gist.github.com)
4 points
itvision
4 months ago
1 comment
349.
▲
Car Reinforcement Learning Training
(github.com/leesweqq)
4 points
kyleliiii
10 months ago
1 comment
350.
▲
Show HN: TONE3000 – Community for Neural Amp Modeling with 90k+ models trained
(tone3000.com)
4 points
woodybury
a year ago
1 comment
351.
▲
Want to Train Your Own GPT-Style Model? – Step-by-Step Notebook
(github.com/kevinpdev)
4 points
kevinpdev
a year ago
1 comment
352.
▲
Training Software Engineering Agents and Verifiers with SWE-Gym
(github.com/SWE-Gym)
4 points
jiayi_pan
a year ago
1 comment
353.
▲
Apple open sources a framework for training text-to-image models
(github.com/apple)
4 points
jlukecarlson
2 years ago
1 comment
354.
▲
Open-Llama: A Open Source Project for Training Language Models
(github.com/s-JoL)
4 points
bayes-song
3 years ago
1 comment
355.
▲
Spacy training on Apple M1 vs. AMD Ryzen 5900X: 55% faster, 16x more efficient
(github.com/explosion)
4 points
KarlKemp
5 years ago
1 comment
356.
▲
Trains – ClearML open source MLOPs platform new release and name change
(github.com/allegroai)
4 points
LSTMeow
5 years ago
1 comment
357.
▲
Show HN: Code Carbon-Estimate and track carbon emissions from ML models training
(github.com/mlco2)
4 points
gidim
6 years ago
1 comment
358.
▲
Tiny GPT in Go. Optimised for Understanding. Trained on Jules Verne Books
(github.com/zakirullin)
4 points
asdfjsjj2342
5 days ago
discuss
359.
▲
A (small) language model walks through its training text
(github.com/chrishwiggins)
4 points
raybb
5 days ago
discuss
360.
▲
Show HN: Profine - Profile and rewrite your PyTorch training loop on real GPUs
(github.com/ProfineAI)
4 points
aisinghal
25 days ago
discuss
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