13 10 125

Danielus

danielus

DanielusG

AI & ML interests

None yet

Recent Activity

liked a model 2 days ago

Lightricks/LTX-Video

liked a Space 5 days ago

Kwai-Kolors/Kolors-Character-With-Flux

liked a model 13 days ago

Qwen/Qwen2.5-Coder-32B-Instruct

View all activity

Organizations

None yet

danielus's activity

liked a model 2 days ago

Lightricks/LTX-Video

Image-to-Video • Updated 1 day ago • 7.61k • 232

liked a Space 5 days ago

Running

437

🤹

Kolors Character With Flux

Kolors Character to keep character developed with Flux

liked a model 13 days ago

Qwen/Qwen2.5-Coder-32B-Instruct

Text Generation • Updated 6 days ago • 72.6k • • 969

New activity in infly/OpenCoder-8B-Instruct 15 days ago

FIM task

#2 opened 15 days ago by

danielus

Reacted to TuringsSolutions's post with 😔 18 days ago

Post

3946

Are you familiar with the difference between discrete learning and predictive learning? This distinction is exactly why LLM models are not designed to perform and execute function calls, they are not the right shape for it. LLM models are prediction machines. Function calling requires discrete learning machines. Fortunately, you can easily couple an LLM model with a discrete learning algorithm. It is beyond easy to do, you simply need to know the math to do it. Want to dive deeper into this subject? Check out this video.

https://youtu.be/wBRem2p8iPM

8 replies

liked 2 models 26 days ago

Kortix/FastApply-7B-v1.0

Text Generation • Updated 28 days ago • 294 • 14

marco/mcdse-2b-v1

Updated 26 days ago • 2.67k • 49

liked a model about 1 month ago

CohereForAI/aya-expanse-8b

Text Generation • Updated 25 days ago • 48.2k • 286

liked a Space about 1 month ago

Running on L40S

138

🐢

Flux Outpainting

Reacted to ImranzamanML's post with 👍 about 1 month ago

Post

1293

Here is how we can calculate the size of any LLM model:

Each parameter in LLM models is typically stored as a floating-point number. The size of each parameter in bytes depends on the precision.

32-bit precision: Each parameter takes 4 bytes.
16-bit precision: Each parameter takes 2 bytes

To calculate the total memory usage of the model:
Memory usage (in bytes) = No. of Parameters × Size of Each Parameter

For example:
32-bit Precision (FP32)
In 32-bit floating-point precision, each parameter takes 4 bytes.
Memory usage in bytes = 1 billion parameters × 4 bytes
1,000,000,000 × 4 = 4,000,000,000 bytes
In gigabytes: ≈ 3.73 GB

16-bit Precision (FP16)
In 16-bit floating-point precision, each parameter takes 2 bytes.
Memory usage in bytes = 1 billion parameters × 2 bytes
1,000,000,000 × 2 = 2,000,000,000 bytes
In gigabytes: ≈ 1.86 GB

It depends on whether you use 32-bit or 16-bit precision, a model with 1 billion parameters would use approximately 3.73 GB or 1.86 GB of memory, respectively.