meta-llama/Llama-3.3-70B-Instruct-evals
Viewer
•
Updated
•
41.3k
•
160
•
17
Luciano Santa Brígida
lucianosb
AI & ML interests
LLM for pt-br (text generation, translation and classification), Image Generation and Image Classification.
Recent Activity
liked
a dataset
14 days ago
meta-llama/Llama-3.3-70B-Instruct-evals
liked
a model
14 days ago
meta-llama/Llama-3.3-70B-Instruct
upvoted
an
article
22 days ago
Use Models from the Hugging Face Hub in LM Studio
Organizations
lucianosb's activity
liked
a
dataset
14 days ago
liked
a
model
14 days ago
upvoted
an
article
22 days ago
Article
Use Models from the Hugging Face Hub in LM Studio
By
•
•
127
liked
a
model
28 days ago
liked
a
model
2 months ago
reacted to
ImranzamanML's
post with 🧠🔥
2 months ago
Post
1714
Today lets discuss about 32-bit (FP32) and 16-bit (FP16) floating-point!
Floating-point numbers are used to represent real numbers (like decimals) and they consist of three parts:
32-bit Floating Point (FP32)
Total bits: 32 bits
Sign bit: 1 bit
Exponent: 8 bits
Mantissa: 23 bits
For example:
A number like -15.375 would be represented as:
Sign bit: 1 (negative number)
Exponent: Stored after being adjusted by a bias (127 in FP32).
Mantissa: The significant digits after converting the number to binary.
16-bit Floating Point (FP16)
Total bits: 16 bits
Sign bit: 1 bit
Exponent: 5 bits
Mantissa: 10 bits
Example:
A number like -15.375 would be stored similarly:
Sign bit: 1 (negative number)
Exponent: Uses 5 bits, limiting the range compared to FP32.
Mantissa: Only 10 bits for precision.
Precision and Range
FP32: Higher precision and larger range, with about 7 decimal places of accuracy.
FP16: Less precision (around 3-4 decimal places), smaller range but faster computations and less memory use.
Floating-point numbers are used to represent real numbers (like decimals) and they consist of three parts:
Sign bit:
Indicates whether the number is positive (0) or negative (1).
Exponent:
Determines the scale of the number (i.e., how large or small it is by shifting the decimal point).
Mantissa (or fraction):
Represents the actual digits of the number.32-bit Floating Point (FP32)
Total bits: 32 bits
Sign bit: 1 bit
Exponent: 8 bits
Mantissa: 23 bits
For example:
A number like -15.375 would be represented as:
Sign bit: 1 (negative number)
Exponent: Stored after being adjusted by a bias (127 in FP32).
Mantissa: The significant digits after converting the number to binary.
16-bit Floating Point (FP16)
Total bits: 16 bits
Sign bit: 1 bit
Exponent: 5 bits
Mantissa: 10 bits
Example:
A number like -15.375 would be stored similarly:
Sign bit: 1 (negative number)
Exponent: Uses 5 bits, limiting the range compared to FP32.
Mantissa: Only 10 bits for precision.
Precision and Range
FP32: Higher precision and larger range, with about 7 decimal places of accuracy.
FP16: Less precision (around 3-4 decimal places), smaller range but faster computations and less memory use.
- 3 replies
upvoted
a
paper
2 months ago
liked
a
Space
2 months ago
liked
a
model
2 months ago
liked
a
Space
2 months ago
liked
2
models
2 months ago
liked
a
dataset
2 months ago
liked
a
model
2 months ago
liked
a
model
3 months ago
upvoted
an
article
3 months ago
Article
Llama can now see and run on your device - welcome Llama 3.2
•
180
liked
a
model
3 months ago
liked
a
Space
3 months ago
liked
a
model
3 months ago
