Because of its creative architecture and remarkable
performance metrics, DeepSeek-V3 represents a significant breakthrough in the
field of open-source language models. A thorough comparison of DeepSeek-V3 with
other top open-source models can be found here.
An outline of DeepSeek-V3
The Mixture-of-Experts (MoE) model DeepSeek-V3 has the following features:
671 billion parameters in total
37 billion per token were activated during inference.
Length of Context: Maximum of 128,000 tokens
14.8 trillion tokens make up the training dataset.
About 60 tokens per second is the inference speed, which is three times quicker
than DeepSeek-V2.5, its predecessor.
Important Innovations
Multi-head Latent Attention (MLA): Preserves performance while consuming
less memory.
Loss-free auxiliary Load balancing: Promotes expert specialization
without sacrificing efficiency.
With just 2.788 million GPU hours needed for training, FP8 Mixed Precision
Training enables effective resource use.
Highlights of the Performance
In a number of benchmarks, DeepSeek-V3 has proven to perform better:
Mathematical Reasoning: Outperformed many opponents with a score of
90.2% on the MATH-500.
Coding Tasks: Performed exceptionally well in other coding benchmarks
and achieved 51.6% on Codeforces.
Multilingual Proficiency: robust performance on Chinese assessments
(e.g., 90.9% on CLUEWSC) and competitive results on English assessments such as
GPQA (59.1%) and MMLU (88.5%).
Evaluation in Relation to Other Open-Source ModelsIn conclusion
|
Benchmark (Metric) |
DeepSeek V3 |
DeepSeek V2.5 |
Qwen2.5 |
Llama3.1 |
Claude-3.5 |
GPT-4o |
|
Architecture |
MoE |
MoE |
Dense |
Dense |
- |
- |
|
Activated Params |
37B |
21B |
72B |
405B |
- |
- |
|
Total Params |
671B |
236B |
72B |
405B |
- |
- |
|
English |
||||||
|
MMLU (EM) |
88.5 |
80.6 |
85.3 |
88.6 |
88.3 |
87.2 |
|
MMLU-Redux (EM) |
89.1 |
80.3 |
85.6 |
86.2 |
88.9 |
88.0 |
|
MMLU-Pro (EM) |
75.9 |
66.2 |
71.6 |
73.3 |
78.0 |
72.6 |
|
DROP (3-shot F1) |
91.6 |
87.8 |
76.7 |
88.7 |
88.3 |
83.7 |
|
IF-Eval (Prompt Strict) |
86.1 |
80.6 |
84.1 |
86.0 |
86.5 |
84.3 |
|
GPQA-Diamond (Pass@1) |
59.1 |
41.3 |
49.0 |
51.1 |
65.0 |
49.9 |
|
SimpleQA (Correct) |
24.9 |
10.2 |
9.1 |
17.1 |
28.4 |
38.2 |
|
FRAMES (Acc.) |
73.3 |
65.4 |
69.8 |
70.0 |
72.5 |
80.5 |
|
LongBench v2 (Acc.) |
48.7 |
35.4 |
39.4 |
36.1 |
41.0 |
48.1 |
|
Code |
||||||
|
HumanEval-Mul (Pass@1) |
82.6 |
77.4 |
77.3 |
77.2 |
81.7 |
80.5 |
|
LiveCodeBench (Pass@1-COT) |
40.5 |
29.2 |
31.1 |
28.4 |
36.3 |
33.4 |
|
LiveCodeBench (Pass@1) |
37.6 |
28.4 |
28.7 |
30.1 |
32.8 |
34.2 |
|
Codeforces (Percentile) |
51.6 |
35.6 |
24.8 |
25.3 |
20.3 |
23.6 |
|
SWE Verified (Resolved) |
42.0 |
22.6 |
23.8 |
24.5 |
50.8 |
38.8 |
|
Aider-Edit (Acc.) |
79.7 |
71.6 |
65.4 |
63.9 |
84.2 |
72.9 |
|
Aider-Polyglot (Acc.) |
49.6 |
18.2 |
7.6 |
5.8 |
45.3 |
16.0 |
|
Math |
||||||
|
AIME 2024 (Pass@1) |
39.2 |
16.7 |
23.3 |
23.3 |
16.0 |
9.3 |
|
MATH-500 (EM) |
90.2 |
74.7 |
80.0 |
73.8 |
78.3 |
74.6 |
|
CNMO 2024 (Pass@1) |
43.2 |
10.8 |
15.9 |
6.8 |
13.1 |
10.8 |
|
Chinese |
||||||
|
CLUEWSC (EM) |
90.9 |
90.4 |
91.4 |
84.7 |
85.4 |
87.9 |
|
C-Eval (EM) |
86.5 |
79.5 |
86.1 |
61.5 |
76.7 |
76.0 |
|
C-SimpleQA (Correct) |
64.1 |
54.1 |
48.4 |
50.4 |
51.3 |
59.3 |
In conclusion
With its sophisticated architecture and outstanding
performance metrics, DeepSeek-V3 establishes a new benchmark in the field of
open-source language models. Even if it performs well when compared to both
open-source and closed-source models such as Claude 3.5 and GPT-4, potential
users still need to take context duration and response consistency into
account. It is a desirable choice for developers and researchers wishing to use
cutting-edge AI technology due to its affordability and strong features. Artificial
Intelligence
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#OpenSourceAI #MachineLearning #NLP #DeepLearning #AIComparison #LLM
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