JoyAI-LLM Flash-Base is a state-of-the-art mixture-of-experts (MoE) language model with 3 billion activated parameters and 48 billion total parameters. Trained with the Muon optimizer, JoyAI Flash-base achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities. JoyAI-LLM Flash series aim to accelarate high-throughput, latency-sensitive applications where cost per query must remain minimal.
Key Features
Training-Inference Collaboration: apply Muon optimizer with dense MTP, develop novel optimization techniques to resolve instabilities while scaling up, delivering 1.3× to 1.7× the throughput of the non-MTP version.
Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving.
2. Model Summary
Architecture
Mixture-of-Experts (MoE)
Total Parameters
48B
Activated Parameters
3B
Number of Layers
(Dense layer included)
40
Number of Dense Layers
1
Attention Hidden Dimension
2048
MoE Hidden Dimension
(per Expert)
768
Number of Attention Heads
32
Number of Experts
256
Selected Experts per Token
8
Number of Shared Experts
1
Vocabulary Size
129K
Context Length
128K
Attention Mechanism
MLA
Activation Function
SwiGLU
3. Evaluation Results
Benchmark
JoyAI-LLM Flash-base
Qwen3-30B-A3B-base
MMLU
84.70
82.12
MMLU-Pro
73.14
61.76
CMMLU
83.09
83.60
HumanEval
85.37
87.80
LiveCodeBench
39.91
37.34
GSM8K
88.78
90.37
MATH
78.16
59.60
MATH 500
77.00
58.00
4. License
Both the code repository and the model weights are released under the
Modified MIT License
.
Runs of jdopensource JoyAI-LLM-Flash-Base on huggingface.co
106
Total runs
0
24-hour runs
0
3-day runs
7
7-day runs
53
30-day runs
More Information About JoyAI-LLM-Flash-Base huggingface.co Model
JoyAI-LLM-Flash-Base huggingface.co
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