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Qwen - Alibaba Cloud's multilingual LLM series:
Qwen1.5-0.5B
Qwen1.5-1.8B
Qwen1.5-4B
Qwen1.5-7B
Qwen1.5-14B
Qwen1.5-32B
Qwen1.5-72B
Qwen1.5-110B
Qwen1.5-MoE-A2.7B
Qwen2-0.5B
Qwen2-1.5B
Qwen2-7B
Qwen2-57B-A14B
Qwen2-72B
Qwen2.5-0.5B
Qwen2.5-1.5B
Qwen2.5-3B
Qwen2.5-7B
Qwen2.5-14B
Qwen2.5-32B
Qwen2.5-72B
Qwen3-0.6B
Qwen3-1.7B
Qwen3-4B
Qwen3-8B
Qwen3-14B
Qwen3-32B
Qwen3-30B-A3B
Qwen3-235B-A22B
Qwen/Qwen3-4B
📊 Model Parameters
Total Parameters
4,411,424,256
Context Length
40,960
Hidden Size
2560
Layers
36
Attention Heads
32
KV Heads
8
💾 Memory Requirements
FP32 (Full)
16.43 GB
FP16 (Half)
8.22 GB
INT8 (Quantized)
4.11 GB
INT4 (Quantized)
2.05 GB
🔑 KV Cache (Inference)
Per Token (FP16)
147.46 KB
Max Context FP32
11.25 GB
Max Context FP16
5.62 GB
Max Context INT8
2.81 GB
⚙️ Model Configuration
Core Architecture
Vocabulary Size
151,936
Hidden Size
2,560
FFN Intermediate Size
9,728
Number of Layers
36
Attention Heads
32
KV Heads
8
Head Dimension
128
Context & Position
Max Context Length
40,960
Uses Sliding Window
No
Sliding Window Size
Not set
Window Attention Layers
36
RoPE Base Frequency
1,000,000
RoPE Scaling
Not set
Layer Attention Types
[36 items]
Attention Configuration
Attention Bias
No
Attention Dropout
0%
Tied Embeddings
Yes
Activation & Normalization
Activation Function
silu
RMSNorm Epsilon
1e-06
Special Tokens
BOS Token ID
151,643
Pad Token ID
Not set
EOS Token ID
151645
Data Type
Model Dtype
bfloat16
Layer Types:
Attention
MLP/FFN
Normalization
Embedding
Attention
MLP
Norm
Embedding
Clear
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