Qwen3-30M with GPT-2 Tokenizer (FP16)
A 30M parameter version of Qwen3-0.6B using GPT-2's tokenizer for better compatibility, optimized with FP16 precision.
Model Details
- Base Model: Qwen/Qwen3-0.6B
- Architecture: Qwen3 (8 layers, 224 hidden size)
- Parameters: ~35M (reduced from 637M)
- Tokenizer: GPT-2 (50,257 vocabulary)
- Vocabulary: Reduced from 151,936 to 50,257 tokens
- Precision: FP16 (half precision for memory efficiency)
- Model Size: ~60MB (vs ~120MB in FP32)
Architecture Specifications
- Layers: 8 transformer layers
- Hidden Size: 224
- Intermediate Size: 896 (4x hidden_size)
- Attention Heads: 8
- Key-Value Heads: 8
- Max Position Embeddings: 32,768
- Activation: SiLU
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load with automatic fp16 support
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/qwen3-30m-fp16")
model = AutoModelForCausalLM.from_pretrained(
"Mostafa8Mehrabi/qwen3-30m-fp16",
torch_dtype=torch.float16, # Explicitly use fp16
device_map="auto" # Automatically place on available device
)
# For GPU inference (recommended)
# model = model.to("cuda") # if you have a GPU
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
# Move inputs to same device as model if using GPU
# inputs = {k: v.to(model.device) for k, v in inputs.items()}
outputs = model.generate(**inputs, max_length=50, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Key Features
- ✅ FP16 Precision: 50% smaller model size, faster inference
- ✅ 30M Parameters: Ultra-lightweight for edge deployment
- ✅ 8 Layers: Balanced depth for performance
- ✅ Standard GPT-2 tokenizer (no trust_remote_code)
- ✅ Compatible vocabulary sizes
- ✅ SafeTensors format for faster loading
- ✅ Works like any HuggingFace model
- ✅ 21x smaller than original Qwen3-0.6B
- ✅ GPU optimized for efficient inference
Architecture Comparison
| Component | Original | This Model |
|---|---|---|
| Parameters | 637M | ~35M |
| Vocabulary | 151,936 | 50,257 |
| Hidden Size | 1024 | 224 |
| Layers | 28 | 8 |
| Intermediate Size | 4096 | 896 |
| Attention Heads | 16 | 8 |
| Tokenizer | Qwen3 | GPT-2 |
| Precision | FP32 | FP16 |
| Model Size | ~1.2GB | ~60MB |
Memory Requirements
- FP16: ~60MB model + ~30MB working memory = ~90MB total
- FP32: ~120MB model + ~60MB working memory = ~180MB total
- Memory savings: ~50% reduction compared to FP32
- Ultra-lightweight: Perfect for mobile and edge devices
Performance Notes
- FP16 provides significant memory savings with minimal quality loss
- 30M parameters optimized for fast inference while maintaining coherence
- Ideal for deployment in resource-constrained environments
- Compatible with both CPU and GPU inference
- Faster loading times due to smaller file size
- 8 layers provide good balance between model capacity and speed