Parameter	Original	Tiny
num_hidden_layers	48	10
num_local_experts	128	16
num_experts_per_tok	8	8
hidden_size	2048	2048
intermediate_size	6144	6144
moe_intermediate_size	768	768
num_attention_heads	32	32
num_key_value_heads	4	4

Checkpoint Structure

The checkpoint is stored as a single model.safetensors file with individual expert weights matching the original Qwen3 structure. Each layer has 16 experts with separate gate_proj, up_proj, and down_proj weights per expert.

Validation

The model was fine-tuned on a toy copypasta dataset and achieves:

Perplexity: 1.0 (on validation text)
Generation: Successfully generates coherent continuations

Example generation:

Input: "According to all known laws"
Output: "According to all known laws of aviation, there is no way a bee should be able to fly."

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("inference-optimization/Qwen3-1.6B-A0.9B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Qwen3-1.6B-A0.9B")

input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))

Creation Process

This model was created using the llm-compressor create-tiny-model Claude skill:

Configuration: Created with 10 layers and 16 experts (8 activated per token) to achieve ~1.6B total parameters with 50% MoE activation
Initialization: Randomly initialized weights using transformers init_weights()
Fine-tuning: Trained on famous internet copypastas until perplexity < 3.0
Checkpoint Conversion: Converted from batched expert format to individual expert format to match original Qwen3 checkpoint structure
Validation: Confirmed perplexity ~1.0 and successful text generation

Notes

This model maintains the same MoE architecture as the original with Grouped Query Attention (GQA)
The checkpoint format exactly matches the original Qwen3-30B-A3B structure for compatibility
This model is intended for testing and development only, not production use