inference-optimization / Qwen3.8-1.0B-A0.6B

Qwen3.8-1.0B-A0.6B

This is a tiny version of Qwen/Qwen3.8-2.4T-A95B created for testing and development.

Model Details

• Base Model : Qwen/Qwen3.8-2.4T-A95B
• Architecture : qwen3_5_moe_text (Qwen3_5MoeForCausalLM)
• Total Parameters : 0.97B
• Activated Parameters : ~0.6B

Configuration Changes

The following parameters were reduced from the original model:

Checkpoint Structure

Single safetensors file with packed expert format ( experts.gate_up_proj , experts.down_proj ) matching the original checkpoint structure. Layer types follow the [linear, linear, linear, full] x 2 pattern from the original.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Qwen3.8-1.0B-A0.6B", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("Qwen3.8-1.0B-A0.6B")

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.

1. Inspected the original 2.4T parameter MoE model configuration
2. Reduced all dimensions to create a ~1B parameter model while preserving the hybrid linear/full attention architecture
3. Fine-tuned on a toy dataset to achieve perplexity ~1.0
4. Converted checkpoint format to match the original (packed experts, correct tensor naming)
5. Validated model loading and generation

Notes

• The model preserves the hybrid attention pattern: 6 linear attention layers and 2 full attention layers in a [linear, linear, linear, full] x 2 pattern
• MTP (multi-token prediction) layers are removed (mtp_num_hidden_layers=0) as they are not needed for the tiny model
• Expert weights use the packed format (gate_up_proj fused, stacked across experts) matching the original checkpoint