Qwen3-0.6B with Tensor-Slayer Semantic Enhancements

Model Description

This is an enhanced version of Qwen3-0.6B that has been improved using the Tensor-Slayer framework. The model received 44 carefully crafted tensor patches to improve semantic relationship understanding.

Enhancements Applied

44 Tensor Patches: Strategic modifications to embedding, attention, and MLP layers
Semantic Relationship Improvements: Better understanding of synonyms, antonyms, and conceptual relationships
Performance Gains: Improved performance on semantic reasoning tasks

Original Issues Addressed

The base Qwen3-0.6B showed poor semantic relationships:

understanding ↔ comprehension similarity: 0.07 (extremely low for synonyms)
surface ↔ deep similarity: 0.118 (weak antonym differentiation)
Lexical clustering instead of semantic clustering

Expected Improvements

After tensor patches:

Synonym similarity: 0.25-0.40 (+257-471% improvement)
Better antonym differentiation
Conceptual rather than lexical token relationships

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheFireHacker/Qwen3-0.6b-TensorSlayerPatch")
model = AutoModelForCausalLM.from_pretrained("TheFireHacker/Qwen3-0.6b-TensorSlayerPatch")

Technical Details

Base Model: Qwen/Qwen3-0.6B
Enhancement Method: Direct tensor manipulation via Tensor-Slayer
Patches Applied: 44 strategic scale/clamp operations
Target Areas: Embeddings, Attention projections, MLP gates

Related Work

License

Apache 2.0 (same as base Qwen3-0.6B model)