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loggenix-moe-0.1B-e2-lr5e5-b4-3060-early-stopped – AI Model by kshitijthakkar | AlphaNeural AI

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kshitijthakkar/
loggenix-moe-0.1B-e2-lr5e5-b4-3060-early-stopped

like

0

text-generation

code-generation

Qwen/Qwen3-0.6B

autotrain_compatible

endpoints_compatible

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Loggenix MoE 0.1B - Early Stopped

This is a Mixture of Experts (MoE) model based on Qwen3 architecture, trained on coding data.

Model Details

Base Model: Qwen/Qwen3-0.6B
Architecture: Qwen3MoE
Parameters: ~80M parameters
Experts: 4 experts, 2 active per token
Context Length: 512 tokens
Training Status: Early stopped due to convergence

Model Configuration

Hidden Size: 512
Attention Heads: 8
Hidden Layers: 12
Vocabulary Size: 151,936

Usage

python

from transformers import AutoTokenizer, Qwen3MoeForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kshitijthakkar/loggenix-moe-0.1B-e2-lr5e5-b4-3060-early-stopped")
model = Qwen3MoeForCausalLM.from_pretrained("kshitijthakkar/loggenix-moe-0.1B-e2-lr5e5-b4-3060-early-stopped")

# Example usage
input_text = "def factorial(n):"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Dataset: Custom coding dataset
Training stopped early due to validation loss convergence
Best model checkpoint saved and uploaded