from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kshitijthakkar/loggenix-nanoKimi2-test")
model = AutoModelForCausalLM.from_pretrained("kshitijthakkar/loggenix-nanoKimi2-test")

# Generate text
input_text = "Hello, how are you?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Script

Base Architecture: kimi_k2
Optimizer: muon
Learning Rate: 0.02
Weight Decay: 0.1
Dataset: loggenix-rca
Hidden Size: 1024
Epochs: 1

This model was trained using a custom training script with the Muon optimizer (if specified).