The model was fine-tuned using a distributed approach across multiple shards. While the base architecture is preserved, this model has been through a fine-tuning process optimized for specific tasks.

Usage

Base Model: microsoft/Phi-3-mini-128k-instruct
Training Method: Distributed fine-tuning with Ray
Shards Used: 2
Parameters: ~3.8B

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("a-k-aAiMGoD/phi3-mini-distributed-fine-tune")
model = AutoModelForCausalLM.from_pretrained("a-k-aAiMGoD/phi3-mini-distributed-fine-tune")

# Example usage
text = "Hello, how are you?"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Configuration

Distributed across 2 shards
Optimized for large-scale deployment
Enhanced with Ray-based parallelization