jatingocodeo/phi2-finetuned-openassistant

Phi-2 Fine-tuned on OpenAssistant

This model is a fine-tuned version of Microsoft's Phi-2 model, trained on the OpenAssistant dataset using QLoRA techniques.

Model Description

• Base Model: Microsoft Phi-2
• Training Data: OpenAssistant Conversations Dataset
• Training Method: QLoRA (Quantized Low-Rank Adaptation)
• Use Case: Conversational AI and text generation

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("your-username/phi2-finetuned-openassistant")
tokenizer = AutoTokenizer.from_pretrained("your-username/phi2-finetuned-openassistant")

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

Training Details

• Fine-tuned for 1 epoch
• Used 4-bit quantization for efficient training
• Implemented gradient checkpointing and mixed precision training

Limitations

• The model inherits limitations from both Phi-2 and the OpenAssistant dataset
• May produce incorrect or biased information
• Should be used with appropriate content filtering and moderation

License

This model is released under the MIT License.