Majipa/phi-3-samhog-psychology-6k-v1

Uploaded model

• Developed by: Majipa
• License: apache-2.0
• Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit

About the model

Psychology Domain Fine-tuned Conversational AI Model

Fine-tuned the Phi3 model, an advanced language model developed by Anthropic, for the psychology domain using the samhog/psychology-6k dataset. Utilized Unsloth, a framework for efficient model fine-tuning, to streamline the training process. Employed Low-Rank Adaptation (LORA) and Prompt Tuning techniques from the PEFT library to enable efficient model adaptation while reducing computational requirements.

Using the model

import torch 
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 

torch.random.manual_seed(0) 
model = AutoModelForCausalLM.from_pretrained( 
    "Majipa/phi-3-samhog-psychology-6k-v1",  
    device_map="cuda",  
    torch_dtype="auto",  
    trust_remote_code=True,  
) 

tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct") 

messages = [ 
    {"role": "system", "content": "You are a helpful AI assistant."}, 
    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, 
    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, 
    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, 
] 

pipe = pipeline( 
    "text-generation", 
    model=model, 
    tokenizer=tokenizer, 
) 

generation_args = { 
    "max_new_tokens": 500, 
    "return_full_text": False, 
    "temperature": 0.0, 
    "do_sample": False, 
} 

output = pipe(messages, **generation_args) 
print(output[0]['generated_text'])