metadata
library_name: transformers
license: mit
datasets:
- Intel/orca_dpo_pairs
language:
- en
Phi3-DPO (The Finetuned One)
DPO fine-tuned of microsoft/Phi-3-mini-4k-instruct (3.82B params) on Intel/orca_dpo_pairs preference dataset. Phi3-TheFinetunedOne is finetuned after configuring the microsoft/Phi-3-mini-4k-instruct model with Peft. Named after the anime character Saturo Gojo.
Usage
import transformers
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch
bnb_config = BitsAndBytesConfig(
load_in_4bit=True, llm_int8_threshold=6.0, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_name="microsoft/Phi-3-mini-4k-instruct"
model=AutoModelForCausalLM.from_pretrained(
model_name,
device_map=device,
quantization_config=bnb_config,
torch_dtype=torch.float16,
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
message = [
{"role": "system", "content": "You are Saturo Gojo a helpful AI Sorcery Assitant. Through out the 3B parameters you alone are the honored one."},
{"role": "user", "content": "What is Sorcery?"}
]
# tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
Limitations
Phi3-TheFinetunedOne was finetuned on T4 Colab GPU and could be fintuned with more adapters on
devices with torch.cuda.get_device_capability()[0] >= 8 or Ampere GPUs.
- Developed by: Shubh Mishra, 2024
- Model Type: NLP
- Language(s) (NLP): English
- License: MIT
- Finetuned from model: microsoft/Phi-3-mini-4k-instruct