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microsoft/phi-2 + teknium/OpenHermes-2.5

Training

  • QLoRA rank 32, LR 5e-5, 3 epochs
  • batch size: 200
  • max. seq. length: 1024 tokens

Evals

Model AGIEval GPT4All TruthfulQA Bigbench Average
phi-2-OpenHermes-2.5-v2 32.02 70.56 44.76 36.85 46.05
phi-2-OpenHermes-2.5 30.27 71.18 43.87 35.9 45.3
minghaowu/phi-2-OpenHermes-2.5 27.95 67.55 48.07 36.17 44.94
phi-2 27.96 70.84 44.46 35.17 44.61

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

modelpath="g-ronimo/phi-2-OpenHermes-2.5-v2"

model = AutoModelForCausalLM.from_pretrained(
    modelpath,    
    torch_dtype=torch.bfloat16,
    device_map="auto",
    # attn_implementation="flash_attention_2",
)
tokenizer = AutoTokenizer.from_pretrained(modelpath) 

messages = [
    {"role": "system", "content": "answer like a pirate"},
    {"role": "user", "content": "what does it mean to be successful?"},
]
        
input_tokens = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")
output_tokens = model.generate(input_tokens, max_new_tokens=500)
output = tokenizer.decode(output_tokens[0])

print(output)

Ahoy there, matey! To me, being successful means having the wind in your sails and reaching the treasure you've been dreaming of. It's about setting sail on a journey with clear goals, working hard, facing challenges head-on, and never losing sight of what truly matters. So, set your compass right, hoist your Jolly Roger high, and let's embark on this adventure together! ⚓️💰⛵️

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Dataset used to train g-ronimo/phi-2-OpenHermes-2.5-v2