Introducing "CatPPT" - the purrfect alternative to that other big cat in town, known for keeping all the secrets to itself! Our feline friend here is created through merging openchat and neuralchat models using Gradient SLERP method (resulting in rishiraj/CatPPT-base) and then finetuned on no_robots dataset for chat.
This is the top-performing 7B model on the leaderboard, that's free from any whiff of evaluation data contamination.
Model date
rishiraj/CatPPT was trained between 15th and 17th December, 2023.
Evaluation
It achieves the following results on the Open_LLM_Leaderboard. At the time of release, CatPPT is the highest ranked 7B chat model on the leaderboard, that's free from evaluation data contamination.
Model
Average
ARC
HellaSwag
MMLU
TruthfulQA
Winogrande
GSM8K
rishiraj/CatPPT
72.32
68.09
86.69
65.16
61.55
81.61
70.81
Intel/neural-chat-7b-v3-3
69.83
66.89
85.26
63.07
63.01
79.64
61.11
openchat/openchat-3.5-1210
68.89
64.93
84.92
64.62
52.15
80.74
65.96
meta-math/MetaMath-Mistral-7B
65.78
60.67
82.58
61.95
44.89
75.77
68.84
Deci/DeciLM-7B-instruct
63.19
61.01
82.37
60.24
49.75
79.72
46.02
mistralai/Mistral-7B-Instruct-v0.2
65.71
63.14
84.88
60.78
68.26
77.19
40.03
mistralai/Mixtral-8x7B-Instruct-v0.1
72.62
70.22
87.63
71.16
64.58
81.37
60.73
meta-llama/Llama-2-70b-hf
67.87
67.32
87.33
69.83
44.92
83.74
54.06
tiiuae/falcon-180B
67.85
69.45
88.86
70.5
45.47
86.9
45.94
Inference procedure
Here's how you can run the model using the pipeline() function from 🤗 Transformers:
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="rishiraj/CatPPT", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate"
},
{
"role": "user",
"content": "How many helicopters can a human eat in one sitting?"
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 128
total_train_batch_size: 512
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 1
Training results
Training Loss
Epoch
Step
Validation Loss
1.9947
0.16
3
2.0093
Framework versions
Transformers 4.36.1
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.0
PEFT 0.6.1
Citation Information
@misc{rishiraj2023catppt,
author = {Rishiraj Acharya},
title = {CatPPT},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/rishiraj/CatPPT}}
}
