metadata
license: apache-2.0
tags:
- merge
model-index:
- name: CatPPT-base
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 67.92
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rishiraj/CatPPT-base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.64
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rishiraj/CatPPT-base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.26
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rishiraj/CatPPT-base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 61.72
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rishiraj/CatPPT-base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.29
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rishiraj/CatPPT-base
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 70.66
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=rishiraj/CatPPT-base
name: Open LLM Leaderboard
😼 CatPPT
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}}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 72.25 |
| AI2 Reasoning Challenge (25-Shot) | 67.92 |
| HellaSwag (10-Shot) | 86.64 |
| MMLU (5-Shot) | 65.26 |
| TruthfulQA (0-shot) | 61.72 |
| Winogrande (5-shot) | 81.29 |
| GSM8k (5-shot) | 70.66 |