We use state-of-the-art Language Model Evaluation Harness to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.

Model Details

Trained by: Luiz G A Alves
Model type: tinyllama-1.1b-chat-v0.3_platypus is an auto-regressive language model based on the tinyllama transformer architecture.
Language(s): English

How to use:

python

# Use a pipeline as a high-level helper
>>> from transformers import pipeline
>>> pipe = pipeline("text-generation", model="lgaalves/tinyllama-1.1b-chat-v0.3_platypus")
>>> question = "What is a large language model?"
>>> answer = pipe(question)
>>> print(answer[0]['generated_text'])

or, you can load the model direclty using:

python

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lgaalves/tinyllama-1.1b-chat-v0.3_platypus")
model = AutoModelForCausalLM.from_pretrained("lgaalves/tinyllama-1.1b-chat-v0.3_platypus")

Training Dataset

lgaalves/tinyllama-1.1b-chat-v0.3_platypus trained using STEM and logic based dataset garage-bAInd/Open-Platypus.

Training Procedure

lgaalves/tinyllama-1.1b-chat-v0.3_platypus was instruction fine-tuned using LoRA on 1 V100 GPU on Google Colab. It took about 43 minutes to train it.

Intended uses, limitations & biases

You can use the raw model for text generation or fine-tune it to a downstream task. The model was not extensively tested and may produce false information. It contains a lot of unfiltered content from the internet, which is far from neutral.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.28
ARC (25-shot)	30.29
HellaSwag (10-shot)	55.12
MMLU (5-shot)	26.13
TruthfulQA (0-shot)	39.15
Winogrande (5-shot)	55.8
GSM8K (5-shot)	0.53
DROP (3-shot)	4.94