Metric	mistral-7b-v0.1-platypus1k	mistralai/Mistral-7B-v0.1	garage-bAInd/Platypus2-7B
Avg.	63.66	62.4	56.13
ARC (25-shot)	61.60	59.98	55.20
HellaSwag (10-shot)	82.93	83.31	78.84
MMLU (5-shot)	63.16	64.16	49.83
TruthfulQA (0-shot)	46.96	42.15	40.64

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: mistral-7b-v0.1-platypus1k is an auto-regressive language model based on the Mistral-7B 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/mistral-7b-v0.1-platypus1k")
>>> 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/mistral-7b-v0.1-platypus1k")
model = AutoModelForCausalLM.from_pretrained("lgaalves/mistral-7b-v0.1-platypus1k")

Training Dataset

lgaalves/mistral-7b-v0.1-platypus1k trained using STEM and logic based dataset garage-bAInd/Open-Platypus.

Training Procedure

lgaalves/mistral-7b-v0.1-platypus1k was instruction fine-tuned using LoRA on 1 Tesla V100-SXM2-16GB.

Limitations and bias

Mistral 7B and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	50.74
ARC (25-shot)	61.6
HellaSwag (10-shot)	82.93
MMLU (5-shot)	63.16
TruthfulQA (0-shot)	46.96
Winogrande (5-shot)	78.14
GSM8K (5-shot)	16.38
DROP (3-shot)	5.99

lgaalves/mistral-7b-platypus1k

mistral-7b-v0.1-platypus1k

Benchmark Metrics