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| Metric | mistral-7b_open_platypus | mistralai/Mistral-7B-v0.1 | garage-bAInd/Platypus2-7B |
|---|---|---|---|
| Avg. | - | 62.40 | 56.13 |
| ARC (25-shot) | - | 59.98 | 55.20 |
| HellaSwag (10-shot) | - | 83.31 | 78.84 |
| MMLU (5-shot) | - | 64.16 | 49.83 |
| TruthfulQA (0-shot) | - | 42.15 | 40.64 |
1# Use a pipeline as a high-level helper
2>>> from transformers import pipeline
3>>> pipe = pipeline("text-generation", model="lgaalves/mistral-7b_open_platypus")
4>>> question = "What is a large language model?"
5>>> answer = pipe(question)
6>>> print(answer[0]['generated_text'])
71# Load model directly
2from transformers import AutoTokenizer, AutoModelForCausalLM
3
4tokenizer = AutoTokenizer.from_pretrained("lgaalves/mistral-7b_open_platypus")
5model = AutoModelForCausalLM.from_pretrained("lgaalves/mistral-7b_open_platypus") "<s>[INST] What is your favourite condiment? [/INST]"lgaalves/mistral-7b_open_platypus trained using STEM and logic based dataset garage-bAInd/Open-Platypus.lgaalves/mistral-7b_open_platypus was instruction fine-tuned using LoRA on 1 Tesla V100-SXM2-16GB. In total, it took 11 hours to fine tune the model.| Metric | Value |
|---|---|
| Avg. | 49.19 |
| ARC (25-shot) | 55.8 |
| HellaSwag (10-shot) | 82.13 |
| MMLU (5-shot) | 59.76 |
| TruthfulQA (0-shot) | 48.87 |
| Winogrande (5-shot) | 78.61 |
| GSM8K (5-shot) | 12.59 |
| DROP (3-shot) | 6.59 |