NeuralPipe-7B-ties
This model is a merge of the following models made with mergekit:
âš¡ Quantized models
Thanks to TheBloke for the quantized models:
- GGUF: https://huggingface.co./TheBloke/NeuralPipe-7B-ties-GGUF
- AWQ: https://huggingface.co./TheBloke/NeuralPipe-7B-ties-AWQ
- GPTQ: https://huggingface.co./TheBloke/NeuralPipe-7B-ties-GPTQ
🧩 Configuration
models:
- model: mistralai/Mistral-7B-v0.1
# no parameters necessary for base model
- model: OpenPipe/mistral-ft-optimized-1218
parameters:
density: 0.5
weight: 0.5
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
parameters:
density: 0.5
weight: 0.3
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: true
int8_mask: true
dtype: float16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 71.55 |
AI2 Reasoning Challenge (25-Shot) | 67.92 |
HellaSwag (10-Shot) | 86.04 |
MMLU (5-Shot) | 64.24 |
TruthfulQA (0-shot) | 61.37 |
Winogrande (5-shot) | 80.19 |
GSM8k (5-shot) | 69.52 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard67.920
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.040
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.240
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard61.370
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard80.190
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard69.520