Text Generation
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Safetensors
mistral
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Eval Results (legacy)
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("nlpguy/Westgate")
model = AutoModelForCausalLM.from_pretrained("nlpguy/Westgate")Quick Links
merged
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model:
model:
path: senseable/garten2-7b
dtype: float16
merge_method: slerp
parameters:
t:
- value: [0.0, 0.3, 0.5, 0.7, 1.0]
slices:
- sources:
- layer_range: [0, 32]
model:
model:
path: jsfs11/TurdusTrixBeagle-DARETIES-7B
- layer_range: [0, 32]
model:
model:
path: senseable/garten2-7b
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 73.84 |
| AI2 Reasoning Challenge (25-Shot) | 71.42 |
| HellaSwag (10-Shot) | 88.14 |
| MMLU (5-Shot) | 65.11 |
| TruthfulQA (0-shot) | 62.59 |
| Winogrande (5-shot) | 85.71 |
| GSM8k (5-shot) | 70.05 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard71.420
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.140
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.110
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard62.590
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard85.710
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.050
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nlpguy/Westgate")