Model Card for smol_bruin-7b
Slerp merge of go-bruins-v2 and smol-7b.
.yaml file for mergekit
slices:
- sources:
- model: rwitz/go-bruins-v2
layer_range: [0, 32]
- model: rishiraj/smol-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-v0.1
parameters:
t:
- filter: self_attn
value: [0.44, 0.72, 0.61, 0.83, 1]
- filter: mlp
value: [0.56, 0.28, 0.39, 0.17, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 71.05 |
AI2 Reasoning Challenge (25-Shot) | 67.58 |
HellaSwag (10-Shot) | 86.48 |
MMLU (5-Shot) | 65.05 |
TruthfulQA (0-shot) | 55.65 |
Winogrande (5-shot) | 81.14 |
GSM8k (5-shot) | 70.43 |
- Downloads last month
- 1,303
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard67.580
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.480
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.050
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard55.650
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.140
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard70.430