Don't mind those at the moment, I need to finetune them for RP, it's just some tests.

WARNING: This model specifically need EOS token I completely forgot to put on the json files, and need to check what was the right ones trough the mix. Please don't use it like this if you really want to review it.

slices:
  - sources:
    - model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
      layer_range: [0, 24]
  - sources:
    - model: "/content/drive/MyDrive/Zephyr-7B"
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

================================================

slices:
  - sources:
      - model: "/content/drive/MyDrive/Mistral-11B-CC-Zephyr"
        layer_range: [0, 48]
      - model: Undi95/Mistral-11B-OpenOrcaPlatypus
        layer_range: [0, 48]
merge_method: slerp
base_model: "/content/drive/MyDrive/Mistral-11B-CC-Zephyr"
parameters:
  t:
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16

hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-Test), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	0.5623	±	0.0145
		acc_norm	0.5794	±	0.0144
arc_easy	0	acc	0.8354	±	0.0076
		acc_norm	0.8165	±	0.0079
hellaswag	0	acc	0.6389	±	0.0048
		acc_norm	0.8236	±	0.0038
piqa	0	acc	0.8139	±	0.0091
		acc_norm	0.8264	±	0.0088
truthfulqa_mc	1	mc1	0.3978	±	0.0171
		mc2	0.5607	±	0.0155
winogrande	0	acc	0.7451	±	0.0122

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	53.06
ARC (25-shot)	64.08
HellaSwag (10-shot)	84.24
MMLU (5-shot)	64.0
TruthfulQA (0-shot)	56.19
Winogrande (5-shot)	78.45
GSM8K (5-shot)	16.15
DROP (3-shot)	8.35