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---
license: llama2
tags:
- merge
- mergekit
model-index:
- name: llama-2-26b-trenchcoat-stack
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 55.03
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chargoddard/llama-2-26b-trenchcoat-stack
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 79.9
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chargoddard/llama-2-26b-trenchcoat-stack
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 53.73
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chargoddard/llama-2-26b-trenchcoat-stack
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.48
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chargoddard/llama-2-26b-trenchcoat-stack
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 74.74
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chargoddard/llama-2-26b-trenchcoat-stack
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 2.88
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chargoddard/llama-2-26b-trenchcoat-stack
name: Open LLM Leaderboard
---
Llama 2 13b is a pretty decent language model. You know what's probably better? *Two* Llama 2 13b models. In a trenchcoat.
Produced by [`bakllama.py`](https://github.com/cg123/mergekit/blob/main/bakllama.py) with this config file:
```yml
layer_slices:
- model: TheBloke/Llama-2-13B-fp16
start: 0
end: 40
- model: TheBloke/Llama-2-13B-fp16
start: 0
end: 40
```
No fine tuning was done on this model. Yes, it's still coherent somehow.
Benchmark results:
| Benchmark | Llama2-13b | Llama2-26b-tcs | Percent Change |
| --- | --- | --- | --- |
| ARC | 59.3 | 55.03 | -7.2% |
| HellaSwag | 82.15 | 79.9 | -2.74% |
| MMLU | 55.67 | 53.73| -3.48% |
| TruthfulQA | 37.39 | 40.48 | +5.59% |
| Average | 58.63 | 57.29 | -2.29% |
| Average Minus TQA | 65.70 | 62.85 | -4.34% |
This tells us two very important things:
1. TruthfulQA is a perfect benchmark in every way.
2. Llama models are amazingly robust to being fed their own output.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__llama-2-26b-trenchcoat-stack)
| Metric |Value|
|---------------------------------|----:|
|Avg. |51.13|
|AI2 Reasoning Challenge (25-Shot)|55.03|
|HellaSwag (10-Shot) |79.90|
|MMLU (5-Shot) |53.73|
|TruthfulQA (0-shot) |40.48|
|Winogrande (5-shot) |74.74|
|GSM8k (5-shot) | 2.88|
|