---
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|