README.md

Quantization made by Richard Erkhov.

[Github](https://github.com/RichardErkhov)

[Discord](https://discord.gg/pvy7H8DZMG)

[Request more models](https://github.com/RichardErkhov/quant_request)


llama-2-26b-trenchcoat-stack - GGUF
- Model creator: https://huggingface.co/chargoddard/
- Original model: https://huggingface.co/chargoddard/llama-2-26b-trenchcoat-stack/


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama-2-26b-trenchcoat-stack.Q2_K.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q2_K.gguf) | Q2_K | 8.87GB |
| [llama-2-26b-trenchcoat-stack.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.IQ3_XS.gguf) | IQ3_XS | 9.8GB |
| [llama-2-26b-trenchcoat-stack.IQ3_S.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.IQ3_S.gguf) | IQ3_S | 10.35GB |
| [llama-2-26b-trenchcoat-stack.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q3_K_S.gguf) | Q3_K_S | 10.35GB |
| [llama-2-26b-trenchcoat-stack.IQ3_M.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.IQ3_M.gguf) | IQ3_M | 10.96GB |
| [llama-2-26b-trenchcoat-stack.Q3_K.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q3_K.gguf) | Q3_K | 11.62GB |
| [llama-2-26b-trenchcoat-stack.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q3_K_M.gguf) | Q3_K_M | 11.62GB |
| [llama-2-26b-trenchcoat-stack.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q3_K_L.gguf) | Q3_K_L | 12.72GB |
| [llama-2-26b-trenchcoat-stack.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.IQ4_XS.gguf) | IQ4_XS | 2.4GB |
| [llama-2-26b-trenchcoat-stack.Q4_0.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q4_0.gguf) | Q4_0 | 13.51GB |
| [llama-2-26b-trenchcoat-stack.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.IQ4_NL.gguf) | IQ4_NL | 13.59GB |
| [llama-2-26b-trenchcoat-stack.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q4_K_S.gguf) | Q4_K_S | 9.59GB |
| [llama-2-26b-trenchcoat-stack.Q4_K.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q4_K.gguf) | Q4_K | 14.44GB |
| [llama-2-26b-trenchcoat-stack.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q4_K_M.gguf) | Q4_K_M | 5.12GB |
| [llama-2-26b-trenchcoat-stack.Q4_1.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q4_1.gguf) | Q4_1 | 14.99GB |
| [llama-2-26b-trenchcoat-stack.Q5_0.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q5_0.gguf) | Q5_0 | 16.48GB |
| [llama-2-26b-trenchcoat-stack.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q5_K_S.gguf) | Q5_K_S | 16.48GB |
| [llama-2-26b-trenchcoat-stack.Q5_K.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q5_K.gguf) | Q5_K | 16.96GB |
| [llama-2-26b-trenchcoat-stack.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q5_K_M.gguf) | Q5_K_M | 16.96GB |
| [llama-2-26b-trenchcoat-stack.Q5_1.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q5_1.gguf) | Q5_1 | 17.97GB |
| [llama-2-26b-trenchcoat-stack.Q6_K.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q6_K.gguf) | Q6_K | 19.64GB |
| [llama-2-26b-trenchcoat-stack.Q8_0.gguf](https://huggingface.co/RichardErkhov/chargoddard_-_llama-2-26b-trenchcoat-stack-gguf/blob/main/llama-2-26b-trenchcoat-stack.Q8_0.gguf) | Q8_0 | 25.44GB |




Original model description:
---
license: llama2
tags:
- merge
- mergekit
---

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.