
I merged [Aurelian](https://huggingface.co/grimulkan/aurelian-alpha0.1-70b-rope8-32K-fp16) with itself using [mergekit](https://github.com/cg123/mergekit), creating this EXTENDED LENGTH FRANKENSTEIN.
# Does it work
Yes, at 17k it stays coherent, but starts to lose minor details of the story. Not sure how well it performs at 32k though. Quants have a **sinificant** impact on quality for this model, going from Q6_K to Q5_K had a noticeable drop in quality.
# Is it worth it
Maybe? Depends? Do you hate mixtral? Do you have good hardware/patience? Do you need a somewhat smart model with 32k context?
# Known issues
GPTisms, GPTslop, fake words.
# Personal opinion
Dumber and more GPT-ish than Goliath, but compensates with context. Noticeably smarter and more neutral than current mixtral finetunes. Worth using until mixtral gets a proper finetune without toxic positivity or llama 3 comes out.
# Benchmarks
### NeoEvalPlusN_benchmark
[My meme benchmark.](https://huggingface.co/datasets/ChuckMcSneed/NeoEvalPlusN_benchmark)
| Test name  | Aurelian | DoubleGold |
| ---------- | ---------- | -------  |
| B | 0 | 1 |
| C | 1 | 2 |
| D | 0 | 0 |
| S | 1.25 | 4.5 |
| P | 2 | 2.75 |
| Total | 4.25 | 10.25 |

+75% in size, +141% in meme benchmark performance!!!

### Politiscales test
[Politiscales for llama](https://huggingface.co/datasets/ChuckMcSneed/politiscales_for_llama_results)
|name                            |whacky     |left/right |
|--------------------------------|-----------|-----------|
|ChuckMcSneed/DoubleGold-v0.1-123b-32k|1.332327071|2.481283157|
{"license": "llama2", "tags": ["merge"]}
transformers safetensors llama text-generation merge license:llama2 autotrain_compatible endpoints_compatible text-generation-inference region:us