Chatty-2x8B
Description
After some testing, finetuning and multiple merges of Llama-3 LLM models, here is something a little different.
This model is a MoE of 2x Llama-3 model trained on different RP format.
This repo contains FP16 files of Chatty-2x8B.
The idea
I started with two separate Llama-3-Instruct-8B models, each fine-tuned for specific RP formats.
Here is two simple exemple of how it was trained.
- Expert 1: This model is trained to handle RP that requires actions and descriptions between asterisks. For example:
*nods* Yes, I understand. - Expert 2: This model is fine-tuned for plain text RP where characters’ dialogues and actions are described straightforwardly. For example:
Nods. "Yes, I understand."
My initial idea was to make a 11B or bigger Llama-3 model, or just make a 2x8B from existing model, but I got some issues, they were not stable enough, even after DPO and FFT on top my frankenmerge/moe of Llama-3, it was not working well enough to release them.
So I just tried the idea of having 2 different RP format trained on 2 separated Llama-3-Instruct-8B, and it worked pretty well!
The dataset
Based on Lumimaid 8B OAS success I still used the same "balance" between RP and non RP in the dataset, the maximum was 50% non RP data on each side.
RP data was different with some exception, the non RP data was exactly the same, despite that, I can't produce repetition so the double usage of non RP datasets didn't hurt the model in the end.
Prompt template: Llama3
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
Others
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IkariDev: Visit my retro/neocities style website please kek
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| arc_challenge | 1 | none | 0 | acc | 0.5469 | ± | 0.0145 |
| none | 0 | acc_norm | 0.5853 | ± | 0.0144 | ||
| arc_easy | 1 | none | 0 | acc | 0.8308 | ± | 0.0077 |
| none | 0 | acc_norm | 0.8258 | ± | 0.0078 | ||
| gsm8k | 3 | strict-match | 5 | exact_match | 0.7149 | ± | 0.0124 |
| flexible-extract | 5 | exact_match | 0.7096 | ± | 0.0125 | ||
| hellaswag | 1 | none | 0 | acc | 0.5945 | ± | 0.0049 |
| none | 0 | acc_norm | 0.7806 | ± | 0.0041 | ||
| piqa | 1 | none | 0 | acc | 0.7943 | ± | 0.0094 |
| none | 0 | acc_norm | 0.7998 | ± | 0.0093 | ||
| truthfulqa_mc2 | 2 | none | 0 | acc | 0.5097 | ± | 0.0150 |
| winogrande | 1 | none | 0 | acc | 0.7356 | ± | 0.0124 |
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