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---
license: llama3
license_name: llama3
license_link: LICENSE
library_name: transformers
tags:
- not-for-all-audiences
datasets:
- crestf411/LimaRP-DS
- Gryphe/Sonnet3.5-Charcard-Roleplay
- anthracite-org/c2_logs_32k_mistral-v3_v1.2_no_system
- anthracite-org/kalo-opus-instruct-22k-no-refusal-no-system
- anthracite-org/kalo-opus-instruct-3k-filtered-no-system
- anthracite-org/nopm_claude_writing_fixed
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---
**Slush** is a two-stage model trained with high LoRA dropout, where stage 1 is a pretraining continuation on the base model, aimed at boosting the model's creativity and writing capabilities. This is then merged into the instruction tune model, and stage 2 is a fine tuning step on top of this to further enhance its roleplaying capabilities and/or to repair any damage caused in the stage 1 merge.
This is an initial experiment done on the at-this-point-infamous Llama 3.1 8B model, in an attempt to retain its smartness while addressing its abysmal lack of imagination/creativity. As always, feedback is welcome, and begone if you demand perfection.
The second stage, like the *Sunfall* series, follows the Silly Tavern preset, so ymmv in particular if you use some other tool and/or preset.
**Feedback so far:** The model is a bit less careful (aka sloppy) with things like formatting, potentially due to the overly impressionable pretraining part. I am adjusting the approach a bit and will post an update.
**Parameter suggestions:**
I did all my testing with temp 1, min-p 0.1, DRY 0.8. I enabled XTC at higher contexts.
**Training details:**
* Stage 1 (continued pretraining)
* Target: meta-llama/Llama-3.1-8B (resulting LoRA merged into meta-llama/Llama-3.1-8B-Instruct)
* LoRA dropout 0.5 ([motivation](https://arxiv.org/abs/2403.00946))
* LoRA rank 64, alpha 128 ([motivation](https://arxiv.org/abs/2410.21228))
* LR constant 1e-5
* [LoRA+](https://arxiv.org/abs/2402.12354) with LR Ratio: 5
* Context size: 16384
* Gradient accumulation steps: 4
* Epochs: 1
* Stage 2 (fine tune)
* Target: Stage 1 model
* LoRA dropout 0.5
* LoRA rank 32, alpha 64
* LR cosine 1.5e-5 (min 1.5e-6)
* [LoRA+](https://arxiv.org/abs/2402.12354) with LR Ratio: 5
* Context size: 16384
* Gradient accumulation steps: 4
* Epochs: 1
Initially I tried to keep stage 2 ultra thin/light to only minimally repair the merge-damage and trying to retain the base model's IQ, but this did not work out well, so I ultimately ramped up the dataset to include external sources (see dataset metadata) to boost the RP capacity of the model.
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