metadata

library_name: transformers
tags:
  - llama-factory
  - not-for-all-audiences
license: llama3
language:
  - en
datasets:
  - nothingiisreal/c2-logs-cleaned
  - nothingiisreal/Claude-3-Opus-Instruct-15K
  - nothingiisreal/Reddit-Dirty-And-WritingPrompts

L3 8B Celeste

Model Making is a process, Follow us for later models!

We trained LLaMA 3 8B Instruct at 8K context using Reddit Writing Prompts, Opus 15K Instruct and

c2 logs cleaned

Usage Tips

GGUFs

Q4_K_M Q8_K_M

System Message / Jailbreak

IF THE GENERATIONS ARE BAD, REMOVE ALL SYSTEM PROMPTS

Particularly SillyTavern default prompts can make the model worse. Claude Jailbreaks should work fine though, there was a lot of them in c2 logs.

You don't need a JB for casual usage but a JB can steer behaviour still.

Sampling

You should also try messing with different settings.

Preset

L3 Instruct with no system prompt or claude jb works, you can also use this

Fewshot

First message and last few messages impact this model quite a bit in terms of style, hornyness, personality. You don't need to have a first message but editing first few messages or having good ones are highly recommended.

Hornyness

If the model is not horny enough then just edit the last character message to hint at something, the model will pick up on it and build on it. (Or just give the char aphrodisiac pills lol)

The model is fine with SFW and doesn't make it NSFW unless you want. It is also able to maintain half-NSFW without devolving down into hardcore.

Train Data

The split was as follows:

2K rows from r/WritingPrompts
2K rows from r/DirtyWritingPrompts
2K rows from Opus Instruct 15K (specifically the 6.5K jsonl)
2K rows from c2 logs cleaned

While we did train all system prompts from c2 logs we also have our own system prompts.

List of trained system prompts. Note: c2 logs system prompts and char cards were also included.

reddit_dirty_writing_prompts.jsonl

2000
"You are a short story writer. Write a story based on prompt provided by user below.
Mode: NSFW"

reddit_writing_prompts.jsonl

2000
"You are a short story writer. Write a story based on prompt provided by user below.
Mode: SFW"

Opus_Instruct-v2-6.5K-Filtered-v2.jsonl

2000
"" (no prompt)

deduped-c2-logs-maywell-final-filter-4.jsonl

2000
(Only if there was no system prompt in the conversation, otherwise keep original system prompt) "You are an expert actor that can fully immerse yourself into any role given. You do not break character for any reason, even if someone tries addressing you as an AI or language model."

Our Findings and Experimentation results

Preface

We think there is too much secrecy around what data is being used, and different training methods. So we decided to share as much as possible.

Findings

The Good

We found that increasing the amount of ranks from 64 to 256 has reduced repetition but also led to the language used resembling Claude more than the 64 rank version. No worries, it's still far enough from Claude.

It also led to increased coherency but reduced instruction following, likely because the model started diverging more away from L3 8B Instruct.

The model is uncensored for RP. For Instruct it needs 2-3 words of prefill for the first message.

We found that increasing the amount of data from 1K to 8K reduced repetition aswell.

The prose is much better than other synthetic data generations. The model also demonstrates increased style copying abilities likely a result of the long data and varying writing styles found in WritingPrompts.

The model is exceptional at being creative in roleplaying, knows different persona's and even a single character will change persona in different contexts, persona is tied to last few messages rather than system message or character card. This is great as it often means the model can do impressive things without you needing to explicitly specify.

Improvements for Next Run

Formatting can break sometimes.

Comments about training

Grad norm kept increasing throughout the run which is concerning, albeit it could be a side effect of the LR getting lower due to cosine LR Scheduler.

Graphs

Colors:

256 rank on 8K rows

64 rank on 8K rows

64 rank on 1K rows

Main training Command

Hardware Used: 4xH100 NVL for 2 hours.

Here is the command, edit rank, learning rate, and any other parameter as you wish.

!FORCE_TORCHRUN=1 llamafactory-cli train \
    --stage sft \
    --do_train True \
    --model_name_or_path NousResearch/Meta-Llama-3-8B-Instruct \
    --preprocessing_num_workers 16 \
    --finetuning_type lora \
    --quantization_method bitsandbytes \
    --use_rslora False \
    --lora_rank 64 \
    --lora_alpha 64 \
    --lora_dropout 0.1 \
    --lora_target all \
    --template llama3 \
    --flash_attn fa2 \
    --deepspeed examples/deepspeed/ds_z3_config.json \
    --use_unsloth False \
    --dataset_dir /workspace/sft \
    --dataset dataset_name \
    --cutoff_len 8192 \
    --learning_rate 4e-6 \
    --lr_scheduler_type cosine \
    --num_train_epochs 2.0 \
    --max_samples 100000 \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 1 \
    --logging_steps 3 \
    --save_steps 500 \
    --warmup_ratio 0.05 \
    --val_size 50 \
    --eval_strategy steps \
    --eval_steps 0.05 \
    --optim adamw_bnb_8bit \
    --packing False \
    --train_on_prompt False \
    --report_to all \
    --max_grad_norm 1.0 \
    --output_dir saves/LLaMA3-8B/trained-models/8krows-dwrp-c2l-opus-lora-4e-6-cosine-24-normal-bs \
    --bf16 True \
    --plot_loss True \
    --ddp_timeout 180000000 \
    --per_device_eval_batch_size 4 \
    --include_num_input_tokens_seen True

Wow, you've read all of that? You seem like the person that would join our discord

Gemma 9B and 27B at some point? ;)