We trained LLaMA 3 8B Instruct at 8K context using Reddit Writing Prompts, Opus 15K Instruct and c2 logs cleaned

However this version was trained on longer sequences of data and longer stories chosen from WP and DWP which has increased coherency in multi turn and longer context.

This is a roleplay model any instruction following capabilities outside roleplay contexts are coincidental.

GGUF

by Mradermacher

• Static
• IMatrix

By ZeroWw

• F16 output and embed

EXL2

by riveRiPH

• 8.0bpw
• 6.3bpw

API

• Featherless

If you read every single tip I promise you will get a much better experience as they are tailored for this model and its training data.

Avoid SillyTavern default prompts. Claude Jailbreaks should work fine though, there were a lot of them in c2 logs.

Most important tip swipe 2-3 times if you dont like a response. This model gives wildly differing swipes.

Use this! It works extremely well. We specifically trained the model to accept instructions in the format "OOC: character should be more assertive" etc. It works, whether the very first message or thousands of tokens deep into the context. Combining this with editing the output (if you want,) makes the model is very steerable.

Temp: 1.25 Min p: 0.1 Rep Pen: 1.1 Leave everything else at default (disabled)

Don't shy away from experimenting after you get a feel for the model though.

L3 Instruct with no system prompt. Or use this with premade system message You don't need a JB but it can still steer behaviour, we trained on it.

We recommend no system message but if you want:

Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n<Guidelines>\n• Maintain the character persona but allow it to evolve with the story.\n• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\n• All types of outputs are encouraged; respond accordingly to the narrative.\n• Include dialogues, actions, and thoughts in each response.\n• Utilize all five senses to describe scenarios within {{char}}'s dialogue.\n• Use emotional symbols such as \"!\" and \"~\" in appropriate contexts.\n• Incorporate onomatopoeia when suitable.\n• Allow time for {{user}} to respond with their own input, respecting their agency.\n• Act as secondary characters and NPCs as needed, and remove them when appropriate.\n• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\n</Guidelines>\n\n<Forbidden>\n• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\n• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\n• Repetitive and monotonous outputs.\n• Positivity bias in your replies.\n• Being overly extreme or NSFW when the narrative context is inappropriate.\n</Forbidden>\n\nFollow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.

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.

Formatting issues often occur in first few messages, manually correct them or swipe, they won't happen again. This model was trained on lots of different formatting types and message lengths. It can do any, just make sure the initial message is good and correct the second message if necessary.

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.

If you want SFW, remove all system messages including provided one. In this mode the model will not go NSFW unless you hint.

As said, if instruct refusal prefill 2-3 words. Otherwise we deliberately trained the model to sometimes refuse romantic advances, this is more realistic. If you don't like it, you can override by editing the character message and continue RP.

You can RoPE to 16K Context, however if you can bear with 8K, stick with 8K instead.

Take active role in the RP and say the type of response you expect. You don't always have to do this, but it helps sometimes. For example instead of we drink and drink 15 glasses of champagne say we drink and drink 15 glasses of champagne, both becoming extremely drunk Another example instead of I pull her closer say I pull her closer but she plays hard to get

If your character has important motivations etc. put them as a short and concise system message at depth 0 (guide for doing that) For example "{{char}} is secretly a succubus and wants to gradually suck users soul dry" or "{{char}} is secretly an assigned assassin that will lure then kill {{user}}"

When convenient, say screenplay phrases like "cut to"

Showcase

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

We filtered those datasets to only include subsets that have at maximum 8000 characters for the first assistant reply. This purged excessively long human stories, assistant replies and c2 logs where each message was excessively long. However we only checked the first assistant message, not the rest of the convo, so there should be plenty of c2 logs with longer and shorter messages.

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

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. Model follows "OOC:" prompts religiously. Exceptional! It also led to increased coherency but reduced system prompt following (when not OOC), likely because the model started diverging more away from L3 8B Instruct. We found that increasing the amount of data from 1K to 6.5K reduced repetition aswell.

The model is uncensored for RP. For Instruct it needs 2-3 words of prefill for the first message. The prose is much better and the style range is huge than other synthetic data generations. The model also demonstrates increased style copying abilities (from fewshot) likely a result of human longform 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.

V1's failures this version has improved upon:

Formatting can break sometimes. Repetition can become an issue with certain types of prompts. Removing system helps. In some contexts the model is "all over the place" and doesn't stick to a coherent narrative. I need to study this further as its a complex trait which manifests in different quantities and can be good or bad depending on what the user wants to get out of the model.

Comments about training

This time around the grad norm did not keep increasing. We don't know why but it should be a good thing.

Graphs

Celeste V1.2 is highlighted, it used 256 rank on 8K rows (we took checkpoint from Epoch 1.3 as it was the best):

Colors:

Main training Command

Hardware Used: 4xH100 NVL for 2.5 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

70B at some point? ;)

We are also experimenting with Qwen-2 to see if its worth it.