Triangle104
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README.md
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This model was converted to GGUF format from [`Sao10K/MN-BackyardAI-Party-12B-v1`](https://huggingface.co/Sao10K/MN-BackyardAI-Party-12B-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/Sao10K/MN-BackyardAI-Party-12B-v1) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`Sao10K/MN-BackyardAI-Party-12B-v1`](https://huggingface.co/Sao10K/MN-BackyardAI-Party-12B-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/Sao10K/MN-BackyardAI-Party-12B-v1) for more details on the model.
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Model Info:
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---
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---
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Trained with compute from Backyard.ai | Thanks to them and @dynafire for helping me out.
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Trained on 2x A100 SXM 40GB as an 8-bit LoRA.
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This is a group-chat based roleplaying model, based off of 12B-Lyra-v4a2, a variant of Lyra-v4 that is currently private.
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It is trained on an entirely human-based dataset, based on forum / internet group roleplaying styles. The only augmentation done with LLMs is to the character sheets, to fit to the system prompt, to fit various character sheets within context.
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This model is still capable of 1 on 1 roleplay, though I recommend using ChatML when doing that instead.
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---
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Formatting:
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Training for the multi-character roleplaying format is done with a variant of ChatML, replaced with [INST] blocks formatted as such. Use this to draw in more of the training done.
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[INST]system
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System Prompt Here[/INST]
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[INST]user
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User's Yapping[/INST]
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[INST]model
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Model Reply[/INST]
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Relevant!
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- Turns do not need to respect user -> model -> user. Training is done with disjointed turns that may have repeating turns to simulate real group roleplay / chat scenarios with multiple users.
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- Additional work may be required to fit for your front-end.
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- Ideally character cards are all included in the turns. Training is done with this in mind. Below on the page has relevant information.
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- This is a Nemo model, so lower Temperature and a sprinkling of min_p helps.
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- This does require a lot of tinkering to fit within SillyTavern / other frontends.
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To get better performance on Regular 1 on 1 Roleplay or Chat scenarios, use ChatML to get more of Lyra's performance.
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<|im_start|>system
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System Prompt Here.<|im_end|>
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<|im_start|>user
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User's Instructions<|im_end|>
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<|im_start|>assistant
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Model Response<|im_end|>
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For best results, set both <|im_end|> and [INST] as stopping strings. Recommended Temperature is <1 , min_p of ateast 0.1
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Dataset Information:
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This dataset is made from a human RP forum source, trimmed down, augmented and reformatted to fit.
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- Each entry has a minimum of 6 turns to be inside
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- Number of unique/main characters are ranged from 2 to 7 characters per entry.
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- Each conversation is kept as is to preserve quality and uniqueness of the human data.
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- Only the added system prompt makes use of the current character sheets given.
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The following below is how the current Character Card / Sheets is done, which are augmented from the messy and non-uniform character sheets available. To get best results, please reformat your current character data to the on as seen below, or as similar as you can if possible.
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- **Character Name**:
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- **Age**:
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- **Race**:
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- **Mageblood Type**: (if applicable)
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- **Favored Magic Class**: (if applicable)
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- **Previous Magic Training**: (if applicable)
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- **Occupation/Profession**: (if applicable)
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- **Appearance**: (if applicable)
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- **Biography**: (if applicable)
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- **Good Attributes**: (if applicable)
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- **Bad Attributes**: (if applicable)
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- **Equipment**: (if applicable)
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- **Other Information**: (if applicable)
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Here is an example based on the above format:
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**Character Name**: Keri Wolf
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**Age**: 21
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**Race**: Vampire
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**Mageblood Type**: Hydromancy
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**Favored Magic Class**: Aqua
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**Previous Magic Training**: Novice
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**Occupation/Profession**: None specified
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**Appearance**:
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- Height: 5'9"
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- A wooden wolf necklace around her neck, contrasting with her pale skin
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- Three swords strapped to her waist
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- A tattoo of a thorn vine, her family crest, on her right arm
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- Normal eye color is red but changes based on her mood or the topic of conversation
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- Carries a hunk of wood and a carving knife for personal activities
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**Biography**:
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Keri Wolf grew up in a family of adopted siblings in Djarkel. She had a normal childhood, with her best friend Satori, and was taught basic self-defense by her father. Her brothers were considered troublemakers but remained close to her. On her 21st birthday, her family was slaughtered by a vampire nest, and she was bitten. This led to her developing vampiric traits and seeking answers at the college.
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**Good Attributes**:
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- Easy-going
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- Observant
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- Helps those in trouble
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- Soft-hearted
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- Kind
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- Cool-headed
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- Good at getting out of difficult situations
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- Avoids violence
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- Gets along well with different people
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- Loves animals
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**Bad Attributes**:
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- Sunlight sensitivity
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- Hatred towards vampires outside the college
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- Keeps feelings in check, leading to dangerous outbursts
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- Cruel manner of speaking
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- Thirst for revenge
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**Equipment**:
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- Wooden wolf necklace
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- Three swords (one engraved with a rose, one engraved with her father's name, and one for decoration)
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- Carving knife
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- Hunk of wood
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- Stealth Ring
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- Knight's Shield
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**Other Information**:
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- Secret word: rebirth
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The following system prompt is augmented from available character sheets, or details from the original dataset. Placeholder names are given as shown.
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You are involved in a multi-character internet-style roleplaying session with a human user, who is playing as Ballbuster Steve. Do not generate dialogue for the user's character, Ballbuster Steve. Focus on the other characters.
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[Human User]
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Ballbuster Steve # {user}
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Character Bio: [Steve's bio]
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[Involved Characters]
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Altair "Arty" Enzo # {char1}
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Character Bio: [Arty's bio]
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---
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Sukuna Gojo # {char2}
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Character Bio: [Sukuna's bio]
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---
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The roleplay begins now.
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This is how some of the turn example looks like, newlines are only for visual use.
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[INST]user
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Ballbuster Steve: Being the doorman at a nightclub, especially one as popular as LUSH... [/INST]
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[INST]model
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Altair "Arty" Enzo: While he was waiting for Jake to answer, Arty noticed from the corner of his eye... [/INST]
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[INST]model
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Sukuna Gojo: Nick was now out of his element; he just came off his portable radio app... [/INST]
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[INST]user
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Ballbuster Steve: Steve grabbed his black clutch from where it was stashed under the mixing desk... [/INST]
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To make it easier, this is how I'd format responses for the backend:
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<s>[INST]system
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{system_prompt}[/INST]
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[INST]user
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{user}: {text}[/INST]
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[INST]model
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{char1}: {text}[/INST]
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[INST]model
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{char2}: {text}[/INST]
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[INST]user
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{user}: {text}[/INST]
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[INST]model
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{char1}: {text}[/INST]<|im_end|> # For Final Turn only. Alternatively, set <|im_end|> as a stopping string.
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Current Issues:
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- Impersonation - This is a common side-effect of pure human roleplaying data, unfortunately.
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Users do like writing the actions of others, though this is more limited to end of reply.
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- Varied Output Quality - A swipe should be enough?
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I only removed obviously bad entries. Output quality varies thanks to the variety of human users involved.
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- Character Detail Confusion when in group chats
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This rarely happens, but it is usually when there are too many main characters, or the bio is improperly formatted and seperated.
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Or if you're using an additional, complex system prompt.
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- Random OOC / Story Break moments may still exist despite me filtering the data.
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- Limited Dataset Size -> 4K Varied Samples ranging from 2-7 characters per entry. I'm looking to expand.
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- Limited System Prompt? -> I'm trying to improve on this.
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- Fantasy-bias? -> Most of the entries are fantasy-based after all.
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Training Metrics
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n_sample: 4000
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n_gpu: 2
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global batch size: 12
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lora: bnb_8bit
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no. epochs: 3
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lr: 0.000004
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lr_scheduler: cosine
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deepspeed: zero2
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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