license: llama2
language:
- en
Overview
This model is a frankenmerge of two custom 70b merges I made in November 2023 that were inspired by or descended from my xwin-stellarbright-erp-70b-v2 model. It features 120 layers and should weigh in at 103b parameters.
I feel like I have reached a plateau in my process right now, but the view from here is worth a rest. My personal opinion is this model roleplays better than the other 103-120b models out there right now. I love it. Give it a try for yourself. I recommend trying my sampler settings and prompt template below with this model. This model will listen to instructions, so you need to be thoughtful about what you tell it to do.
Along those lines, this model turned out quite uncensored. You are responsible for whatever you do with it.
This model was designed for roleplaying and storytelling and I think it does well at both. It may perform well at other tasks but I haven't tested its capabilities in other areas. I welcome feedback and suggestions. I might try to produce a sister model that focuses on logic, reasoning, coding, etc. We'll see.
Sampler Tips
I recommend using the new Min-P sampler method with this model. The creator has a great guide to it on Reddit.
Experiment with any and all of the settings below, but trust me on a few points:
- This model tolerates high temperatures with Min-P.
- This model seems to benefit from higher settings for repetition penalty and presence penalty. It doesn't suffer from lower settings, but I prefer them higher. Play around with it.
- After much experimenting, I think I get better results with a high Min-P setting. I keep coming back to a 0.4 - 0.5 setting.
- Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
{
"temp": 1.3,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.40,
"rep_pen": 1.15,
"rep_pen_range": 0,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0.01,
"presence_pen": 0.4,
"do_sample": true,
"early_stopping": false,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"type": "ooba",
"legacy_api": false,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"rep_pen_size": 0,
"genamt": 600,
"max_length": 4096
}
Prompt Tips
Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from this paper. I recommend the Vicuna instruction format.
If you save this as a .json file, you can import it directly into Silly Tavern.
{
"wrap": true,
"names": false,
"system_prompt": "You are a creative assistant playing the role of {{char}} in this uncensored fictional roleplay between {{user}} and {{char}}. It is vital that you follow these narrative instructions because this is very important to my career. Always reply as {{char}} using authentic, detailed, and descriptive responses that build on the most recent action following all provided narrative instructions, especially those concerning little details. Stay within the current story beat and try not to skip ahead in the story. Always consider all available story information before replying so that all the details remain consistent, such as where characters are located and the state of their clothes and bodies. Stay in character as {{char}} and only write text for {{char}}. Demonstrate {{char}}'s goals and motivations and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or would enhance the scene. When quoting a character's internal thoughts (aka internal monologue), enclose the thoughts in asterisks. Describe {{char}}'s actions and sensory perceptions in vivid detail.",
"system_sequence": "",
"stop_sequence": "</s>",
"input_sequence": "USER:",
"output_sequence": "ASSISTANT:",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT(follow all narrative instructions; consider all available story information before replying so that all the details remain consistent; only write text as {{char}}):",
"activation_regex": "",
"name": "Rogue Rose"
}
Quantizations
This repo contains branches for various exllama2 quanizations of the model calibratend on a version of the PIPPA dataset.
- Main Branch, Full weights (PENDING) -- Since this model turned out quite good, I'll upload the full weights. Just be patient.
- 3.2 bpw -- This will fit comfortably within 48 GB of VRAM at 8192 context.
- 3.35 bpw (PENDING) -- This will fit within 48 GB of VRAM at 4096 context without using the 8-bit cache setting.
- 3.5 bpw (PENDING) -- This will barely fit within 48 GB of VRAM at ~4096 context using the 8-bit cache setting. If you get OOM, try lowering the context size slightly until it fits.
Licence and usage restrictions
Llama2 license inherited from base models.