transformers safetensors mistral text-generation not-for-all-audiences license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
---

# NeuraLake-m7-AshhLimaRP-Mistral-7B ⚡

- **Base model :** [Meggido/NeuraLake-m7-7B](https://huggingface.co/Meggido/NeuraLake-m7-7B)
- **LoRA adapter :** [lemonilia/AshhLimaRP-Mistral-7B](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B)

Many thanks to [Herman555](https://huggingface.co/Herman555) for his invaluable help 🤗.

## 📜 Prompt format
[Extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca),
with `### Instruction:`, `### Input:` immediately preceding user inputs and `### Response:`
immediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn
responses, in practice this is not a problem; the format follows a pattern already used by
other models.

```
### Instruction:
Character's Persona: {bot character description}

User's Persona: {user character description}

Scenario: {what happens in the story}

Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.

### Input:
User: {utterance}

### Response:
Character: {utterance}

### Input
User: {utterance}

### Response:
Character: {utterance}

(etc.)
```
### Message length control
Inspired by the previously named "Roleplay" preset in SillyTavern, with this
version of LimaRP it is possible to append a length modifier to the response instruction
sequence, like this:

```
### Input
User: {utterance}

### Response: (length = medium)
Character: {utterance}
```
This has an immediately noticeable effect on bot responses. The lengths using during training are:
`micro`, `tiny`, `short`, `medium`, `long`, `massive`, `huge`, `enormous`, `humongous`, `unlimited`.
**The recommended starting length is medium**. Keep in mind that the AI can ramble or impersonate
the user with very long messages.

The length control effect is reproducible, but the messages will not necessarily follow
lengths very precisely, rather follow certain ranges on average, as seen in this table
with data from tests made with one reply at the beginning of the conversation:

![lengths](https://i.imgur.com/2WXGgaV.png)

Response length control appears to work well also deep into the conversation. **By omitting
the modifier, the model will choose the most appropriate response length** (although it might
not necessarily be what the user desires).

➡️ **More info :** [lemonilia/AshhLimaRP-Mistral-7B](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B)