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README.md

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+# AshhLimaRP-Mistral-7B (Alpaca, v1)
+
+This is a version of LimaRP with 2000 training samples _up to_ about 9k tokens length
+finetuned on [Ashhwriter-Mistral-7B](https://huggingface.co/lemonilia/Ashhwriter-Mistral-7B).
+
+LimaRP is a longform-oriented, novel-style roleplaying chat model intended to replicate the experience
+of 1-on-1 roleplay on Internet forums. Short-form, IRC/Discord-style RP (aka "Markdown format")
+is not supported. The model does not include instruction tuning, only manually picked and
+slightly edited RP conversations with persona and scenario data.
+
+Ashhwriter, the base, is a model entirely finetuned on human-written lewd stories.
+
+## Available versions
+- Float16 HF weights (_to be uploaded_)
+- LoRA Adapter ([adapter_config.json](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_config.json) and [adapter_model.bin](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_model.bin))
+- 4bit AWQ (_to be uploaded_)
+- [Q4_K_M GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q4_K_M.gguf)
+- [Q6_K GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q6_K.gguf)
+
+## 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.)
+```
+
+You should:
+- Replace all text in curly braces (curly braces included) with your own text.
+- Replace `User` and `Character` with appropriate names.
+
+
+### 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).
+
+## Suggested settings
+You can follow these instruction format settings in SillyTavern. Replace `medium` with
+your desired response length:
+
+![settings](https://files.catbox.moe/fpieug.png)
+
+## Text generation settings
+These settings could be a good general starting point:
+
+- TFS = 0.90
+- Temperature = 0.70
+- Repetition penalty = ~1.11
+- Repetition penalty range = ~2048
+- top-k = 0 (disabled)
+- top-p = 1 (disabled)
+
+## Training procedure
+[Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
+on 2x NVidia A40 GPUs.
+
+The A40 GPUs have been graciously provided by [Arc Compute](https://www.arccompute.io/).
+
+### Training hyperparameters
+A lower learning rate than usual was employed. Due to an unforeseen issue the training
+was cut short and as a result 3 epochs were trained instead of the planned 4. Using 2 GPUs,
+the effective global batch size would have been 16.
+
+Training was continued from the most recent LoRA adapter from Ashhwriter, using the same
+LoRA R and LoRA alpha.
+
+- lora_model_dir: /home/anon/bin/axolotl/OUT_mistral-stories/checkpoint-6000/
+- learning_rate: 0.00005
+- lr_scheduler: cosine
+- noisy_embedding_alpha: 3.5
+- num_epochs: 4
+- sequence_len: 8750
+- lora_r: 256
+- lora_alpha: 16
+- lora_dropout: 0.05
+- lora_target_linear: True
+- bf16: True
+- fp16: false
+- tf32: True
+- load_in_8bit: True
+- adapter: lora
+- micro_batch_size: 2
+- optimizer: adamw_bnb_8bit
+- warmup_steps: 10
+- optimizer: adamw_torch
+- flash_attention: true
+- sample_packing: true
+- pad_to_sequence_len: true
+
+
+### Loss graphs
+Values are higher than typical because the training is performed on the entire
+sample, similar to unsupervised finetuning.
+
+#### Train loss
+![Train loss](https://files.catbox.moe/ovw8c7.png)
+
+#### Eval loss
+![Eval loss](https://files.catbox.moe/yp7o0h.png)