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--- |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: mistralai/Mixtral-8x7B-v0.1 |
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model-index: |
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- name: limarp-zloss-mixtral-8x7b-qlora |
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results: [] |
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datasets: |
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- lemonilia/LimaRP |
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language: |
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- en |
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license: apache-2.0 |
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--- |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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# limarp-zloss-mixtral-8x7b-qlora |
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Experimental limarp qlora trained at 10k ctx length (greater than size of the longest limarp sample when tokenized via mistral's tokenizer) on [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) using [Charles Goddard](https://huggingface.co/chargoddard)'s ZLoss and Megablocks-based fork of transformers. |
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## Model description |
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The intended prompt format is the Alpaca instruction format of LimaRP v3: |
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``` |
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### Instruction: |
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Character's Persona: {bot character description} |
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User's Persona: {user character description} |
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Scenario: {what happens in the story} |
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Play the role of Character. Taking the above information into consideration, you must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User. |
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### Input: |
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User: {utterance} |
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### Response: |
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Character: {utterance} |
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### Input: |
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User: {utterance} |
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### Response: |
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Character: {utterance} |
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(etc.) |
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``` |
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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: |
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``` |
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### Input |
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User: {utterance} |
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### Response: (length = medium) |
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Character: {utterance} |
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``` |
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This has an immediately noticeable effect on bot responses. The lengths using during training are: |
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`micro`, `tiny`, `short`, `medium`, `long`, `massive`, `huge`, `enormous`, `humongous`, `unlimited`. |
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**The recommended starting length is medium**. Keep in mind that the AI can ramble or impersonate |
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the user with very long messages. |
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The length control effect is reproducible, but the messages will not necessarily follow |
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lengths very precisely, rather follow certain ranges on average, as seen in this table |
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with data from tests made with one reply at the beginning of the conversation: |
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 |
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Response length control appears to work well also deep into the conversation. **By omitting |
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the modifier, the model will choose the most appropriate response length** (although it might |
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not necessarily be what the user desires). |
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## Intended uses & limitations |
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The model will show biases similar to those observed in niche roleplaying forums on the Internet, besides those exhibited by the base model. |
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## Training and evaluation data |
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For more details about LimaRP, see the dataset page. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 3 |
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### Framework versions |
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- Transformers 4.37.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: bfloat16 |
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### Framework versions |
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- PEFT 0.6.0 |
