Text Generation
Transformers
PyTorch
mistral
Not-For-All-Audiences
nsfw
text-generation-inference
Inference Endpoints
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README.md
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---
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tags:
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- name: out
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results: []
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---
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<!--
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<details><summary>See axolotl config</summary>
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```yaml
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base_model: alpindale/Mistral-7B-v0.2-hf
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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load_in_4bit: false
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strict: false
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- path: ./datasets/ToxicQAFinal.parquet
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type: sharegpt
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conversation: chatml
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- path: ./datasets/aesir-3-sfw_names-replaced.json
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type: sharegpt
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conversation: chatml
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- path: ./datasets/aesir-3-nsfw_names-replaced.json
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type: sharegpt
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conversation: chatml
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- path: ./datasets/aesir2_modified_sharegpt.json
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type: sharegpt
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conversation: chatml
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- path: ./datasets/aesir_modified_sharegpt.json
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type: sharegpt
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conversation: chatml
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- path: ./datasets/no-robots-sharegpt-fixed.jsonl
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type: sharegpt
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conversation: chatml
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- path: ./datasets/bluemoon.train.json
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type: sharegpt
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conversation: chatml
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- path: ./datasets/toxicsharegpt-NoWarning.jsonl
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type: sharegpt
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conversation: chatml
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- path: ./datasets/LimaRP-ShareGPT.json
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type: sharegpt
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conversation: chatml
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- path: ./datasets/CapybaraPure_Decontaminated-ShareGPT.json
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type: sharegpt
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conversation: chatml
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dataset_prepared_path:
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val_set_size: 0.05
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output_dir: ./out
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gradient_checkpointing_kwargs:
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use_reentrant: true
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wandb_project: MistralMaid-7B-0.2
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 1
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micro_batch_size: 3
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num_epochs: 2
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.000005
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train_on_inputs: true
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 10
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evals_per_epoch: 4
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eval_table_size:
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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special_tokens:
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bos_token: "<s>"
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eos_token: "</s>"
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unk_token: "<unk>"
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```
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# out
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This model is a fine-tuned version of [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.1414
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## Model description
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More information needed
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## Intended uses & limitations
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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: 5e-06
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- train_batch_size: 3
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- eval_batch_size: 3
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- total_train_batch_size: 6
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- total_eval_batch_size: 6
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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: 2
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.4494 | 0.0 | 1 | 1.4125 |
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| 1.2942 | 0.25 | 296 | 1.1561 |
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| 1.3496 | 0.5 | 592 | 1.1433 |
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| 1.0723 | 0.75 | 888 | 1.1374 |
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| 1.3354 | 1.0 | 1184 | 1.1313 |
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| 0.9644 | 1.24 | 1480 | 1.1415 |
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| 1.1276 | 1.49 | 1776 | 1.1412 |
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| 0.9386 | 1.74 | 2072 | 1.1414 |
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### Framework versions
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- Pytorch 2.0.1+cu118
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- Datasets 2.18.0
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- Tokenizers 0.15.0
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---
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license: cc-by-nc-4.0
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tags:
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- not-for-all-audiences
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- nsfw
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---
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<!-- description start -->
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## Description
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This repo contains fp16 files of LewdMistral-7B-0.2.
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It's a full finetune (on 2 epoch) of [Mistral-7B-v0.2](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) based on multiple RP datasets.
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It was made for being merged with old 0.1 model as an experiment to see if it would be possible to add new data from 0.2 into 0.1 finetunes, but since it's usable, I let is open for further train/merging.
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It was used to create [BigL](https://huggingface.co/Undi95/BigL-7B), a model who take Mistral 0.2 7B as a base, but merged with Mistral 0.1 finetunes.
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<!-- description end -->
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<!-- prompt-template start -->
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## Prompt template: Alpaca
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{system prompt}
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### Input:
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{prompt}
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### Response:
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{output}
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```
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If you want to support me, you can [here](https://ko-fi.com/undiai).
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