Mistral-11B-OmniMix-pippa-sharegpt-11b-qlora

This is a repository of my Mistral-11B-OmniMix Qlora checkpoints of the PIPPA-ShareGPT dataset.

You can read more about the dataset on its relevant page. It's a ShareGPT reformat of the PIPPA dataset by PygmalionAI. The reformat was done to allow for axolotl compatability.

Architecture

• Model Architecture: Mistral-11B-OmniMix
• Training Algorithm: QLora
• Dataset Used: PIPPA-ShareGPT (pippa_sharegpt_trimmed.jsonl)

Training Details

• Dataset: PIPPA-ShareGPT
• Datset type: ShareGPT
• Training Parameters: See Here
• Training Environment: Axolotl
• sequence_len: 8196

Instruct Format

ShareGPT gets converted to vicuna format. The dataset uses modified roles of USER and CHARACTER instead of USER and ASSISTANT.

SYSTEM: Enter roleplay mode...
USER: {prompt}
CHARACTER:

Notes

This Qlora was produced as an experiment to see how the public version of PIPPA can affect a model. Also, Mistral is fairly new and training/finetune can be broken. As a result, I have no idea if this lora is of great quality or absolute garbage and was mean to be only used with OmniMix.

Acknowledgments

Thanks to:

• PygmalionAI: The creators of the PIPPA dataset
• Axolotl: Finetuning suite
• Kingbri: The OG author of this LoRA who helped me a lot

Donate?

If you'd like to donate to Kingbri, you can do so here: https://ko-fi.com/kingbri

If you'd like to donate to me, you can also do it here: https://ko-fi.com/undiai

You should not feel obligated to donate, but if you do, we'll appreciate it.

Axolotl stuff

Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: bfloat16

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0002
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 3

Training results

Framework versions

• Transformers 4.35.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1