RichardErkhov/NicholasCorrado_-_tinyllama-1.1b-chat-v1.0-ui-math-coding-dpo-gguf

Quantization made by Richard Erkhov.

Github

Discord

Request more models

tinyllama-1.1b-chat-v1.0-ui-math-coding-dpo - GGUF

• —Model creator: https://huggingface.co/NicholasCorrado/
• —Original model: https://huggingface.co/NicholasCorrado/tinyllama-1.1b-chat-v1.0-ui-math-coding-dpo/

Original model description: --- libraryname: transformers license: apache-2.0 basemodel: TinyLlama/TinyLlama-1.1B-Chat-v1.0 tags:

• —alignment-handbook
• —trl
• —dpo
• —generatedfromtrainer
• —trl
• —dpo
• —generatedfromtrainer datasets:
• —data/ui_math
• —data/ui_coding model-index:
• —name: tinyllama-1.1b-chat-v1.0-ui-math-coding-dpo results: [] ---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

tinyllama-1.1b-chat-v1.0-ui-math-coding-dpo

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the data/uimath and the data/uicoding datasets. It achieves the following results on the evaluation set:

• —Loss: 0.3968
• —Rewards/chosen: -1.5087
• —Rewards/rejected: -3.7229
• —Rewards/accuracies: 0.7812
• —Rewards/margins: 2.2142
• —Logps/rejected: -740.7106
• —Logps/chosen: -538.8605
• —Logits/rejected: -2.5209
• —Logits/chosen: -2.5392

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-07
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 8
• —gradientaccumulationsteps: 8
• —totaltrainbatch_size: 1024
• —totalevalbatch_size: 128
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 1

Training results

Framework versions

• —Transformers 4.44.1
• —Pytorch 2.1.2+cu121
• —Datasets 2.21.0
• —Tokenizers 0.19.1