RichardErkhov/postbot_-_pythia-160m-hq-emails-gguf

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pythia-160m-hq-emails - GGUF

• —Model creator: https://huggingface.co/postbot/
• —Original model: https://huggingface.co/postbot/pythia-160m-hq-emails/

Original model description: --- language:

• —en license: apache-2.0 tags:
• —generatedfromtrainer datasets:
• —postbot/multi-emails-hq metrics:
• —accuracy widget:
• —text: 'Good Morning Professor Beans,

Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam' example_title: email to prof

• —text: 'Hey <NAME>,

Thank you for signing up for my weekly newsletter. Before we get started, you''ll have to confirm your email address.' example_title: newsletter

• —text: 'Hi <NAME>,

I hope this email finds you well. I wanted to reach out and ask about office hours' example_title: office hours

• —text: 'Greetings <NAME>,

I hope you had a splendid evening at the Company sausage eating festival. I am reaching out because' example_title: festival

• —text: 'Good Morning Harold,

I was wondering when the next' example_title: event

• —text: URGENT - I need the TPS reports example_title: URGENT
• —text: 'Hi Archibald,

I hope this email finds you extremely well.' example_title: emails that find you

• —text: 'Hello there.

I just wanted to reach out and check in to' example_title: checking in

• —text: 'Hello <NAME>,

I hope this email finds you well. I wanted to reach out and see if you''ve enjoyed your time with us' example_title: work well

• —text: 'Hi <NAME>,

I hope this email finds you well. I wanted to reach out and see if we could catch up' example_title: catch up

• —text: I'm <NAME> and I just moved into the area and wanted to reach out and get some details on where I could get groceries and exampletitle: grocery inference: parameters: minlength: 16 maxlength: 64 norepeatngramsize: 4 dosample: true topk: 40 topp: 0.95 repetitionpenalty: 3.5 pipelinetag: text-generation basemodel: EleutherAI/pythia-160m-deduped model-index:
• —name: pythia-160m-hq-emails-v4 results:
• —task: type: text-generation name: Causal Language Modeling dataset: name: postbot/multi-emails-hq type: postbot/multi-emails-hq metrics:
• —type: accuracy value: 0.611281497151223 name: Accuracy ---

pythia-160m-hq-emails-v4

This model is a fine-tuned version of EleutherAI/pythia-160m-deduped on the postbot/multi-emails-hq dataset. It achieves the following results on the evaluation set:

• —Loss: 2.2856
• —Accuracy: 0.6113
• —perplexity: 9.8313

Model description

this is v4

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: 0.0006
• —trainbatchsize: 4
• —evalbatchsize: 1
• —seed: 42
• —distributed_type: multi-GPU
• —gradientaccumulationsteps: 32
• —totaltrainbatch_size: 128
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_ratio: 0.05
• —num_epochs: 4.0
• —mixedprecisiontraining: Native AMP

Training results

Framework versions

• —Transformers 4.27.0.dev0
• —Pytorch 1.13.1+cu117
• —Datasets 2.8.0
• —Tokenizers 0.13.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here