Text Generation
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
language:
  - en
datasets:
  - BEE-spoke-data/UltraTextbooks-2.1-fw_mix
  - BEE-spoke-data/napierone-epub-raw
  - BEE-spoke-data/knowledge-inoc-concat-v1
inference:
  parameters:
    max_new_tokens: 64
    do_sample: true
    temperature: 0.7
    repetition_penalty: 1.1
    no_repeat_ngram_size: 6
    eta_cutoff: 0.0008
    renormalize_logits: true
widget:
  - text: My name is El Microondas the Wise, and
    example_title: El Microondas
  - text: Kennesaw State University is a public
    example_title: Kennesaw State University
  - text: >-
      Bungie Studios is an American video game developer. They are most famous
      for developing the award winning Halo series of video games. They also
      made Destiny. The studio was founded
    example_title: Bungie
  - text: The Mona Lisa is a world-renowned painting created by
    example_title: Mona Lisa
  - text: >-
      The Harry Potter series, written by J.K. Rowling, begins with the book
      titled
    example_title: Harry Potter Series
  - text: >-
      Question: I have cities, but no houses. I have mountains, but no trees. I
      have water, but no fish. What am I?

      Answer:
    example_title: Riddle
  - text: The process of photosynthesis involves the conversion of
    example_title: Photosynthesis
  - text: >-
      Jane went to the store to buy some groceries. She picked up apples,
      oranges, and a loaf of bread. When she got home, she realized she forgot
    example_title: Story Continuation
  - text: >-
      Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph,
      and another train leaves Station B at 10:00 AM and travels at 80 mph, when
      will they meet if the distance between the stations is 300 miles?

      To determine
    example_title: Math Problem
  - text: In the context of computer programming, an algorithm is
    example_title: Algorithm Definition
pipeline_tag: text-generation

mega-ar-350m-v0.13

Model description

Continued-training of BEE-spoke-data/mega-ar-350m-L3t-v0.08-ultraTBfw on a few more datasets.

It achieves the following results on the evaluation set (BEE-spoke-data/UltraTextbooks-2.1-fw_mix):

  • Loss: 1.9926
  • Accuracy: 0.5885
  • Num Input Tokens Seen: 3468165120

Quick eval

Quick eval for: pszemraj/mega-ar-350m-v0.13

hf (pretrained=pszemraj/mega-ar-350m-v0.13,trust_remote_code=True,dtype=float), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8

Tasks Version Filter n-shot Metric Value Stderr
arc_easy 1 none 0 acc 0.4491 ± 0.0102
none 0 acc_norm 0.4061 ± 0.0101
boolq 2 none 0 acc 0.5367 ± 0.0087
lambada_openai 1 none 0 perplexity 55.3308 ± 2.3100
none 0 acc 0.3113 ± 0.0065
openbookqa 1 none 0 acc 0.1760 ± 0.0170
none 0 acc_norm 0.2680 ± 0.0198
piqa 1 none 0 acc 0.6366 ± 0.0112
none 0 acc_norm 0.6213 ± 0.0113
winogrande 1 none 0 acc 0.5036 ± 0.0141

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 80085
  • distributed_type: multi-GPU
  • num_devices: 3
  • gradient_accumulation_steps: 32
  • total_train_batch_size: 96
  • total_eval_batch_size: 3
  • optimizer: Adam with betas=(0.9,0.985) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 1.0