metadata
license: apache-2.0
base_model: pszemraj/tinyllama-1.1b-3T
tags:
- generated_from_trainer
metrics:
- accuracy
inference:
parameters:
max_new_tokens: 64
do_sample: true
renormalize_logits: true
repetition_penalty: 1.05
no_repeat_ngram_size: 6
temperature: 0.9
top_p: 0.95
epsilon_cutoff: 0.0008
widget:
- text: In beekeeping, the term "queen excluder" refers to
example_title: Queen Excluder
- text: One way to encourage a honey bee colony to produce more honey is by
example_title: Increasing Honey Production
- text: The lifecycle of a worker bee consists of several stages, starting with
example_title: Lifecycle of a Worker Bee
- text: Varroa destructor is a type of mite that
example_title: Varroa Destructor
- text: In the world of beekeeping, the acronym PPE stands for
example_title: Beekeeping PPE
- text: The term "robbing" in beekeeping refers to the act of
example_title: Robbing in Beekeeping
- text: |-
Question: What's the primary function of drone bees in a hive?
Answer:
example_title: Role of Drone Bees
- text: To harvest honey from a hive, beekeepers often use a device known as a
example_title: Honey Harvesting Device
- text: >-
Problem: You have a hive that produces 60 pounds of honey per year. You
decide to split the hive into two. Assuming each hive now produces at a
70% rate compared to before, how much honey will you get from both hives
next year?
To calculate
example_title: Beekeeping Math Problem
- text: In beekeeping, "swarming" is the process where
example_title: Swarming
pipeline_tag: text-generation
TinyLlama-3T-1.1bee
This model is a fine-tuned version of pszemraj/tinyllama-1.1b-3T on the BEE-spoke-data/bees-internal dataset. It achieves the following results on the evaluation set:
- Loss: 2.1640
- Accuracy: 0.5406
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 13707
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
2.4432 | 0.19 | 50 | 2.3850 | 0.5033 |
2.3655 | 0.39 | 100 | 2.3124 | 0.5129 |
2.374 | 0.58 | 150 | 2.2588 | 0.5215 |
2.3558 | 0.78 | 200 | 2.2132 | 0.5291 |
2.2677 | 0.97 | 250 | 2.1828 | 0.5348 |
2.0701 | 1.17 | 300 | 2.1788 | 0.5373 |
2.0766 | 1.36 | 350 | 2.1673 | 0.5398 |
2.0669 | 1.56 | 400 | 2.1651 | 0.5402 |
2.0314 | 1.75 | 450 | 2.1641 | 0.5406 |
2.0281 | 1.95 | 500 | 2.1639 | 0.5407 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0
- Datasets 2.16.1
- Tokenizers 0.15.0