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--- |
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license: apache-2.0 |
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base_model: pszemraj/tinyllama-1.1b-3T |
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tags: |
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- bees |
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- bzz |
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- honey |
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- oprah winfrey |
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metrics: |
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- accuracy |
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inference: |
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parameters: |
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max_new_tokens: 64 |
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do_sample: true |
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renormalize_logits: true |
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repetition_penalty: 1.05 |
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no_repeat_ngram_size: 6 |
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temperature: 0.9 |
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top_p: 0.95 |
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epsilon_cutoff: 0.0008 |
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widget: |
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- text: In beekeeping, the term "queen excluder" refers to |
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example_title: Queen Excluder |
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- text: One way to encourage a honey bee colony to produce more honey is by |
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example_title: Increasing Honey Production |
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- text: The lifecycle of a worker bee consists of several stages, starting with |
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example_title: Lifecycle of a Worker Bee |
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- text: Varroa destructor is a type of mite that |
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example_title: Varroa Destructor |
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- text: In the world of beekeeping, the acronym PPE stands for |
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example_title: Beekeeping PPE |
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- text: The term "robbing" in beekeeping refers to the act of |
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example_title: Robbing in Beekeeping |
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- text: |- |
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Question: What's the primary function of drone bees in a hive? |
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Answer: |
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example_title: Role of Drone Bees |
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- text: To harvest honey from a hive, beekeepers often use a device known as a |
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example_title: Honey Harvesting Device |
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- text: >- |
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Problem: You have a hive that produces 60 pounds of honey per year. You |
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decide to split the hive into two. Assuming each hive now produces at a 70% |
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rate compared to before, how much honey will you get from both hives next |
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year? |
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To calculate |
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example_title: Beekeeping Math Problem |
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- text: In beekeeping, "swarming" is the process where |
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example_title: Swarming |
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pipeline_tag: text-generation |
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datasets: |
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- BEE-spoke-data/bees-internal |
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language: |
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- en |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# TinyLlama-3T-1.1bee |
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A grand successor to [the original](https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee). This one has the following improvements: |
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- start from [finished 3T TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) |
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- vastly improved and expanded SoTA beekeeping dataset |
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## Model description |
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This model is a fine-tuned version of TinyLlama-1.1b-3T on the BEE-spoke-data/bees-internal dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1640 |
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- Accuracy: 0.5406 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 2 |
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- seed: 13707 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 2.4432 | 0.19 | 50 | 2.3850 | 0.5033 | |
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| 2.3655 | 0.39 | 100 | 2.3124 | 0.5129 | |
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| 2.374 | 0.58 | 150 | 2.2588 | 0.5215 | |
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| 2.3558 | 0.78 | 200 | 2.2132 | 0.5291 | |
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| 2.2677 | 0.97 | 250 | 2.1828 | 0.5348 | |
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| 2.0701 | 1.17 | 300 | 2.1788 | 0.5373 | |
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| 2.0766 | 1.36 | 350 | 2.1673 | 0.5398 | |
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| 2.0669 | 1.56 | 400 | 2.1651 | 0.5402 | |
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| 2.0314 | 1.75 | 450 | 2.1641 | 0.5406 | |
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| 2.0281 | 1.95 | 500 | 2.1639 | 0.5407 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.0 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
