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--- |
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license: apache-2.0 |
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base_model: BEE-spoke-data/smol_llama-220M-GQA |
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tags: |
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- generated_from_trainer |
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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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# smol_llama-220M-bees-internal |
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This model is a fine-tuned version of [BEE-spoke-data/smol_llama-220M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-220M-GQA) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6892 |
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- Accuracy: 0.4610 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 27634 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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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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| 3.0959 | 0.1 | 50 | 2.9671 | 0.4245 | |
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| 2.9975 | 0.19 | 100 | 2.8691 | 0.4371 | |
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| 2.8938 | 0.29 | 150 | 2.8271 | 0.4419 | |
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| 2.9027 | 0.39 | 200 | 2.7973 | 0.4457 | |
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| 2.8983 | 0.49 | 250 | 2.7719 | 0.4489 | |
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| 2.8789 | 0.58 | 300 | 2.7519 | 0.4515 | |
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| 2.8672 | 0.68 | 350 | 2.7366 | 0.4535 | |
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| 2.8369 | 0.78 | 400 | 2.7230 | 0.4558 | |
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| 2.8271 | 0.88 | 450 | 2.7118 | 0.4569 | |
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| 2.7775 | 0.97 | 500 | 2.7034 | 0.4587 | |
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| 2.671 | 1.07 | 550 | 2.6996 | 0.4592 | |
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| 2.695 | 1.17 | 600 | 2.6965 | 0.4598 | |
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| 2.6962 | 1.27 | 650 | 2.6934 | 0.4601 | |
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| 2.6034 | 1.36 | 700 | 2.6916 | 0.4605 | |
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| 2.716 | 1.46 | 750 | 2.6901 | 0.4609 | |
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| 2.6968 | 1.56 | 800 | 2.6896 | 0.4608 | |
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| 2.6626 | 1.66 | 850 | 2.6893 | 0.4609 | |
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| 2.6881 | 1.75 | 900 | 2.6891 | 0.4610 | |
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| 2.7339 | 1.85 | 950 | 2.6891 | 0.4610 | |
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| 2.6729 | 1.95 | 1000 | 2.6892 | 0.4610 | |
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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 |
