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--- |
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license: apache-2.0 |
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base_model: PY007/TinyLlama-1.1B-intermediate-step-240k-503b |
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tags: |
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- bees |
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- beekeeping |
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- honey |
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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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# TinyLlama-1.1bee 🐝 |
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As we feverishly hit the refresh button on hf.co's homepage, on the hunt for the newest waifu chatbot to grace the AI stage, an epiphany struck us like a bee sting. What could we offer to the hive-mind of the community? The answer was as clear as honey—beekeeping, naturally. And thus, this un-bee-lievable model was born. |
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## Details |
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This model is a fine-tuned version of [PY007/TinyLlama-1.1B-intermediate-step-240k-503b](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-240k-503b) 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: 2.4285 |
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- Accuracy: 0.4969 |
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``` |
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***** eval metrics ***** |
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eval_accuracy = 0.4972 |
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eval_loss = 2.4283 |
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eval_runtime = 0:00:53.12 |
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eval_samples = 239 |
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eval_samples_per_second = 4.499 |
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eval_steps_per_second = 1.129 |
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perplexity = 11.3391 |
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``` |
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## 📜 Intended Uses & Limitations 📜 |
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### Intended Uses: |
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1. **Educational Engagement**: Whether you're a novice beekeeper, an enthusiast, or someone just looking to understand the buzz around bees, this model aims to serve as an informative and entertaining resource. |
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2. **General Queries**: Have questions about hive management, bee species, or honey extraction? Feel free to consult the model for general insights. |
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3. **Academic & Research Inspiration**: If you're diving into the world of apiculture studies or environmental science, our model could offer some preliminary insights and ideas. |
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### Limitations: |
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1. **Not a Beekeeping Expert**: As much as we admire bees and their hard work, this model is not a certified apiculturist. Please consult professional beekeeping resources or experts for serious decisions related to hive management, bee health, and honey production. |
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2. **Licensing**: Apache-2.0, following TinyLlama |
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3. **Infallibility**: Our model can err, just like any other piece of technology (or bee). Always double-check the information before applying it to your own hive or research. |
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4. **Ethical Constraints**: This model may not be used for any illegal or unethical activities, including but not limited to: bioterrorism & standard terrorism, harassment, or spreading disinformation. |
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## Training and evaluation data |
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While the full dataset is not yet complete and therefore not yet released for "safety reasons", you can check out a preliminary sample at: [bees-v0](https://huggingface.co/datasets/BEE-spoke-data/bees-v0) |
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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: 4 |
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- seed: 80085 |
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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=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 2.0 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__TinyLlama-1.1bee) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 29.15 | |
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| ARC (25-shot) | 30.55 | |
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| HellaSwag (10-shot) | 51.8 | |
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| MMLU (5-shot) | 24.25 | |
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| TruthfulQA (0-shot) | 39.01 | |
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| Winogrande (5-shot) | 54.46 | |
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| GSM8K (5-shot) | 0.23 | |
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| DROP (3-shot) | 3.74 | |
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