| --- |
| license: apache-2.0 |
| base_model: PY007/TinyLlama-1.1B-intermediate-step-240k-503b |
| tags: |
| - bees |
| - beekeeping |
| - honey |
| 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 |
| datasets: |
| - BEE-spoke-data/bees-internal |
| language: |
| - en |
| --- |
| |
|
|
| # TinyLlama-1.1bee 🐝 |
|
|
|
|
|  |
|
|
| 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. |
|
|
|
|
| ## Details |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.4285 |
| - Accuracy: 0.4969 |
|
|
|
|
| ``` |
| ***** eval metrics ***** |
| eval_accuracy = 0.4972 |
| eval_loss = 2.4283 |
| eval_runtime = 0:00:53.12 |
| eval_samples = 239 |
| eval_samples_per_second = 4.499 |
| eval_steps_per_second = 1.129 |
| perplexity = 11.3391 |
| ``` |
|
|
| ## 📜 Intended Uses & Limitations 📜 |
|
|
| ### Intended Uses: |
|
|
| 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. |
| 2. **General Queries**: Have questions about hive management, bee species, or honey extraction? Feel free to consult the model for general insights. |
| 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. |
|
|
| ### Limitations: |
|
|
| 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. |
| 2. **Licensing**: Apache-2.0, following TinyLlama |
| 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. |
| 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. |
|
|
|
|
| ## Training and evaluation data |
|
|
| 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) |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 0.0001 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 80085 |
| - gradient_accumulation_steps: 8 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 2.0 |
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__TinyLlama-1.1bee) |
|
|
| | Metric | Value | |
| |-----------------------|---------------------------| |
| | Avg. | 29.15 | |
| | ARC (25-shot) | 30.55 | |
| | HellaSwag (10-shot) | 51.8 | |
| | MMLU (5-shot) | 24.25 | |
| | TruthfulQA (0-shot) | 39.01 | |
| | Winogrande (5-shot) | 54.46 | |
| | GSM8K (5-shot) | 0.23 | |
| | DROP (3-shot) | 3.74 | |
|
|
