---
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 🐝


![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/vgDfbjic0S3OJwv9BNzQN.png)

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         |