---
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
tags:
- bees
- bzz
- honey
- oprah winfrey
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# TinyLlama-3T-1.1bee


![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/I6AfPId0Xo_vVobtkAP12.png)

A grand successor to [the original](https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee). This one has the following improvements:

- start from [finished 3T TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T)
- vastly improved and expanded SoTA beekeeping dataset


## Model description

This model is a fine-tuned version of TinyLlama-1.1b-3T on the BEE-spoke-data/bees-internal dataset.

It achieves the following results on the evaluation set:
- Loss: 2.1640
- Accuracy: 0.5406

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 13707
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4432        | 0.19  | 50   | 2.3850          | 0.5033   |
| 2.3655        | 0.39  | 100  | 2.3124          | 0.5129   |
| 2.374         | 0.58  | 150  | 2.2588          | 0.5215   |
| 2.3558        | 0.78  | 200  | 2.2132          | 0.5291   |
| 2.2677        | 0.97  | 250  | 2.1828          | 0.5348   |
| 2.0701        | 1.17  | 300  | 2.1788          | 0.5373   |
| 2.0766        | 1.36  | 350  | 2.1673          | 0.5398   |
| 2.0669        | 1.56  | 400  | 2.1651          | 0.5402   |
| 2.0314        | 1.75  | 450  | 2.1641          | 0.5406   |
| 2.0281        | 1.95  | 500  | 2.1639          | 0.5407   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0
- Datasets 2.16.1
- Tokenizers 0.15.0