---
license: apache-2.0
base_model: BEE-spoke-data/smol_llama-220M-GQA
tags:
- generated_from_trainer
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. -->

# smol_llama-220M-bees-internal

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.
It achieves the following results on the evaluation set:
- Loss: 2.6892
- Accuracy: 0.4610

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 27634
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.0959        | 0.1   | 50   | 2.9671          | 0.4245   |
| 2.9975        | 0.19  | 100  | 2.8691          | 0.4371   |
| 2.8938        | 0.29  | 150  | 2.8271          | 0.4419   |
| 2.9027        | 0.39  | 200  | 2.7973          | 0.4457   |
| 2.8983        | 0.49  | 250  | 2.7719          | 0.4489   |
| 2.8789        | 0.58  | 300  | 2.7519          | 0.4515   |
| 2.8672        | 0.68  | 350  | 2.7366          | 0.4535   |
| 2.8369        | 0.78  | 400  | 2.7230          | 0.4558   |
| 2.8271        | 0.88  | 450  | 2.7118          | 0.4569   |
| 2.7775        | 0.97  | 500  | 2.7034          | 0.4587   |
| 2.671         | 1.07  | 550  | 2.6996          | 0.4592   |
| 2.695         | 1.17  | 600  | 2.6965          | 0.4598   |
| 2.6962        | 1.27  | 650  | 2.6934          | 0.4601   |
| 2.6034        | 1.36  | 700  | 2.6916          | 0.4605   |
| 2.716         | 1.46  | 750  | 2.6901          | 0.4609   |
| 2.6968        | 1.56  | 800  | 2.6896          | 0.4608   |
| 2.6626        | 1.66  | 850  | 2.6893          | 0.4609   |
| 2.6881        | 1.75  | 900  | 2.6891          | 0.4610   |
| 2.7339        | 1.85  | 950  | 2.6891          | 0.4610   |
| 2.6729        | 1.95  | 1000 | 2.6892          | 0.4610   |


### Framework versions

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