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---
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 |