metadata
language:
- en
license: apache-2.0
tags:
- bees
- bzz
- honey
- oprah winfrey
datasets:
- BEE-spoke-data/bees-internal
metrics:
- accuracy
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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
model-index:
- name: TinyLlama-3T-1.1bee
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 33.79
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 60.29
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.86
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 38.13
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 60.22
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.45
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/TinyLlama-3T-1.1bee
name: Open LLM Leaderboard
TinyLlama-3T-1.1bee
A grand successor to the original. This one has the following improvements:
- start from finished 3T TinyLlama
- 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
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 36.46 |
| AI2 Reasoning Challenge (25-Shot) | 33.79 |
| HellaSwag (10-Shot) | 60.29 |
| MMLU (5-Shot) | 25.86 |
| TruthfulQA (0-shot) | 38.13 |
| Winogrande (5-shot) | 60.22 |
| GSM8k (5-shot) | 0.45 |