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TinyLlama-3T-1.1bee-GGUF

Text Generation
GGUF
English
bees
bzz
honey
oprah winfrey
ggml
quantized
q2_k
q3_k_m
q4_k_m
q5_k_m
q6_k
q8_0
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metadata
base_model: BEE-spoke-data/TinyLlama-3T-1.1bee
datasets:
  - BEE-spoke-data/bees-internal
inference: false
language:
  - en
license: apache-2.0
metrics:
  - accuracy
model_creator: BEE-spoke-data
model_name: TinyLlama-3T-1.1bee
pipeline_tag: text-generation
quantized_by: afrideva
tags:
  - bees
  - bzz
  - honey
  - oprah winfrey
  - gguf
  - ggml
  - quantized
  - q2_k
  - q3_k_m
  - q4_k_m
  - q5_k_m
  - q6_k
  - q8_0
widget:
  - example_title: Queen Excluder
    text: In beekeeping, the term "queen excluder" refers to
  - example_title: Increasing Honey Production
    text: One way to encourage a honey bee colony to produce more honey is by
  - example_title: Lifecycle of a Worker Bee
    text: The lifecycle of a worker bee consists of several stages, starting with
  - example_title: Varroa Destructor
    text: Varroa destructor is a type of mite that
  - example_title: Beekeeping PPE
    text: In the world of beekeeping, the acronym PPE stands for
  - example_title: Robbing in Beekeeping
    text: The term "robbing" in beekeeping refers to the act of
  - example_title: Role of Drone Bees
    text: |-
      Question: What's the primary function of drone bees in a hive?
      Answer:
  - example_title: Honey Harvesting Device
    text: To harvest honey from a hive, beekeepers often use a device known as a
  - example_title: Beekeeping Math Problem
    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: Swarming
    text: In beekeeping, "swarming" is the process where

BEE-spoke-data/TinyLlama-3T-1.1bee-GGUF

Quantized GGUF model files for TinyLlama-3T-1.1bee from BEE-spoke-data

Name Quant method Size
tinyllama-3t-1.1bee.fp16.gguf fp16 2.20 GB
tinyllama-3t-1.1bee.q2_k.gguf q2_k 432.13 MB
tinyllama-3t-1.1bee.q3_k_m.gguf q3_k_m 548.40 MB
tinyllama-3t-1.1bee.q4_k_m.gguf q4_k_m 667.81 MB
tinyllama-3t-1.1bee.q5_k_m.gguf q5_k_m 782.04 MB
tinyllama-3t-1.1bee.q6_k.gguf q6_k 903.41 MB
tinyllama-3t-1.1bee.q8_0.gguf q8_0 1.17 GB

Original Model Card:

TinyLlama-3T-1.1bee

image/png

A grand successor to the original. This one has the following improvements:

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