132 lines
5.0 KiB
Markdown
132 lines
5.0 KiB
Markdown
---
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base_model: BEE-spoke-data/TinyLlama-3T-1.1bee
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datasets:
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- BEE-spoke-data/bees-internal
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inference: false
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language:
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- en
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license: apache-2.0
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metrics:
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- accuracy
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model_creator: BEE-spoke-data
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model_name: TinyLlama-3T-1.1bee
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pipeline_tag: text-generation
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quantized_by: afrideva
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tags:
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- bees
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- bzz
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- honey
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- oprah winfrey
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- gguf
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- ggml
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- quantized
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- q2_k
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- q3_k_m
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- q4_k_m
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- q5_k_m
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- q6_k
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- q8_0
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widget:
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- example_title: Queen Excluder
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text: In beekeeping, the term "queen excluder" refers to
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- example_title: Increasing Honey Production
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text: One way to encourage a honey bee colony to produce more honey is by
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- example_title: Lifecycle of a Worker Bee
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text: The lifecycle of a worker bee consists of several stages, starting with
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- example_title: Varroa Destructor
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text: Varroa destructor is a type of mite that
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- example_title: Beekeeping PPE
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text: In the world of beekeeping, the acronym PPE stands for
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- example_title: Robbing in Beekeeping
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text: The term "robbing" in beekeeping refers to the act of
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- example_title: Role of Drone Bees
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text: 'Question: What''s the primary function of drone bees in a hive?
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Answer:'
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- example_title: Honey Harvesting Device
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text: To harvest honey from a hive, beekeepers often use a device known as a
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- example_title: Beekeeping Math Problem
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text: 'Problem: You have a hive that produces 60 pounds of honey per year. You decide
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to split the hive into two. Assuming each hive now produces at a 70% rate compared
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to before, how much honey will you get from both hives next year?
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To calculate'
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- example_title: Swarming
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text: In beekeeping, "swarming" is the process where
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---
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# BEE-spoke-data/TinyLlama-3T-1.1bee-GGUF
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Quantized GGUF model files for [TinyLlama-3T-1.1bee](https://huggingface.co/BEE-spoke-data/TinyLlama-3T-1.1bee) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data)
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| Name | Quant method | Size |
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| ---- | ---- | ---- |
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| [tinyllama-3t-1.1bee.fp16.gguf](https://huggingface.co/afrideva/TinyLlama-3T-1.1bee-GGUF/resolve/main/tinyllama-3t-1.1bee.fp16.gguf) | fp16 | 2.20 GB |
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| [tinyllama-3t-1.1bee.q2_k.gguf](https://huggingface.co/afrideva/TinyLlama-3T-1.1bee-GGUF/resolve/main/tinyllama-3t-1.1bee.q2_k.gguf) | q2_k | 432.13 MB |
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| [tinyllama-3t-1.1bee.q3_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-3T-1.1bee-GGUF/resolve/main/tinyllama-3t-1.1bee.q3_k_m.gguf) | q3_k_m | 548.40 MB |
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| [tinyllama-3t-1.1bee.q4_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-3T-1.1bee-GGUF/resolve/main/tinyllama-3t-1.1bee.q4_k_m.gguf) | q4_k_m | 667.81 MB |
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| [tinyllama-3t-1.1bee.q5_k_m.gguf](https://huggingface.co/afrideva/TinyLlama-3T-1.1bee-GGUF/resolve/main/tinyllama-3t-1.1bee.q5_k_m.gguf) | q5_k_m | 782.04 MB |
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| [tinyllama-3t-1.1bee.q6_k.gguf](https://huggingface.co/afrideva/TinyLlama-3T-1.1bee-GGUF/resolve/main/tinyllama-3t-1.1bee.q6_k.gguf) | q6_k | 903.41 MB |
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| [tinyllama-3t-1.1bee.q8_0.gguf](https://huggingface.co/afrideva/TinyLlama-3T-1.1bee-GGUF/resolve/main/tinyllama-3t-1.1bee.q8_0.gguf) | q8_0 | 1.17 GB |
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## Original Model Card:
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# TinyLlama-3T-1.1bee
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A grand successor to [the original](https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee). This one has the following improvements:
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- start from [finished 3T TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T)
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- vastly improved and expanded SoTA beekeeping dataset
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## Model description
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This model is a fine-tuned version of TinyLlama-1.1b-3T on the BEE-spoke-data/bees-internal dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.1640
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- Accuracy: 0.5406
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 4
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- eval_batch_size: 2
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- seed: 13707
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.05
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- num_epochs: 2.0
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 2.4432 | 0.19 | 50 | 2.3850 | 0.5033 |
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| 2.3655 | 0.39 | 100 | 2.3124 | 0.5129 |
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| 2.374 | 0.58 | 150 | 2.2588 | 0.5215 |
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| 2.3558 | 0.78 | 200 | 2.2132 | 0.5291 |
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| 2.2677 | 0.97 | 250 | 2.1828 | 0.5348 |
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| 2.0701 | 1.17 | 300 | 2.1788 | 0.5373 |
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| 2.0766 | 1.36 | 350 | 2.1673 | 0.5398 |
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| 2.0669 | 1.56 | 400 | 2.1651 | 0.5402 |
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| 2.0314 | 1.75 | 450 | 2.1641 | 0.5406 |
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| 2.0281 | 1.95 | 500 | 2.1639 | 0.5407 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.0
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- Datasets 2.16.1
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- Tokenizers 0.15.0 |