--- 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](https://huggingface.co/BEE-spoke-data/TinyLlama-3T-1.1bee) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data) | Name | Quant method | Size | | ---- | ---- | ---- | | [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 | | [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 | | [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 | | [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 | | [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 | | [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 | | [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 | ## Original Model Card: # TinyLlama-3T-1.1bee ![image/png](https://cdn-uploads.huggingface.co/production/uploads/60bccec062080d33f875cd0c/I6AfPId0Xo_vVobtkAP12.png) A grand successor to [the original](https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee). This one has the following improvements: - start from [finished 3T TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) - 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