--- base_model: BEE-spoke-data/smol_llama-220M-bees-internal datasets: - BEE-spoke-data/bees-internal inference: false language: - en license: apache-2.0 metrics: - accuracy model_creator: BEE-spoke-data model_name: smol_llama-220M-bees-internal pipeline_tag: text-generation quantized_by: afrideva tags: - generated_from_trainer - 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/smol_llama-220M-bees-internal-GGUF Quantized GGUF model files for [smol_llama-220M-bees-internal](https://huggingface.co/BEE-spoke-data/smol_llama-220M-bees-internal) from [BEE-spoke-data](https://huggingface.co/BEE-spoke-data) | Name | Quant method | Size | | ---- | ---- | ---- | | [smol_llama-220m-bees-internal.fp16.gguf](https://huggingface.co/afrideva/smol_llama-220M-bees-internal-GGUF/resolve/main/smol_llama-220m-bees-internal.fp16.gguf) | fp16 | 436.50 MB | | [smol_llama-220m-bees-internal.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-bees-internal-GGUF/resolve/main/smol_llama-220m-bees-internal.q2_k.gguf) | q2_k | 94.43 MB | | [smol_llama-220m-bees-internal.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-bees-internal-GGUF/resolve/main/smol_llama-220m-bees-internal.q3_k_m.gguf) | q3_k_m | 114.65 MB | | [smol_llama-220m-bees-internal.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-bees-internal-GGUF/resolve/main/smol_llama-220m-bees-internal.q4_k_m.gguf) | q4_k_m | 137.58 MB | | [smol_llama-220m-bees-internal.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-220M-bees-internal-GGUF/resolve/main/smol_llama-220m-bees-internal.q5_k_m.gguf) | q5_k_m | 157.91 MB | | [smol_llama-220m-bees-internal.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-220M-bees-internal-GGUF/resolve/main/smol_llama-220m-bees-internal.q6_k.gguf) | q6_k | 179.52 MB | | [smol_llama-220m-bees-internal.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-220M-bees-internal-GGUF/resolve/main/smol_llama-220m-bees-internal.q8_0.gguf) | q8_0 | 232.28 MB | ## Original Model Card: # 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