--- 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 ![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 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__TinyLlama-3T-1.1bee) | 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|