BEE-spoke-data_-_TinyLlama-1.1bee-awq

Quantization made by Richard Erkhov.

Github

Discord

Request more models

TinyLlama-1.1bee - AWQ

• Model creator: https://huggingface.co/BEE-spoke-data/
• Original model: https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee/

Original model description:

license: apache-2.0 base_model: PY007/TinyLlama-1.1B-intermediate-step-240k-503b tags:

• bees

• beekeeping

• honey metrics:

• accuracy 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 datasets:

• BEE-spoke-data/bees-internal language:

• en

---

TinyLlama-1.1bee 🐝

As we feverishly hit the refresh button on hf.co's homepage, on the hunt for the newest waifu chatbot to grace the AI stage, an epiphany struck us like a bee sting. What could we offer to the hive-mind of the community? The answer was as clear as honey—beekeeping, naturally. And thus, this un-bee-lievable model was born.

Details

This model is a fine-tuned version of PY007/TinyLlama-1.1B-intermediate-step-240k-503b on the BEE-spoke-data/bees-internal dataset. It achieves the following results on the evaluation set:

• Loss: 2.4285
• Accuracy: 0.4969

***** eval metrics *****
  eval_accuracy           =     0.4972                                              
  eval_loss               =     2.4283
  eval_runtime            = 0:00:53.12
  eval_samples            =        239
  eval_samples_per_second =      4.499
  eval_steps_per_second   =      1.129
  perplexity              =    11.3391

📜 Intended Uses & Limitations 📜

Intended Uses:

1. Educational Engagement: Whether you're a novice beekeeper, an enthusiast, or someone just looking to understand the buzz around bees, this model aims to serve as an informative and entertaining resource.
2. General Queries: Have questions about hive management, bee species, or honey extraction? Feel free to consult the model for general insights.
3. Academic & Research Inspiration: If you're diving into the world of apiculture studies or environmental science, our model could offer some preliminary insights and ideas.

Limitations:

1. Not a Beekeeping Expert: As much as we admire bees and their hard work, this model is not a certified apiculturist. Please consult professional beekeeping resources or experts for serious decisions related to hive management, bee health, and honey production.
2. Licensing: Apache-2.0, following TinyLlama
3. Infallibility: Our model can err, just like any other piece of technology (or bee). Always double-check the information before applying it to your own hive or research.
4. Ethical Constraints: This model may not be used for any illegal or unethical activities, including but not limited to: bioterrorism & standard terrorism, harassment, or spreading disinformation.

Training and evaluation data

While the full dataset is not yet complete and therefore not yet released for "safety reasons", you can check out a preliminary sample at: bees-v0

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 4
• eval_batch_size: 4
• seed: 80085
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• num_epochs: 2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here