Quantization made by Richard Erkhov.
TinyLlama-1.1bee - GGUF
- Model creator: https://huggingface.co/BEE-spoke-data/
- Original model: https://huggingface.co/BEE-spoke-data/TinyLlama-1.1bee/
Name | Quant method | Size |
---|---|---|
TinyLlama-1.1bee.Q2_K.gguf | Q2_K | 0.4GB |
TinyLlama-1.1bee.IQ3_XS.gguf | IQ3_XS | 0.44GB |
TinyLlama-1.1bee.IQ3_S.gguf | IQ3_S | 0.47GB |
TinyLlama-1.1bee.Q3_K_S.gguf | Q3_K_S | 0.47GB |
TinyLlama-1.1bee.IQ3_M.gguf | IQ3_M | 0.48GB |
TinyLlama-1.1bee.Q3_K.gguf | Q3_K | 0.51GB |
TinyLlama-1.1bee.Q3_K_M.gguf | Q3_K_M | 0.51GB |
TinyLlama-1.1bee.Q3_K_L.gguf | Q3_K_L | 0.55GB |
TinyLlama-1.1bee.IQ4_XS.gguf | IQ4_XS | 0.57GB |
TinyLlama-1.1bee.Q4_0.gguf | Q4_0 | 0.59GB |
TinyLlama-1.1bee.IQ4_NL.gguf | IQ4_NL | 0.6GB |
TinyLlama-1.1bee.Q4_K_S.gguf | Q4_K_S | 0.6GB |
TinyLlama-1.1bee.Q4_K.gguf | Q4_K | 0.62GB |
TinyLlama-1.1bee.Q4_K_M.gguf | Q4_K_M | 0.62GB |
TinyLlama-1.1bee.Q4_1.gguf | Q4_1 | 0.65GB |
TinyLlama-1.1bee.Q5_0.gguf | Q5_0 | 0.71GB |
TinyLlama-1.1bee.Q5_K_S.gguf | Q5_K_S | 0.71GB |
TinyLlama-1.1bee.Q5_K.gguf | Q5_K | 0.73GB |
TinyLlama-1.1bee.Q5_K_M.gguf | Q5_K_M | 0.73GB |
TinyLlama-1.1bee.Q5_1.gguf | Q5_1 | 0.77GB |
TinyLlama-1.1bee.Q6_K.gguf | Q6_K | 0.84GB |
TinyLlama-1.1bee.Q8_0.gguf | Q8_0 | 1.09GB |
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 π
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 honeyβbeekeeping, 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
π Intended Uses & Limitations π
Intended Uses:
- 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.
- General Queries: Have questions about hive management, bee species, or honey extraction? Feel free to consult the model for general insights.
- 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:
- 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.
- Licensing: Apache-2.0, following TinyLlama
- 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.
- 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
Metric | Value |
---|---|
Avg. | 29.15 |
ARC (25-shot) | 30.55 |
HellaSwag (10-shot) | 51.8 |
MMLU (5-shot) | 24.25 |
TruthfulQA (0-shot) | 39.01 |
Winogrande (5-shot) | 54.46 |
GSM8K (5-shot) | 0.23 |
DROP (3-shot) | 3.74 |
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