afrideva
/

smol_llama-220M-bees-internal-GGUF

+---
+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:
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# 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