smol_llama-220M-bees-internal-GGUF/README.md

base_model, datasets, inference, language, license, metrics, model_creator, model_name, pipeline_tag, quantized_by, tags, widget

base_model	datasets	inference	language	license	metrics	model_creator	model_name	pipeline_tag	quantized_by	tags	widget
BEE-spoke-data/smol_llama-220M-bees-internal	BEE-spoke-data/bees-internal	false	en	apache-2.0	accuracy	BEE-spoke-data	smol_llama-220M-bees-internal	text-generation	afrideva	generated_from_trainer gguf ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0	example_title text Queen Excluder In beekeeping, the term "queen excluder" refers to example_title text Increasing Honey Production One way to encourage a honey bee colony to produce more honey is by example_title text Lifecycle of a Worker Bee The lifecycle of a worker bee consists of several stages, starting with example_title text Varroa Destructor Varroa destructor is a type of mite that example_title text Beekeeping PPE In the world of beekeeping, the acronym PPE stands for example_title text Robbing in Beekeeping The term "robbing" in beekeeping refers to the act of example_title text Role of Drone Bees Question: What's the primary function of drone bees in a hive? Answer: example_title text Honey Harvesting Device To harvest honey from a hive, beekeepers often use a device known as a example_title text Beekeeping Math Problem 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 text Swarming 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 from BEE-spoke-data

Name	Quant method	Size
smol_llama-220m-bees-internal.fp16.gguf	fp16	436.50 MB
smol_llama-220m-bees-internal.q2_k.gguf	q2_k	94.43 MB
smol_llama-220m-bees-internal.q3_k_m.gguf	q3_k_m	114.65 MB
smol_llama-220m-bees-internal.q4_k_m.gguf	q4_k_m	137.58 MB
smol_llama-220m-bees-internal.q5_k_m.gguf	q5_k_m	157.91 MB
smol_llama-220m-bees-internal.q6_k.gguf	q6_k	179.52 MB
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 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