no-prompt-1.3b-GGUF/README.md

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

base_model	datasets	inference	language	license	model_creator	model_name	pipeline_tag	quantized_by	tags
appvoid/no-prompt-1.3b	appvoid/no-prompt-15k	false	en	apache-2.0	appvoid	no-prompt-1.3b	text-generation	afrideva	gguf ggml quantized q2_k q3_k_m q4_k_m q5_k_m q6_k q8_0

appvoid/no-prompt-1.3b-GGUF

Quantized GGUF model files for no-prompt-1.3b from appvoid

Name	Quant method	Size
no-prompt-1.3b.fp16.gguf	fp16	2.69 GB
no-prompt-1.3b.q2_k.gguf	q2_k	631.52 MB
no-prompt-1.3b.q3_k_m.gguf	q3_k_m	704.72 MB
no-prompt-1.3b.q4_k_m.gguf	q4_k_m	873.27 MB
no-prompt-1.3b.q5_k_m.gguf	q5_k_m	1.00 GB
no-prompt-1.3b.q6_k.gguf	q6_k	1.17 GB
no-prompt-1.3b.q8_0.gguf	q8_0	1.43 GB

Original Model Card:

no-prompt

a sheared-llama-1.3b fine-tuning

This model uses an 1.3 billion parameters model as base to be further fine-tuned on the same data as palmer. It works pretty good and even surpasses sota model on hellaswag.

evaluation

Model	ARC_C	HellaSwag	PIQA	Winogrande
tinyllama-2t	0.2807	0.5463	0.7067	0.5683
palmer-001	0.2807	0.5524	0.7106	0.5896
sheared-1.3b	0.2910	0.5935	0.7339	0.5809
no-prompt-1.3b	0.3157	0.6022	0.7334	0.5864
falcon-rw-1b-instruct-openorca (sota)	0.3362	0.5997	0.7394	0.6148

This model was trained on less than 25% of the dataset yet achieves competitive performance to current sota on open llm leaderboard.

training

Training took ~5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. no-prompt was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.

prompt

no prompt

limitations

Hallucinations are frequent, just as any transformer model this size.