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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:

palmer

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.

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160
GGUF
Model size
1.35B params
Architecture
llama
+1
Inference Examples
Inference API (serverless) has been turned off for this model.

Quantized from

Dataset used to train afrideva/no-prompt-1.3b-GGUF