pszemraj/Phi-3-small-8k-prune6

Phi-3-small-8k-instruct: 6 layers pruned

This is a layer-pruned language model created using mergekit. Layers to prune were selected based off of the average distances as follows:

Quick eval

Quick eval for: pszemraj/Phi-3-small-8k-prune6

hf (pretrained=pszemraj/Phi-3-small-8k-prune6,trust_remote_code=True,dtype=bfloat16), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 2

Tasks	Version	Filter	n-shot	Metric	Value		Stderr
arc_easy	1	none	0	acc	0.7479	±	0.0089
		none	0	acc_norm	0.7125	±	0.0093
boolq	2	none	0	acc	0.7489	±	0.0076
lambada_openai	1	none	0	perplexity	27.3270	±	1.0861
		none	0	acc	0.3600	±	0.0067
openbookqa	1	none	0	acc	0.3360	±	0.0211
		none	0	acc_norm	0.4020	±	0.0219
piqa	1	none	0	acc	0.7182	±	0.0105
		none	0	acc_norm	0.7329	±	0.0103
winogrande	1	none	0	acc	0.7143	±	0.0127

Usage

While some further pre-training will be good, it seems capable of generating coherent text as is.

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "microsoft/Phi-3-small-8k-instruct", trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    "pszemraj/Phi-3-small-8k-prune6", trust_remote_code=True
)

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

• microsoft/Phi-3-small-8k-instruct

Configuration

The following YAML configuration was used to produce this model:

dtype: bfloat16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 25]
    model: microsoft/Phi-3-small-8k-instruct
- sources:
  - layer_range: [31, 32]
    model: microsoft/Phi-3-small-8k-instruct