metadata
base_model:
- microsoft/Phi-3-small-8k-instruct
library_name: transformers
tags:
- mergekit
- merge
license: mit
language:
- en
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:
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