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---
language:
- en
license: apache-2.0
tags:
- merge
model-index:
- name: palmer-002.5
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 37.54
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 61.84
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.21
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.22
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.38
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002.5
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 1.97
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002.5
name: Open LLM Leaderboard
---
Creative writing has never been so accesible, palmer goes beyond what it was thought about small language models. This model is a "MErging of Experts" (MEoE) using `palmer-002-2401` as base, biased as an assistant without using any prompts—as a result of these efforts—palmer is better than most 1b language models on most benchmarks, despite being sometimes 40% smaller than its counterparts.
```
MMLU ARC-C OBQA HellaSwag PIQA Winogrande Average
tinyllama-chat | 0.2470 | 0.3285 | 0.3740 | 0.6037 | 0.7448 | 0.6022 | 0.4833 |
zyte-1b | 0.2397 | 0.3353 | 0.3700 | 0.6086 | 0.7541 | 0.5998 | 0.4845 |
palmer-002.5 | 0.2534 | 0.3370 | 0.3740 | 0.6128 | 0.7486 | 0.6535 | 0.4965 |
qwen-1-8 | 0.4536 | 0.3490 | 0.3320 | 0.5876 | 0.7307 | 0.5896 | 0.5070 |
```
This work constitutes, given its compactness, an advancement towards SMLs, easily empowering edge devices such as mobile phones, raspberry pis and automated software/robots. Aditionally, palmer-002.5 deviates its main philosophy from palmer-family to become a more powerful model with more data instead of less.
```
prompt: Reality is but
output: a dream,
And the dreams we make are our reality.
The world is a canvas, painted by our minds,
And we can make it a masterpiece.
So let us create, let us dream,
And let our imagination run wild.
For in our imagination lies our power,
To create a world that is truly our own.
```
You can support me [through kofi](https://ko-fi.com/appvoid)
Note that since this model uses a transformer architecture as any popular language model, its output sometimes contains hallucinations (make mistakes or false statements), and as such, it must be used with caution on sensitive scenarios.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_appvoid__palmer-002.5)
| Metric |Value|
|---------------------------------|----:|
|Avg. |38.86|
|AI2 Reasoning Challenge (25-Shot)|37.54|
|HellaSwag (10-Shot) |61.84|
|MMLU (5-Shot) |25.21|
|TruthfulQA (0-shot) |40.22|
|Winogrande (5-shot) |66.38|
|GSM8k (5-shot) | 1.97|
|