language:
- en
license: apache-2.0
datasets:
- appvoid/no-prompt-15k
pipeline_tag: text-generation
model-index:
- name: palmer-002
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: 34.47
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002
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: 59.41
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002
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: 25.94
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002
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: 37.06
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002
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: 62.67
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002
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: 1.21
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=appvoid/palmer-002
name: Open LLM Leaderboard
palmer
a better base model
palmer is a series of ~1b parameters language models fine-tuned to be used as base models instead of using custom prompts for tasks. This means that it can be further fine-tuned on more data with custom prompts as usual or be used for downstream tasks as any base model you can get. The model has the best of both worlds: some "bias" to act as an assistant, but also the abillity to predict the next-word from its internet knowledge base. It's a 1.1b llama 2 model so you can use it with your favorite tools/frameworks.
evaluation 🧪
note that this is a zero-shot setting as opposite to open llm leaderboard's few-shot evals
Model ARC_C HellaSwag PIQA Winogrande Average
tinyllama-2 | 0.2807 | 0.5463 | 0.7067 | 0.5683 | 0.5255 |
palmer-001 | 0.2807 | 0.5524 | 0.7106 | 0.5896 | 0.5333 |
babbage-001 | 0.2944 | 0.5448 | 0.7410 | 0.5935 | 0.5434 |
deacon-1b | 0.2944 | 0.5727 | 0.7040 | 0.5801 | 0.5434 |
tinyllama-2.5 | 0.3191 | 0.5896 | 0.7307 | 0.5872 | 0.5566 |
palmer-002 | 0.3242 | 0.5956 | 0.7345 | 0.5888 | 0.5607 |
babbage-002 | 0.3285 | 0.6380 | 0.7606 | 0.6085 | 0.5839 |
This model shows exceptional performance and as of now is the best tinyllama-size base model. Furthermore, this proves LIMA paper point and serves as a good open-source alternative to openai's babbage-002.
training 🦾
Training took ~3.5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. palmer was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.
prompt 📝
no prompt 🚀
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 36.79 |
| AI2 Reasoning Challenge (25-Shot) | 34.47 |
| HellaSwag (10-Shot) | 59.41 |
| MMLU (5-Shot) | 25.94 |
| TruthfulQA (0-shot) | 37.06 |
| Winogrande (5-shot) | 62.67 |
| GSM8k (5-shot) | 1.21 |