language:
- en
license: cc-by-nc-4.0
pipeline_tag: text-generation
widget:
- text: |-
Below is an instruction that describes a task.
Write a response that appropriately completes the request.
### Instruction:
how can I become more healthy?
### Response:
example_title: example
model-index:
- name: lamini-cerebras-256m
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: 21.76
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MBZUAI/lamini-cerebras-256m
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: 28.7
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MBZUAI/lamini-cerebras-256m
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: 26.66
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MBZUAI/lamini-cerebras-256m
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: 41.81
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MBZUAI/lamini-cerebras-256m
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: 52.01
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MBZUAI/lamini-cerebras-256m
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: 0
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MBZUAI/lamini-cerebras-256m
name: Open LLM Leaderboard
LaMini-Cerebras-256M
This model is one of our LaMini-LM model series in paper "LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions".
This model is a fine-tuned version of cerebras/Cerebras-GPT-256M on LaMini-instruction dataset that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our project repository.
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
| Base model | LaMini-LM series (#parameters) | |||
|---|---|---|---|---|
| T5 | LaMini-T5-61M | LaMini-T5-223M | LaMini-T5-738M | |
| Flan-T5 | LaMini-Flan-T5-77M✩ | LaMini-Flan-T5-248M✩ | LaMini-Flan-T5-783M✩ | |
| Cerebras-GPT | LaMini-Cerebras-111M | LaMini-Cerebras-256M | LaMini-Cerebras-590M | LaMini-Cerebras-1.3B |
| GPT-2 | LaMini-GPT-124M✩ | LaMini-GPT-774M✩ | LaMini-GPT-1.5B✩ | |
| GPT-Neo | LaMini-Neo-125M | LaMini-Neo-1.3B | ||
| GPT-J | coming soon | |||
| LLaMA | coming soon | |||
Use
Intended use
We recommend using the model to respond to human instructions written in natural language. Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance. See the example on the right or the code below.
We now show you how to load and use our model using HuggingFace pipeline().
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
Training Procedure
We initialize with cerebras/Cerebras-GPT-256M and fine-tune it on our LaMini-instruction dataset. Its total number of parameters is 256M.
Training Hyperparameters
Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our paper.
Limitations
More information needed
Citation
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.49 |
| AI2 Reasoning Challenge (25-Shot) | 21.76 |
| HellaSwag (10-Shot) | 28.70 |
| MMLU (5-Shot) | 26.66 |
| TruthfulQA (0-shot) | 41.81 |
| Winogrande (5-shot) | 52.01 |
| GSM8k (5-shot) | 0.00 |