language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- cerebras
- LLM
inference: false
base_model: cerebras/Cerebras-GPT-111M
Instruction-tuned Cerebras GPT 111M
The smallest of cerebras GPT models with only 111M parameters instruction fine-tuned.
Model Description
Instruction fine-tuned cerebras-GPT-111M
Evaluation
The model has been evaluated with Huggingface's Open LLM leaderboard. Have a look at the leaderboard for more details: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard The performance of the instruction fine-tuned model does improve compared to the cerebras base model by about 5.7% (average score):
| Model | Average | ARC (25-shot) | HellaSwag (10-shot) | MMLU (5-shot) | TruthfulQA (0-shot) |
|---|---|---|---|---|---|
| SebastianSchramm/Cerebras-GPT-111M-instruction | 31.6 | 24.3 | 26.2 | 26.5 | 49.5 |
| cerebras/Cerebras-GPT-111M | 29.9 | 20 | 26.7 | 26.7 | 46.3 |
Training data
The model was fine-tuned with the following data: alpaca_gpt4_data (data generated by GPT-4 using Alpaca prompts for fine-tuning LLMs) and alpaca_data_cleaned.
Prompt template
Fine-tuning was performed with the promp template from stanford alpaca:
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
Usage
It is recommended to format input according to the prompt template mentioned above during inference for best results.