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:

python

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.

RichardErkhov/SebastianSchramm_-_Cerebras-GPT-111M-instruction-8bits

Original model description:

Instruction-tuned Cerebras GPT 111M

Model Description

Evaluation

Training data

Prompt template

Usage

RichardErkhov/
SebastianSchramm_-_Cerebras-GPT-111M-instruction-8bits