Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- cerebras
+- LLM
+---
+
+# Instruction-tuned Cerebras GPT 111M
+
+The smallest of [cerebras GPT models](https://huggingface.co/cerebras) with only 111M parameters instruction fine-tuned.
+
+## Model Description
+
+Instruction fine-tuned [cerebras-GPT-111M](https://huggingface.co/cerebras/Cerebras-GPT-111M)
+
+## Training data
+
+The model was fine-tuned with the following data: [alpaca_gpt4_data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM/blob/main/data/alpaca_gpt4_data.json) (data generated by GPT-4 using Alpaca prompts for fine-tuning LLMs) and [alpaca_data_cleaned](https://github.com/tloen/alpaca-lora/blob/a3027fea37c2087b8b0131b21a4cd948bbdcd9e0/alpaca_data_cleaned.json).
+
+## Prompt template
+
+Fine-tuning was performed with the promp template from [stanford alpaca](https://github.com/tatsu-lab/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.