Create README.md

README.md

@@ -0,0 +1,70 @@
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: summarization
+widget:
+- text: What is the peak phase of T-eV?
+  example_title: Question Answering
+tags:
+- arxiv
+---
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Usage](#usage)
+3. [Uses](#uses)
+4. [Citation](#citation)
+
+# TL;DR
+
+This is a Phi-1_5 model trained on [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology). This model is for research purposes only and ***should not be used in production settings***.
+
+
+## Model Description
+
+
+- **Model type:** Language model
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Related Models:** [Phi-1_5](https://huggingface.co/microsoft/phi-1_5)
+
+# Usage
+
+Find below some example scripts on how to use the model in `transformers`:
+
+## Using the Pytorch model
+
+```python
+
+from huggingface_hub import notebook_login
+from datasets import load_dataset, Dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+model = "ArtifactAI/phi-biology"
+
+model = AutoModelForCausalLM.from_pretrained(base_model, trust_remote_code= True)
+tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+
+def generate(prompt):
+  inputs = tokenizer(f'''Below is an instruction that describes a task. Write a response that appropriately completes the request If you are adding additional white spaces, stop writing".\n\n### Instruction:\n{prompt}.\n\n### Response:\n ''', return_tensors="pt", return_attention_mask=False)
+  streamer = TextStreamer(tokenizer, skip_prompt= True)
+  _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)
+  
+generate("What are the common techniques used in identifying a new species, and how can scientists accurately categorize it within the existing taxonomy system?")
+```
+
+## Training Data
+
+The model was trained on [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology), a dataset of question/answer pairs. Questions are generated using the t5-base model, while the answers are generated using the GPT-3.5-turbo model.
+
+# Citation
+
+```
+@misc{phi-math,
+    title={phi-biology},
+    author={Matthew Kenney},
+    year={2023}
+}
+```