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README.md

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+# README: tinyChat Instruction-Based LLM
+
+Introducing tinyChat, the instruction-based Large Language Model (LLM) that’s less than 1% the size of GPT-3.5. tinyChat is an open-source model under the Apache 2.0 license and based on Google’s Flan-T5-Large, a 770m parameter model. Although not as performant as larger models, tinyChat can perform a variety of NLP tasks such as summarization, question answering, and sentiment analysis.
+
+tinyChat is available on the HuggingFace model hub and the code repository is on GitHub. While tinyChat is open-sourced, we do not recommend using it in a production setting in its current state.
+
+
+## Use Cases
+
+- Chatbots
+- Summarization
+- Sentiment analysis
+- Q&A systems
+- Text completion
+- Language modeling
+- Mobile applications
+- Complementing larger LLMs
+
+## Future Directions
+
+- Improving model accuracy
+- Reducing biases and toxicity
+- Developing new datasets
+- Collaborating with the open-source community
+- Applying tinyChat to new domains
+
+## Acknowledgements
+
+We express our gratitude to OpenAI, Hugging Face, Microsoft Research, and the creators of the Pile, Alpaca, and Databricks 15k datasets for their contributions to the landscape of open-source machine learning and the advancement of generative AI.
+
+## Running the Code
+
+```python
+import transformers
+from transformers import PeftModel
+
+model_name = "google/flan-t5-large"
+peft_model_id = "ckpts_databricks_large"
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+base_model = transformers.AutoModelForSeq2SeqLM.from_pretrained(model_name)
+peft_model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+inputs = tokenizer("""[INSERT INSTRUCTION HERE]""", return_tensors="pt")
+outputs = peft_model.generate(**inputs, max_length=300, do_sample=True)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+
+
+
 ---
 license: apache-2.0
 ---