Create README.md

README.md

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+---
+license: cc
+pipeline_tag: text-generation
+---
+
+# TimeLlama
+
+TimeLlama is an instruction-finetuned Llama2 series that improves complex temporal reasoning ability.
+## Model Details
+### Model Description
+In this work, we introduce the first multi-source dataset for explainable temporal reasoning, called ExpTime. The dataset contains 26k examples derived from temporal knowledge graph datasets. Each example includes a context with multiple events, a future event to predict, and an explanation for the prediction in the form of temporal reasoning over the events.
+
+To generate the dataset, we propose a novel knowledge-graph-instructed-generation strategy. The dataset supports the comprehensive evaluation of large language models on complex temporal reasoning, future event prediction, and explainability.
+
+Based on ExpTime, we develop TimeLlaMA, a series of LLM models fine-tuned for explainable temporal reasoning. TimeLlaMA builds on the foundation LLM LLaMA-2 and utilizes instruction tuning to follow prompts for making explanations.
+
+### Model Sources
+
+- **Repository:** https://github.com/chenhan97/TimeLlama
+- **Paper:** https://arxiv.org/abs/2310.01074
+
+## Uses
+
+### Direct Use
+```python
+from transformers import LlamaConfig, LlamaTokenizer, LlamaForCausalLM
+# Model names: "chrisyuan45/TimeLlama-7b-chat", "chrisyuan45/TimeLlama-13b-chat"
+model = LlamaForCausalLM.from_pretrained(
+        model_name,
+        return_dict=True,
+        load_in_8bit=quantization,
+        device_map="auto",
+        low_cpu_mem_usage=True)
+tokenizer = LlamaTokenizer.from_pretrained(model_name)
+```
+
+### Finetune
+Please check our repository for the detailed finetuning method.