Create README.md

README.md

@@ -0,0 +1,42 @@
+---
+language:
+- en
+pipeline_tag: text-generation
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model aims to handle **Multi-hop Question answering** by splitting a multi-hop questions into a sequence of single questions, handle these single questions then summarize the information to get the final answer.
+## Model Details
+This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the dataset: [khaimaitien/qa-expert-multi-hop-qa-V1.0](https://huggingface.co/datasets/khaimaitien/qa-expert-multi-hop-qa-V1.0)
+
+You can get more information about how to **use/train** the model from this repo: https://github.com/khaimt/qa_expert
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [https://github.com/khaimt/qa_expert]
+
+## How to Get Started with the Model
+First, you need to clone the repo: https://github.com/khaimt/qa_expert
+
+Then install the requirements:
+
+```shell
+pip install -r requirements.txt
+```
+Here is the example code: 
+
+```python
+from qa_expert import get_inference_model, InferenceType
+def retrieve(query: str) -> str:
+    # You need to implement this retrieval function, input is a query and output is a string
+    # This can be treated as the function to call in function calling of OpenAI
+    return context
+
+model_inference = get_inference_model(InferenceType.hf, "khaimaitien/qa-expert-7B-V1.0")
+answer, messages = model_inference.generate_answer(question, retriever_func)
+```
+