|
--- |
|
license: llama3.1 |
|
language: |
|
- en |
|
base_model: |
|
- meta-llama/Llama-3.1-8B-Instruct |
|
pipeline_tag: question-answering |
|
tags: |
|
- biology |
|
- medical |
|
datasets: |
|
- shellwork/ChatParts_Dataset |
|
--- |
|
|
|
# shellwork/ChatParts-llama3.1-8b |
|
|
|
🤖 [XJTLU-Software RAG GitHub Repository](https://github.com/shellwork/XJTLU-Software-RAG/tree/main) • 📊 [ChatParts Dataset](https://huggingface.co./datasets/shellwork/ChatParts_Dataset) |
|
|
|
**shellwork/ChatParts-llama3.1-8b** is a specialized dialogue model fine-tuned from **Meta-Llama-3.1-8B-Instruct** by the XJTLU-Software iGEM Competition team. This model is tailored for the synthetic biology domain, aiming to assist competition participants and researchers in efficiently collecting and organizing relevant information. It serves as the local model component of the XJTLU-developed Retrieval-Augmented Generation (RAG) software, enhancing search and summarization capabilities within synthetic biology data. |
|
|
|
## 📚 Dataset Information |
|
|
|
The model is trained on a comprehensive synthetic biology-specific dataset curated from multiple authoritative sources: |
|
|
|
- **iGEM Wiki Pages (2004-2023):** Comprehensive coverage of synthetic biology topics from over two decades of iGEM competitions. |
|
- **Synthetic Biology Review Papers:** More than 1,000 high-quality review articles providing in-depth insights into various aspects of synthetic biology. |
|
- **iGEM Parts Registry Documentation:** Detailed documentation of parts used in iGEM projects, facilitating accurate information retrieval. |
|
|
|
In total, the dataset comprises over **200,000 question-answer pairs**, meticulously assembled to cover a wide spectrum of synthetic biology topics. For more detailed information about the dataset, please visit our [training data repository](https://huggingface.co./datasets/shellwork/ChatParts_Dataset). |
|
|
|
## 🛠️ How to Use |
|
|
|
This repository supports usage with the `transformers` library. Below is a straightforward example of how to deploy the **shellwork/ChatParts-llama3.1-8b** model using `transformers`. |
|
|
|
### Requirements |
|
|
|
- **Transformers Library:** Ensure you have `transformers` version **>= 4.43.0** installed. You can update your installation using: |
|
|
|
```bash |
|
pip install --upgrade transformers |
|
``` |
|
|
|
### Example: Deploying with Transformers |
|
|
|
```python |
|
import torch |
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
import json |
|
|
|
# Load the tokenizer and model |
|
tokenizer = AutoTokenizer.from_pretrained('shellwork/ChatParts-llama3.1-8b', trust_remote_code=True) |
|
model = AutoModelForCausalLM.from_pretrained( |
|
'shellwork/ChatParts-llama3.1-8b', |
|
torch_dtype=torch.bfloat16, |
|
trust_remote_code=True, |
|
device_map='auto' |
|
) |
|
|
|
# Example context from synthetic biology literature |
|
context = ''' |
|
Synthetic biology enables the design and construction of new biological parts, devices, and systems, or the re-design of existing natural biological systems. |
|
''' |
|
|
|
query = "What is the goal of synthetic biology?" |
|
|
|
# Generate the response with fine-grained citations |
|
result = model.query_longcite( |
|
context, |
|
query, |
|
tokenizer=tokenizer, |
|
max_input_length=128000, |
|
max_new_tokens=1024 |
|
) |
|
|
|
# Display the results |
|
print("Answer:\n{}\n".format(result['answer'])) |
|
print("Statement with citations:\n{}\n".format( |
|
json.dumps(result['statements_with_citations'], indent=2, ensure_ascii=False) |
|
)) |
|
print("Context (divided into sentences):\n{}\n".format(result['splited_context'])) |
|
``` |
|
|
|
|
|
## 📄 License |
|
|
|
This model is released under the **Llama-3.1 License**. For more details, please refer to the [license information](https://huggingface.co./meta-llama/Llama-3.1-8B-Instruct) in the repository. |
|
|
|
## 🔗 Additional Resources |
|
|
|
- **RAG Software:** Explore the full capabilities of our Retrieval-Augmented Generation software [here](https://github.com/shellwork/XJTLU-Software-RAG/tree/main). |
|
- **Training Data:** Access and review the extensive training dataset [here](https://huggingface.co./datasets/shellwork/ChatParts_Dataset) . |
|
- **Support & Contributions:** For support or to contribute to the project, visit our [GitHub Issues](https://github.com/shellwork/XJTLU-Software-RAG/issues) page. |
|
|
|
|
|
Feel free to reach out through our GitHub repository for any questions, issues, or contributions related to **shellwork/ChatParts-llama3.1-8b**. |
