README.md

---
license: llama3
language:
- en
pipeline_tag: text-generation
tags:
- pytorch
- llama
- llama-3
datasets:
- AnonymousAuthors/OSS-License-Terms
base_model:
- meta-llama/Meta-Llama-3-8B
---

# License-Llama3-8B

## Introduction
We developed License-Llama3-8B, the first large language model (LLM) specifically designed for identifying terms in open-source software (OSS) licenses. We achieved this by first constructing a domain-specific dataset based on 3,238 OSS licenses, and then performing domain-adaptive pre-training (DAPT) and supervised fine-tuning (SFT) on the meta-llama/Meta-Llama-3-8B model. [Our work has been accepted for publication in the proceedings of the 47th International Conference on Software Engineering(ICSE 2025).](https://conf.researchr.org/details/icse-2025/icse-2025-industry-challenge-track/6/Exploring-Large-Language-Models-for-Analyzing-Open-Source-License-Conflicts-How-Far-)

License-Llama3-8B supports the identification of 27 common license terms and their corresponding three types of attitudes.The experimental results demonstrate that License-Llama3-8B achieves a precision of 92.63% and a recall of 83.89% in license term identification.  In the combined task of term and attitude identification, it achieves a precision of 90.04% and a recall of 81.55%.

## Use with transformers
Starting with transformers >= 4.42.4 onward, you can run inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

Make sure to update your transformers installation via pip install --upgrade transformers.

````python
import transformers
import torch
import json

# Definition of license terms and attitudes
Terms = {
	'Place Warranty': 'offer warranty protection (or other support), place warranty on the software licensed',
	'Add License Terms': 'provide additional license terms',
	'Add Own Notices': 'add own notices in derivative works',
	'Ask Distribution Fee': 'ask a fee to distribute a copy',
	'Combine Libraries': 'place side by side with a library (that is not an application or covered work)',
	'Copy': 'reproduce the original work in copies',
	'Distribute': 'distribute original or modified derivative works',
	'Modify': 'modify the software and create derivatives',
	'Grant Patents': 'grant rights to use copyrighted patents by the licensor, practice patent claims of contributors to the code',
	'Publicly Display': 'display the original work publicly',
	'Publicly Perform': 'perform the original work publicly',
	'Sublicense': 'incorporate the work into something that has a more restrictive license',
	'Commercial Use': 'use the software for commercial purposes',
	'Private Use': 'use or modify the software freely or privately without distributing it',
	'State Changes': 'state significant changes made to the software, cause modified files to carry prominent notices',
	'Add Statement For Additional Terms': 'place a statement of the additional terms that apply',
	'Retain Copyright Notice': 'retain the copyright notice in all copies or substantial uses of the work.',
	'Include License': 'include the full text of license(license copy) in modified software',
	'Include Notice': 'notice text needs to be distributed (if it exists) with any derivative work',
	'Offer Source Code': 'disclose your source code when you distribute the software and make the source for the library available',
	'Rename': 'the name of the derivative work must differ from original, change software name as to not misrepresent them as the original software',
	'Retain Disclaimer': 'redistributions of source code must retain disclaimer',
	'Use TradeMark': 'use contributor’s name, trademark or logo',
	'Give Credit': 'give explicit credit or acknowledgement to the author with the software',
	'Include Install Instructions': 'include build & install instructions necessary to modify and reinstall the software',
	'Liable for Damages': 'the licensor cannot be held liable for any damages arising from the use of the software',
	'Keep Same License': 'distribute the modified or derived work of the software under the terms and conditions of this license'
}


Attitudes = {"CAN": "Indicates that the licensee can perform the actions, commonly used expressions include: hereby grants to you, you may, you can",
             "CANNOT": "Indicates that the licensee is not allowed to perform the actions, commonly used expressions include: you may not, you can not, without, prohibit, refuse, disallow, decline, against",
             "MUST": "Indicates that the licensee must perform the actions, commonly used expressions include: you must, you should, as long as, shall, provided that, ensure that, ask that, have to"}


# Create the Prompt
def create_prompt(term_definition, attitude_definition, license_text):
    exm = {
    "Distribute": "CAN",
    "Use": "CAN",
    "Modify": "CANNOT"
    }

    prompt = f"""### OBJECTIVE
Identify the terms and corresponding attitudes contained in the given license text based on the definition of license terms and attitudes.

### DEFINITION OF TERMS
{term_definition}

### DEFINITION OF ATTITUDES
{attitude_definition}

### LICENSE TEXT
{license_text}

### RESPONSE
Output the results in the form of JSON key-value pairs, where the key is the term name and the value is the corresponding attitude name.

### Output Format Example
```
{json.dumps(exm, indent=2)}
```
"""
    return prompt


# Load model and create a pipeline
model_id = "AnonymousAuthors/License-Llama3-8B"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="auto"
)

# An example of extracting license terms
license_text = "you may convey modified covered source (with the effect that you shall also become a licensor) provided that you: a) retain notices as required in subsection 3.2; and b) add a notice to the modified covered source stating that you have modified it, with the date and brief description of how you have modified it."

prompt = create_prompt(Terms, Attitudes, license_text)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=512,
    eos_token_id=terminators,
    pad_token_id=pipeline.tokenizer.eos_token_id,
    do_sample=True,
    temperature=0.3,
    top_p=0.7,
)

response = outputs[0]["generated_text"][len(prompt):]

print(f"License Text: {license_text}\n")
print(f"LLM Response: {response}\n")
````

## Use with vLLM
vLLM is a fast and easy-to-use library for LLM inference and serving.    
Install vLLM with pip: 
```bash
pip install vllm == 0.3.1
```

Run the following command to start the vLLM server:
```bash
python -m vllm.entrypoints.openai.api_server \
       --served-model-name llama3-8b \
       --model /YOUR_LOCAL_PATH/AnonymousAuthors/License-Llama3-8B \
       --gpu-memory-utilization 0.9 \
       --tensor-parallel-size 1 \
       --host 0.0.0.0 \
       --port 8000
```

Then you can request the server to identify license terms:
```python
from openai import OpenAI

client = OpenAI(
    api_key='EMPTY',
    base_url='http://127.0.0.1:8000/v1',
)


def license_extract(query, model_type='llama3-8b', max_tokens=2048, temperature=0.3, top_p=0.7):
    resp = client.completions.create(
        model=model_type,
        prompt=query,
        max_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        seed=42)
    
    response = resp.choices[0].text
    return response


# An example of extracting license terms
license_text = "you may convey modified covered source (with the effect that you shall also become a licensor) provided that you: a) retain notices as required in subsection 3.2; and b) add a notice to the modified covered source stating that you have modified it, with the date and brief description of how you have modified it."

# For the definition of Terms and Attitudes, please refer to the previous section
prompt = create_prompt(Terms, Attitudes, license_text)

response = license_extract(prompt, model_type='llama3-8b', 
                            max_tokens=1500, temperature=0.3, top_p=0.7)

print(f"License Text: {license_text}\n")
print(f"LLM Response: {response}\n")
```


⚠️ **NOTE**：According to our multiple tests, we found that under the same inference parameters, the model performance is better when using vLLM for inference.