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--- |
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license: llama3 |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- pytorch |
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- llama |
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- llama-3 |
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datasets: |
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- AnonymousAuthors/OSS-License-Terms |
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base_model: |
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- meta-llama/Meta-Llama-3-8B |
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--- |
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# License-Llama3-8B |
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## Introduction |
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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-) |
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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%. |
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## Use with transformers |
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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. |
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Make sure to update your transformers installation via pip install --upgrade transformers. |
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````python |
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import transformers |
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import torch |
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import json |
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# Definition of license terms and attitudes |
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Terms = { |
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'Place Warranty': 'offer warranty protection (or other support), place warranty on the software licensed', |
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'Add License Terms': 'provide additional license terms', |
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'Add Own Notices': 'add own notices in derivative works', |
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'Ask Distribution Fee': 'ask a fee to distribute a copy', |
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'Combine Libraries': 'place side by side with a library (that is not an application or covered work)', |
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'Copy': 'reproduce the original work in copies', |
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'Distribute': 'distribute original or modified derivative works', |
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'Modify': 'modify the software and create derivatives', |
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'Grant Patents': 'grant rights to use copyrighted patents by the licensor, practice patent claims of contributors to the code', |
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'Publicly Display': 'display the original work publicly', |
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'Publicly Perform': 'perform the original work publicly', |
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'Sublicense': 'incorporate the work into something that has a more restrictive license', |
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'Commercial Use': 'use the software for commercial purposes', |
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'Private Use': 'use or modify the software freely or privately without distributing it', |
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'State Changes': 'state significant changes made to the software, cause modified files to carry prominent notices', |
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'Add Statement For Additional Terms': 'place a statement of the additional terms that apply', |
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'Retain Copyright Notice': 'retain the copyright notice in all copies or substantial uses of the work.', |
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'Include License': 'include the full text of license(license copy) in modified software', |
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'Include Notice': 'notice text needs to be distributed (if it exists) with any derivative work', |
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'Offer Source Code': 'disclose your source code when you distribute the software and make the source for the library available', |
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'Rename': 'the name of the derivative work must differ from original, change software name as to not misrepresent them as the original software', |
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'Retain Disclaimer': 'redistributions of source code must retain disclaimer', |
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'Use TradeMark': 'use contributor’s name, trademark or logo', |
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'Give Credit': 'give explicit credit or acknowledgement to the author with the software', |
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'Include Install Instructions': 'include build & install instructions necessary to modify and reinstall the software', |
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'Liable for Damages': 'the licensor cannot be held liable for any damages arising from the use of the software', |
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'Keep Same License': 'distribute the modified or derived work of the software under the terms and conditions of this license' |
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} |
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Attitudes = {"CAN": "Indicates that the licensee can perform the actions, commonly used expressions include: hereby grants to you, you may, you can", |
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"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", |
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"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"} |
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# Create the Prompt |
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def create_prompt(term_definition, attitude_definition, license_text): |
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exm = { |
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"Distribute": "CAN", |
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"Use": "CAN", |
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"Modify": "CANNOT" |
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} |
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prompt = f"""### OBJECTIVE |
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Identify the terms and corresponding attitudes contained in the given license text based on the definition of license terms and attitudes. |
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### DEFINITION OF TERMS |
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{term_definition} |
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### DEFINITION OF ATTITUDES |
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{attitude_definition} |
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### LICENSE TEXT |
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{license_text} |
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### RESPONSE |
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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. |
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### Output Format Example |
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``` |
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{json.dumps(exm, indent=2)} |
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``` |
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""" |
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return prompt |
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# Load model and create a pipeline |
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model_id = "AnonymousAuthors/License-Llama3-8B" |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model_id, |
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model_kwargs={"torch_dtype": torch.bfloat16}, |
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device="auto" |
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) |
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# An example of extracting license terms |
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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." |
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prompt = create_prompt(Terms, Attitudes, license_text) |
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terminators = [ |
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pipeline.tokenizer.eos_token_id, |
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pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") |
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] |
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outputs = pipeline( |
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prompt, |
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max_new_tokens=512, |
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eos_token_id=terminators, |
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pad_token_id=pipeline.tokenizer.eos_token_id, |
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do_sample=True, |
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temperature=0.3, |
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top_p=0.7, |
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) |
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response = outputs[0]["generated_text"][len(prompt):] |
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print(f"License Text: {license_text}\n") |
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print(f"LLM Response: {response}\n") |
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```` |
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## Use with vLLM |
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vLLM is a fast and easy-to-use library for LLM inference and serving. |
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Install vLLM with pip: |
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```bash |
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pip install vllm == 0.3.1 |
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``` |
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Run the following command to start the vLLM server: |
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```bash |
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python -m vllm.entrypoints.openai.api_server \ |
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--served-model-name llama3-8b \ |
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--model /YOUR_LOCAL_PATH/AnonymousAuthors/License-Llama3-8B \ |
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--gpu-memory-utilization 0.9 \ |
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--tensor-parallel-size 1 \ |
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--host 0.0.0.0 \ |
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--port 8000 |
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``` |
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Then you can request the server to identify license terms: |
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```python |
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from openai import OpenAI |
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client = OpenAI( |
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api_key='EMPTY', |
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base_url='http://127.0.0.1:8000/v1', |
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) |
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def license_extract(query, model_type='llama3-8b', max_tokens=2048, temperature=0.3, top_p=0.7): |
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resp = client.completions.create( |
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model=model_type, |
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prompt=query, |
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max_tokens=max_tokens, |
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temperature=temperature, |
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top_p=top_p, |
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seed=42) |
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response = resp.choices[0].text |
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return response |
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# An example of extracting license terms |
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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." |
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# For the definition of Terms and Attitudes, please refer to the previous section |
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prompt = create_prompt(Terms, Attitudes, license_text) |
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response = license_extract(prompt, model_type='llama3-8b', |
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max_tokens=1500, temperature=0.3, top_p=0.7) |
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print(f"License Text: {license_text}\n") |
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print(f"LLM Response: {response}\n") |
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``` |
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⚠️ **NOTE**：According to our multiple tests, we found that under the same inference parameters, the model performance is better when using vLLM for inference. |
