| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - codellama/CodeLlama-7b-hf |
| --- |
| # [EMNLP 2025] TL-CodeLLaMA-2 |
|
|
| TL-CodeLLaMA-2 is a model designed for tool use, built upon CodeLLaMA-7b. It is trained on 1,217 data samples using the *TL-Training* framework and demonstrates effective performance across a variety of tool use tasks. More information can be found in the paper "[TL-Training: A Task-Feature-Based Framework for Training Large Language Models in Tool Use](https://www.arxiv.org/abs/2412.15495)". |
|
|
| # Model Use |
|
|
| ## Requirements |
|
|
| To use this model, please make sure to install transformers: |
| ```bash |
| pip install transformers |
| ``` |
|
|
| ## Data Orgnization |
|
|
| The data needs to be organized in the following format: |
|
|
| ```json |
| [ |
| { |
| "role": "System", |
| "content": "Function:\ndef random_advice():\n \"\"\"\n Returns a random advice slip as a slip object.\n \"\"\"\n\nFunction:\ndef advice_by_id(slip_id:str):\n \"\"\"\n If an advice slip is found with the corresponding {slip_id}, a slip object is returned.\n\n Args:\n slip_id (string): The unique ID of this advice slip.\n \"\"\"\n\nFunction:\ndef search_advice(query:str):\n \"\"\"\n If an advice slip is found, containing the corresponding search term in {query}, an array of slip objects is returned inside a search object.\n\n Args:\n query (string): The search query provided.\n \"\"\"\n\nFunction:\ndef ask_to_user(question:str):\n \"\"\"\n You can ask user for guidance when you think you need more information to handle the task, but you should use this tool as less as you can.\n\n Args:\n question (string): The question you want to ask to user.\n \"\"\"\n\nFunction:\ndef finish(answer:str):\n \"\"\"\n Finish the task and give your answer.\n\n Args:\n answer (string): Your answer for the task.\n \"\"\"\n\n" |
| }, |
| { |
| "role": "User", |
| "content": "Could you give me some advice about 'love'?" |
| }, |
| { |
| "role": "Assistant", |
| "content": "search_advice(query = 'love') " |
| }, |
| { |
| "role": "Output", |
| "content": "..." |
| } |
| ] |
| ``` |
|
|
| ## Chat Template |
|
|
| The chat template is: |
|
|
| ```jinja |
| {% for message in messages %}{{message['role'] + ': ' + message['content']}}{% if loop.last %}{% if add_generation_prompt %}{{ '\nAssistant:' }}{% else %}{{ '</s>'}}{% endif %}{% else %}{{ '\n' }}{% endif %}{% endfor %} |
| ``` |
|
|
| ## Inference |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_path = "Junjie-Ye/TL-CodeLLaMA-2" |
| |
| data = [ |
| { |
| "role": "System", |
| "content": "Function:\ndef random_advice():\n \"\"\"\n Returns a random advice slip as a slip object.\n \"\"\"\n\nFunction:\ndef advice_by_id(slip_id:str):\n \"\"\"\n If an advice slip is found with the corresponding {slip_id}, a slip object is returned.\n\n Args:\n slip_id (string): The unique ID of this advice slip.\n \"\"\"\n\nFunction:\ndef search_advice(query:str):\n \"\"\"\n If an advice slip is found, containing the corresponding search term in {query}, an array of slip objects is returned inside a search object.\n\n Args:\n query (string): The search query provided.\n \"\"\"\n\nFunction:\ndef ask_to_user(question:str):\n \"\"\"\n You can ask user for guidance when you think you need more information to handle the task, but you should use this tool as less as you can.\n\n Args:\n question (string): The question you want to ask to user.\n \"\"\"\n\nFunction:\ndef finish(answer:str):\n \"\"\"\n Finish the task and give your answer.\n\n Args:\n answer (string): Your answer for the task.\n \"\"\"\n\n" |
| }, |
| { |
| "role": "User", |
| "content": "Could you give me some advice about 'love'?" |
| } |
| ] |
| |
| chat_template = "{% for message in messages %}{{message['role'] + ': ' + message['content']}}{% if loop.last %}{% if add_generation_prompt %}{{ '\nAssistant:' }}{% else %}{{ '</s>'}}{% endif %}{% else %}{{ '\n' }}{% endif %}{% endfor %}" |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| model_path, |
| torch_dtype="auto", |
| device_map="auto", |
| trust_remote_code=True |
| ).eval() |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_path, |
| padding_side="left", |
| trust_remote_code=True) |
| if tokenizer.pad_token_id is None: |
| tokenizer.pad_token_id = tokenizer.eos_token_id |
| |
| text = tokenizer.apply_chat_template( |
| data, |
| tokenize=False, |
| chat_template=chat_template, |
| add_generation_prompt=add_generation_prompt |
| ) |
| model_inputs = tokenizer( |
| [text], return_tensors="pt", padding=True).to("cuda") |
| |
| generated_ids = model.generate( |
| max_new_tokens=1024, |
| **model_inputs, |
| ) |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
| |
| print(response) |
| ``` |
|
|
| # Citation |
|
|
| If you find this model useful in your research, please cite: |
|
|
| ```bibtex |
| @inproceedings{TL-Training, |
| author = {Junjie Ye and |
| Yilong Wu and |
| Sixian Li and |
| Yuming Yang and |
| Zhiheng Xi and |
| Tao Gui and |
| Qi Zhang and |
| Xuanjing Huang and |
| Peng Wang and |
| Zhongchao Shi and |
| Jianping Fan and |
| Zhengyin Du}, |
| editor = {Christos Christodoulopoulos and |
| Tanmoy Chakraborty and |
| Carolyn Rose and |
| Violet Peng}, |
| title = {TL-Training: {A} Task-Feature-Based Framework for Training Large Language |
| Models in Tool Use}, |
| booktitle = {Findings of the Association for Computational Linguistics: {EMNLP} |
| 2025, Suzhou, China, November 4-9, 2025}, |
| pages = {239--258}, |
| publisher = {Association for Computational Linguistics}, |
| year = {2025}, |
| url = {https://aclanthology.org/2025.findings-emnlp.15/}, |
| timestamp = {Fri, 20 Feb 2026 08:07:46 +0100}, |
| biburl = {https://dblp.org/rec/conf/emnlp/YeWLYXGZHWSFD25.bib}, |
| bibsource = {dblp computer science bibliography, https://dblp.org} |
| } |
| ``` |
|
|
