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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}
}
```
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