README.md

---
license: apache-2.0
language:
- en
base_model:
- codellama/CodeLlama-7b-hf
---
# **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)
```