---
base_model: google/gemma-2-27b-it
datasets:
- DiTy/function-calling
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- conversational
- gemma2
- function-calling
- trl
---
# DiTy/gemma-2-27b-it-function-calling-GGUF

This model is a fine-tuned version of [google/gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it) for the **Function Calling** task on non-synthetic data, 
fully annotated by humans only, on the English version of the <ins>*DiTy/function-calling*</ins> dataset.
<!-- Provide a quick summary of what the model is/does. -->

In addition to **safetensors**, the model is available in **GGUF** formats (in this case, you need to download only a single file (*[how to inference GGUF model](https://github.com/abetlen/llama-cpp-python?tab=readme-ov-file#high-level-api)*)):

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [gemma-2-27B-it-function-calling-Q8_0.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q8_0.gguf) | Q8_0 | 28.9GB | Extremely high quality, generally unneeded but max available quant. |
| [gemma-2-27B-it-function-calling-Q6_K.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q6_K.gguf) | Q6_K | 22.3GB | Very high quality, near perfect, *recommended*. |
| [gemma-2-27B-it-function-calling-Q5_K_M.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q5_K_M.gguf) | Q5_K_M | 19.4GB | High quality, very usable. |
| [gemma-2-27B-it-function-calling-Q5_K_S.gguf](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF/blob/main/gemma-2-27B-it-function-calling-Q5_K_S.gguf) | Q5_K_S | 18.9GB | High quality, very usable. |


## Model card tree

* [How prepare your functions (tools) for *Function Calling*](#prepare_func_call)
* [Just use chat template for generation](#just_chat_template)
* [Prompt structure and expected content](#roles)
* [Evaluation of function calling models](#eval)

## Usage (HuggingFace Transformers)

Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
```bash
pip install -U transformers
```

### <a name="prepare_func_call"></a>Prepare your functions for *Function Calling*

You should write the functions (tools) used by the model in *Python code* and make sure to add *Python docstrings* as in the example below:
```python
def get_weather(city: str):
    """
    A function that returns the weather in a given city.
    
    Args:
        city: The city to get the weather for.
    """
    import random
    
    return "sunny" if random.random() > 0.5 else "rainy"
def get_sunrise_sunset_times(city: str):
    """
    A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].
    
    Args:
        city: The city to get the sunrise and sunset times for.
    """
    
    return ["6:00 AM", "6:00 PM"]
```

### <a name="just_chat_template"></a>Just use chat template

Next, you need to download the model and tokenizer:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
    "DiTy/gemma-2-27b-it-function-calling-GGUF",
    device_map="auto",
    torch_dtype=torch.bfloat16,  # use float16 or float32 if bfloat16 is not available to you.
    cache_dir=PATH_TO_MODEL_DIR,  # optional
)
tokenizer = AutoTokenizer.from_pretrained(
    "DiTy/gemma-2-27b-it-function-calling-GGUF",
    cache_dir=PATH_TO_MODEL_DIR,  # optional
)
```

To get the result of generation, just use `apply_chat_template`. In order to take into account our written functions (tools), 
we need to pass them as a list through the `tools` attribute and also use `add_prompt_generation=True`.
```python
history_messages = [
    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
]
inputs = tokenizer.apply_chat_template(
    history_messages,
    tokenize=False,
    add_generation_prompt=True,  # adding prompt for generation
    tools=[get_weather, get_sunrise_sunset_times],  # our functions (tools)
)
print(inputs)
```

Then our `inputs` will look like this:
```
<bos><start_of_turn>user
You are a helpful assistant with access to the following functions. Use them if required - {
    "name": "get_weather",
    "description": "A function that returns the weather in a given city.",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "The city to get the weather for."
            }
        },
        "required": [
            "city"
        ]
    }
},
{
    "name": "get_sunrise_sunset_times",
    "description": "A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "The city to get the sunrise and sunset times for."
            }
        },
        "required": [
            "city"
        ]
    }
}
Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
<start_of_turn>model
```

Now we can generate a model's response. 
Be careful because, after `apply_chat_template`, there is no need to *add special tokens* during tokenization. So, use `add_special_tokens=False`:
```python
terminator_ids = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<end_of_turn>"),
]
prompt_ids =  tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
generated_ids = model.generate(
    prompt_ids,
    max_new_tokens=512,
    eos_token_id=terminator_ids,
    bos_token_id=tokenizer.bos_token_id,
)
generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False)  # `skip_special_tokens=False` for debug
print(generated_response)
```

We get the generation as a function call:
```
Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
```

Great, now we can pick up and process the results with our *called function*, and then provide the model with the *function's response*:
```python
history_messages = [
    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
    {"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
    {"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},  # a hypothetical response from our function
]
inputs = tokenizer.apply_chat_template(
    history_messages,
    tokenize=False,
    add_generation_prompt=True,  # adding prompt for generation
    tools=[get_weather, get_sunrise_sunset_times],  # our functions (tools)
)
print(inputs)
```

Let's make sure the `inputs` are correct:
```
<bos><start_of_turn>user
You are a helpful assistant with access to the following functions. Use them if required - {
    "name": "get_weather",
    "description": "A function that returns the weather in a given city.",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "The city to get the weather for."
            }
        },
        "required": [
            "city"
        ]
    }
},
{
    "name": "get_sunrise_sunset_times",
    "description": "A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "The city to get the sunrise and sunset times for."
            }
        },
        "required": [
            "city"
        ]
    }
}
Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
<start_of_turn>model
Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
<start_of_turn>user
Function response: {"times_list": ["6:00 AM", "6:00 PM"]}<end_of_turn>
<start_of_turn>model
```

Similarly, we generate a response from the model:
```python
prompt_ids =  tokenizer.encode(inputs, add_special_tokens=False, return_tensors='pt').to(model.device)
generated_ids = model.generate(
    prompt_ids,
    max_new_tokens=512,
    eos_token_id=terminator_ids,
    bos_token_id=tokenizer.bos_token_id,
)
generated_response = tokenizer.decode(generated_ids[0][prompt_ids.shape[-1]:], skip_special_tokens=False)  # `skip_special_tokens=False` for debug
print(generated_response)
```

As a result, we get the model's response:
```
The sunrise time in Los Angeles is 6:00 AM.<end_of_turn>
```

## Usage via transformers `pipeline`

<details>
  <summary>
  Generation via pipeline
  </summary>

```python
from transformers import pipeline
generation_pipeline = pipeline(
    "text-generation",
    model="DiTy/gemma-2-27b-it-function-calling-GGUF",
    model_kwargs={
        "torch_dtype": torch.bfloat16,  # use float16 or float32 if bfloat16 is not supported for you. 
        "cache_dir": PATH_TO_MODEL_DIR,  # OPTIONAL
    },
    device_map="auto",
)
history_messages = [
    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
    {"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
    {"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},
]
inputs = generation_pipeline.tokenizer.apply_chat_template(
    history_messages,
    tokenize=False,
    add_generation_prompt=True,
    tools=[get_weather, get_sunrise_sunset_times],
)
terminator_ids = [
    generation_pipeline.tokenizer.eos_token_id,
    generation_pipeline.tokenizer.convert_tokens_to_ids("<end_of_turn>")
]
outputs = generation_pipeline(
    inputs,
    max_new_tokens=512,
    eos_token_id=terminator_ids,
)
print(outputs[0]["generated_text"][len(inputs):])
```
  
</details>

## <a name="roles"></a>Prompt structure and expected content

For the most correct operation of the model, it is assumed that `apply_chat_template` will be used. 
It is necessary to transmit the message history in a certain format.
```python
history_messages = [
    {"role": "...", "content": "..."},
    ...
]
```

The following roles are available for use:

* `system` - an optional role, its content is always placed at the very beginning and before listing the functions available to the model (tools).
You can always use the standard option that was used during the training: ***"You are a helpful assistant with access to the following functions. Use them if required - "***
* `user` - the user's request is transmitted through this role.
* `function-call` - The body of the function call is passed through this role.
Although the model is trained to generate a function call in the form of ***"Function call: {...}\<end_of_turn\>"***, you should still pass only the body ***"{...}"***
to the *"content"* field, since using `apply_chat_template`, the postscript in the instructions is added automatically.
* `function-response` - in this role, we must pass the response of our function in the *"content"* field as a dictionary ***'{"name_returnable_value": value}'***.
* `model` - the content under this role is considered to be the generated text of the model.

### Chat history with *Function Calling*

```
[
    {"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required - "},
    {"role": "user", "content": "Hi, can you tell me the time of sunrise in Los Angeles?"},
    {"role": "function-call", "content": '{"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}'},
    {"role": "function-response", "content": '{"times_list": ["6:00 AM", "6:00 PM"]}'},
]
```

It looks like:
```
<bos><start_of_turn>user
You are a helpful assistant with access to the following functions. Use them if required - {
    "name": "get_weather",
    "description": "A function that returns the weather in a given city.",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "The city to get the weather for."
            }
        },
        "required": [
            "city"
        ]
    }
},
{
    "name": "get_sunrise_sunset_times",
    "description": "A function that returns the time of sunrise and sunset at the present moment, for a given city, in the form of a list: [sunrise_time, sunset_time].",
    "parameters": {
        "type": "object",
        "properties": {
            "city": {
                "type": "string",
                "description": "The city to get the sunrise and sunset times for."
            }
        },
        "required": [
            "city"
        ]
    }
}
Hi, can you tell me the time of sunrise in Los Angeles?<end_of_turn>
<start_of_turn>model
Function call: {"name": "get_sunrise_sunset_times", "arguments": {"city": "Los Angeles"}}<end_of_turn>
<start_of_turn>user
Function response: {"times_list": ["6:00 AM", "6:00 PM"]}<end_of_turn>
```


### Chat history with a standard user-model template

```
[
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "Tell me about California"},
]
```

It looks like:
```
<bos><start_of_turn>user
You are a helpful assistant
Tell me about California<end_of_turn>
```

## <a name="eval"></a>Evaluation

During the learning process, the validation error was approximated to the following values:

| **Model** | **Generation Language** | **Approximately Validation Loss** |
| :-----: | :-----: | :-----: |
| [**DiTy/gemma-2-27b-it-function-calling-GGUF**](https://huggingface.co/DiTy/gemma-2-27b-it-function-calling-GGUF) | **EN** | **0.47** |
| [DiTy/gemma-2-9b-it-russian-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-russian-function-calling-GGUF) | RU | 0.57 |
| [DiTy/gemma-2-9b-it-function-calling-GGUF](https://huggingface.co/DiTy/gemma-2-9b-it-function-calling-GGUF) | EN | 0.5 |
| [DiTy/gemma-2-2b-it-function-calling](https://huggingface.co/DiTy/gemma-2-2b-it-function-calling) | EN | 0.66 |

## Citation

```none
@article{gemma_2024,
    title={Gemma},
    url={https://www.kaggle.com/m/3301},
    DOI={10.34740/KAGGLE/M/3301},
    publisher={Kaggle},
    author={Gemma Team},
    year={2024}
}
```