README.md

---
library_name: transformers
tags:
- LAM
- tooluse
- function calling
license: mit
datasets:
- Salesforce/xlam-function-calling-60k
---

# Model Card for Model ID

ActionGemma is a LargeActionModel inspired from Salesforce/xLAM and trained on it's dataset. this is a combination of multi-lingual capabilities of Gemma with Function calling capabilites from xLAM dataset
Now you have a Action /function calling Model for all the languages supported by gemma2


## Model Details

Base Model : Gemma2-9B-it
Fine-Tuned on : xLAM dataset

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** KishoreK
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Gemma2-9B-it

### Model Sources [optional]

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- **Paper [optional]:** [More Information Needed]
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## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
tokenizer in built have a chat template as follows
```
tokenizer = AutoTokenizer.from_pretrained("KishoreK/ActionGemma-9B")
tokenizer.chat_template = """{{ bos_token }}
{% for message in messages %}
    {% if message['role'] == 'tools' %}
        {{ '<unused0>\n' + message['content'] + '<unused1>\n'}}
    {% else %}
      {{ '<start_of_turn>' + message['role'] + '\n' + message['content'] | trim + '<end_of_turn>\n' }}
  {% endif %}
{% endfor %}

{% if add_generation_prompt %}
  {{ '<start_of_turn>assistant\n' }}
{% endif %}
"""
```
you can utilise this or modify based on this syntax.

```
task_instruction = """
You are an expert in composing functions. You are given a question and a set of possible functions. 
Based on the question, you will need to make one or more function/tool calls to achieve the purpose. 
If none of the functions can be used, point it out and refuse to answer. 
If the given question lacks the parameters required by the function, also point it out.
""".strip()

get_weather_api = {
    "name": "get_weather",
    "description": "Get the current weather for a location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, New York"
            },
            "unit": {
                "type": "string",
                "enum": ["celsius", "fahrenheit"],
                "description": "The unit of temperature to return"
            }
        },
        "required": ["location"]
    }
}

search_api = {
    "name": "search",
    "description": "Search for information on the internet",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "The search query, e.g. 'latest news on AI'"
            }
        },
        "required": ["query"]
    }
}

openai_format_tools = [get_weather_api, search_api]

def convert_to_xlam_tool(tools):
    ''''''
    if isinstance(tools, dict):
        return {
            "name": tools["name"],
            "description": tools["description"],
            "parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()}
        }
    elif isinstance(tools, list):
        return [convert_to_xlam_tool(tool) for tool in tools]
    else:
        return tools

user_query = "अमेरिका के राष्ट्रपति कौन है?"
tools = openai_format_tools
messages = [{
    "role" : "system",
    "content" : task_instruction
},{
    "role" : "user",
    "content" : user_query
},{
    "role": "tools",
    "content": json.dumps(convert_to_xlam_tool(tools))
}]

print(tokenizer.decode(tokenizer.apply_chat_template(messages, add_generation_prompt=True)))
```

sample response from applied chat template 
```
<bos>
      <start_of_turn>system
You are an expert in composing functions. You are given a question and a set of possible functions. 
Based on the question, you will need to make one or more function/tool calls to achieve the purpose. 
If none of the functions can be used, point it out and refuse to answer. 
If the given question lacks the parameters required by the function, also point it out.<end_of_turn>

      <start_of_turn>user
अमेरिका के राष्ट्रपति कौन है?<end_of_turn>

        <unused0>
[{"name": "get_weather", "description": "Get the current weather for a location", "parameters": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, New York"}, "unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "The unit of temperature to return"}}}, {"name": "search", "description": "Search for information on the internet", "parameters": {"query": {"type": "string", "description": "The search query, e.g. 'latest news on AI'"}}}]<unused1>


  <start_of_turn>assistant
```
invoking model with this prompt
```
model = AutoModelForCausalLM.from_pretrained("KishoreK/ActionGemma-9B", use_cache=True)
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
output for the question
```
[{"name": "search", "arguments": {"query": "US President"}}]
```
chat template, inference are referenced from xLAM documentation.
[More Information Needed]

### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Hardware

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## Citation [optional]

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

email : Kishorekunisetty123@gmail.com