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
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
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- Language(s) (NLP): [More Information Needed]
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- Finetuned from model [optional]: Gemma2-9B-it
Model Sources [optional]
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Uses
Direct Use
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]
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Recommendations
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How to Get Started with the Model
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Training Details
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Model Card Contact
email : Kishorekunisetty123@gmail.com