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import spaces |
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import json |
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import torch |
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import gradio as gr |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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title = """# 🙋🏻♂️ Welcome to Tonic's Salesforce/Xlam-7B-r""" |
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description = """ |
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Large Action Models (LAMs) are advanced large language models designed to enhance decision-making and translate user intentions into executable actions that interact with the world. LAMs autonomously plan and execute tasks to achieve specific goals, serving as the brains of AI agents. They have the potential to automate workflow processes across various domains, making them invaluable for a wide range of applications. |
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### Join us : |
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🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [](https://discord.gg/GWpVpekp) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Build Tonic](https://git.tonic-ai.com/)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 |
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""" |
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model_name = "Salesforce/xLAM-7b-r" |
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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torch.random.manual_seed(0) |
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task_instruction = """ |
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Based on the previous context and API request history, generate an API request or a response as an AI assistant.""".strip() |
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format_instruction = """ |
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The output should be of the JSON format, which specifies a list of generated function calls. The example format is as follows, please make sure the parameter type is correct. If no function call is needed, please make |
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tool_calls an empty list "[]". |
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``` |
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{"thought": "the thought process, or an empty string", "tool_calls": [{"name": "api_name1", "arguments": {"argument1": "value1", "argument2": "value2"}}]} |
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``` |
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""".strip() |
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def convert_to_xlam_tool(tools): |
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if isinstance(tools, dict): |
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return { |
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"name": tools["name"], |
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"description": tools["description"], |
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"parameters": {k: v for k, v in tools["parameters"].get("properties", {}).items()} |
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} |
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elif isinstance(tools, list): |
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return [convert_to_xlam_tool(tool) for tool in tools] |
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else: |
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return tools |
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def build_conversation_history_prompt(conversation_history: str): |
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parsed_history = [] |
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for step_data in conversation_history: |
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parsed_history.append({ |
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"step_id": step_data["step_id"], |
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"thought": step_data["thought"], |
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"tool_calls": step_data["tool_calls"], |
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"next_observation": step_data["next_observation"], |
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"user_input": step_data['user_input'] |
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}) |
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history_string = json.dumps(parsed_history) |
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return f"\n[BEGIN OF HISTORY STEPS]\n{history_string}\n[END OF HISTORY STEPS]\n" |
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def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str, conversation_history: list): |
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prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n" |
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prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(tools)}\n[END OF AVAILABLE TOOLS]\n\n" |
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prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n" |
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prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n" |
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if len(conversation_history) > 0: |
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prompt += build_conversation_history_prompt(conversation_history) |
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return prompt |
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@spaces.GPU |
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def generate_response(tools_input, query): |
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try: |
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tools = json.loads(tools_input) |
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except json.JSONDecodeError: |
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return "Error: Invalid JSON format for tools input." |
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xlam_format_tools = convert_to_xlam_tool(tools) |
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conversation_history = [] |
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content = build_prompt(task_instruction, format_instruction, xlam_format_tools, query, conversation_history) |
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messages = [ |
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{'role': 'user', 'content': content} |
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] |
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inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) |
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outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) |
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agent_action = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True) |
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return agent_action |
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iface = gr.Interface( |
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fn=generate_response, |
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inputs=[ |
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gr.Textbox( |
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label="Available Tools (JSON format)", |
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lines=20, |
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value=json.dumps([ |
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{ |
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"name": "get_weather", |
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"description": "Get the current weather for a location", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"location": { |
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"type": "string", |
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"description": "The city and state, e.g. San Francisco, New York" |
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}, |
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"unit": { |
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"type": "string", |
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"enum": ["celsius", "fahrenheit"], |
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"description": "The unit of temperature to return" |
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} |
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}, |
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"required": ["location"] |
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} |
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}, |
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{ |
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"name": "search", |
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"description": "Search for information on the internet", |
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"parameters": { |
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"type": "object", |
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"properties": { |
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"query": { |
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"type": "string", |
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"description": "The search query, e.g. 'latest news on AI'" |
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} |
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}, |
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"required": ["query"] |
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} |
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} |
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], indent=2) |
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), |
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gr.Textbox(label="User Query", lines=2, value="What's the weather like in New York in fahrenheit?") |
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], |
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outputs=gr.Textbox(label="Generated Response", lines=5), |
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title=title, |
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description=description, |
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) |
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if __name__ == "__main__": |
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iface.launch() |
