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Model: driaforall/Tiny-Agent-a-0.5B
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-0.5B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- chat
+- qwen
+- qwen-coder
+- agent
+---
+
+
+# Tiny-Agent-α
+
+## Introduction
+
+***Tiny-Agent-α*** is an extension of Dria-Agent-a, trained on top of the [Qwen2.5-Coder](https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f) series to be used in edge devices. These models are carefully fine tuned with quantization aware training to minimize performance degradation after quantization. 
+
+Tiny-Agent-α employs ***Pythonic function calling***, which is LLMs using blocks of Python code to interact with provided tools and output actions. This method was inspired by many previous work, including but not limited to [DynaSaur](https://arxiv.org/pdf/2411.01747), [RLEF](https://arxiv.org/pdf/2410.02089), [ADAS](https://arxiv.org/pdf/2408.08435) and [CAMEL](https://arxiv.org/pdf/2303.17760). This way of function calling has a few advantages over traditional JSON-based function calling methods:
+
+1. **One-shot Parallel Multiple Function Calls:** The model can can utilise many synchronous processes in one chat turn to arrive to a solution, which would require other function calling models multiple turns of conversation.
+2. **Free-form Reasoning and Actions:** The model provides reasoning traces freely in natural language and the actions in between \`\`\`python \`\`\` blocks, as it already tends to do without special prompting or tuning. This tries to mitigate the possible performance loss caused by imposing specific formats on LLM outputs discussed in [Let Me Speak Freely?](https://arxiv.org/pdf/2408.02442)
+3. **On-the-fly Complex Solution Generation:** The solution provided by the model is essentially a Python program with the exclusion of some "risky" builtins like `exec`, `eval` and `compile` (see full list in **Quickstart** below). This enables the model to implement custom complex logic with conditionals and synchronous pipelines (using the output of one function in the next function's arguments) which would not be possible with the current JSON-based function calling methods (as far as we know).
+
+## Quickstart
+
+You can use tiny-agents easily with the dria_agent package:
+
+````bash
+pip install dria_agent
+````
+
+This package handles models, tools, code execution, and backend (supports mlx, ollama, and transformers).
+
+### Usage 
+
+Decorate functions with @tool to expose them to the agent.
+
+````python
+from dria_agent import tool
+
+@tool
+def check_availability(day: str, start_time: str, end_time: str) -> bool:
+    """
+    Checks if a given time slot is available.
+
+    :param day: The date in "YYYY-MM-DD" format.
+    :param start_time: The start time of the desired slot (HH:MM format, 24-hour).
+    :param end_time: The end time of the desired slot (HH:MM format, 24-hour).
+    :return: True if the slot is available, otherwise False.
+    """
+    # Mock implementation
+    if start_time == "12:00" and end_time == "13:00":
+        return False
+    return True
+````
+
+Create an agent with custom tools:
+
+```python
+from dria_agent import ToolCallingAgent
+
+agent = ToolCallingAgent(
+    tools=[check_availability]
+)
+```
+
+Use agent.run(query) to execute tasks with tools.
+
+```python
+execution = agent.run("Check my calendar for tomorrow noon", print_results=True)
+```
+
+Where output is
+````
+let me help you check your availability for a 1-hour meditation session       
+starting at noon tomorrow.                                                    
+                                                                                
+Step-by-step reasoning:                                                       
+ 1. We need to check availability for a specific time slot (noon)              
+ 2. The duration is 1 hour, so we'll use the same start and end times          
+ 3. Since it's tomorrow, we should format the date as "YYYY-MM-DD"             
+ 4. Use the check_availability() function with these parameters                
+                                                                                
+Here's the code to check your availability:                                   
+                                                                                
+```python                                                                     
+tomorrow = (datetime.now() + timedelta(days=1)).strftime("%Y-%m-%d")          
+start_time = "12:00"  # Noon in 24-hour format                                
+end_time = "13:00"   # One hour after noon                                    
+                                                                                
+availability = check_availability(tomorrow, start_time, end_time)             
+```                                                                           
+                                                                                
+The code will:                                                                
+- Calculate tomorrow's date using datetime and timedelta                      
+- Set the time slot to noon (12:00) for 1 hour duration                       
+- Check if this time slot is available using the check_availability function  
+                                                                                
+The availability variable will contain True if you're available, or False if  
+not. 
+````
+
+If using dria_agent, system prompts and tooling work out of the box—no extra setup needed. 
+
+You can use Tiny-Agent-1.5B directly with transformers:
+
+````python
+import json
+from typing import Any, Dict, List
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "driaforall/Tiny-Agent-a-3B"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", torch_dtype="auto", trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# System prompt for optimal performance
+SYSTEM_PROMPT = """
+You are an expert AI assistant that specializes in providing Python code to solve the task/problem at hand provided by the user.
+
+You can use Python code freely, including the following available functions:
+
+<|functions_schema|>
+{{functions_schema}}
+<|end_functions_schema|>
+
+The following dangerous builtins are restricted for security:
+- exec
+- eval
+- execfile
+- compile
+- importlib
+- input
+- exit
+
+Think step by step and provide your reasoning, outside of function calls.
+You can write Python code and use the available functions. Provide all your Python code in a SINGLE markdown code block.
+
+DO NOT use print() statements AT ALL. Avoid mutating variables whenever possible.
+""".strip()
+
+# Example function schema (define the functions available to the model)
+FUNCTIONS_SCHEMA = """
+def check_availability(day: str, start_time: str, end_time: str) -> bool:
+    """
+    Checks if a given time slot is available.
+
+    :param day: The date in "YYYY-MM-DD" format.
+    :param start_time: The start time of the desired slot (HH:MM format, 24-hour).
+    :param end_time: The end time of the desired slot (HH:MM format, 24-hour).
+    :return: True if the slot is available, otherwise False.
+    """
+    pass
+"""
+
+# Format system prompt
+system_prompt = SYSTEM_PROMPT.replace("{{functions_schema}}", FUNCTIONS_SCHEMA)
+
+# Example user query
+USER_QUERY = "Check if I'm available for an hour long meditation at tomorrow noon."
+
+# Format messages for the model
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": USER_QUERY},
+]
+
+# Prepare input for the model
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Generate response
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+
+# Extract new generated tokens
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+# Decode response
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+````
+
+## Evaluation & Performance
+
+We evaluate the model on the **Dria-Pythonic-Agent-Benchmark ([DPAB](https://github.com/firstbatchxyz/function-calling-eval)):** The benchmark we curated with a synthetic data generation +model-based validation + filtering and manual selection to evaluate LLMs on their Pythonic function calling ability, spanning multiple scenarios and tasks. See [blog](https://huggingface.co/blog/andthattoo/dpab-a) for more information.
+
+Below are the DPAB results: 
+
+Current benchmark results for various models **(strict)**:
+
+| Model Name                      | Pythonic | JSON |
+|---------------------------------|----------|------|
+| **Closed Models**               |          |      |
+| Claude 3.5 Sonnet              | 87       | 45   |
+| gpt-4o-2024-11-20              | 60       | 30   |
+| **Open Models**                 |          |      |
+| **> 100B Parameters**           |          |      |
+| DeepSeek V3 (685B)             | 63       | 33   |
+| MiniMax-01                     | 62       | 40   |
+| Llama-3.1-405B-Instruct        | 60       | 38   |
+| **> 30B Parameters**            |          |      |
+| Qwen-2.5-Coder-32b-Instruct    | 68       | 32   |
+| Qwen-2.5-72b-instruct          | 65       | 39   |
+| Llama-3.3-70b-Instruct         | 59       | 40   |
+| QwQ-32b-Preview                | 47       | 21   |
+| **< 20B Parameters**           |          |      |
+| Phi-4 (14B)                    | 55       | 35   |
+| Qwen2.5-Coder-7B-Instruct      | 44       | 39   |
+| Qwen-2.5-7B-Instruct           | 47       | 34   |
+| **Tiny-Agent-a-3B**               | **72**       | 34   |
+| Qwen2.5-Coder-3B-Instruct      | 26       | 37   |
+| **Tiny-Agent-a-1.5B**               | **73**       | 30   |
+
+#### Citation
+
+```
+@misc{Dria-Agent-a,
+      url={https://huggingface.co/blog/andthattoo/dria-agent-a},
+      title={Dria-Agent-a},
+      author={"andthattoo", "Atakan Tekparmak"}
+}
+```

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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 32768,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}