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---
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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library_name: transformers
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---
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## Introduction
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FLock Web3 Agent Model is a specialized LLM designed to address complex queries in the Web3 ecosystem, with a focus on DeFi, blockchain interoperability, on-chain analytics, and etc.. The model excels in function-calling reasoning, enabling it to break down intricate user requests into actionable steps, interact with external APIs, and provide data-driven insights for Web3 applications. It is tailored for users ranging from developers and researchers to investors navigating the decentralized landscape.
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## Requirements
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We advise you to use the latest version of `transformers`.
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## Quickstart
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Given a query and a list of available tools. The model generate function calls using the provided tools to respond the query correctly.
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**Example query and tools format**
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```python
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input_example=
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{
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"query": "Track crosschain message verification, implement timeout recovery procedures.",
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"tools": [
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{"type": "function", "function": {"name": "track_crosschain_message", "description": "Track the status of a crosschain message", "parameters": {"type": "object", "properties": {"message_id": {"type": "string"}}}}},
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{"type": "function", "function": {"name": "schedule_timeout_check", "description": "Schedule a timeout check for a message", "parameters": {"type": "object", "properties": {"message_id": {"type": "string"}, "timeout": {"type": "integer"}}}}}
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]
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}
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```
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**Function calling generation**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import json
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model_name = "flock-io/Flock_Web3_Agent_Model"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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messages = [
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{"role": "system", "content": "You are a helpful assistant with access to the following functions. Use them if required -"
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+ json.dumps(input_example["tools"], ensure_ascii=False)},
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{"role": "user", "content": input_example["query"]}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=3000
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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The output text is in the string format
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```
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[
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{"name": "track_crosschain_message", "arguments": {"message_id": "msg12345"}},
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{"name": "schedule_timeout_check", "arguments": {"message_id": "msg12345", "timeout": "30"}}
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]
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``` |
