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---
library_name: transformers
tags:
- trl
- sft
base_model: Qwen/Qwen2.5-3B-Instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
This models fine-tuning for function calling task, and communicate
Training time: more than 1 hour
Steps: 200 (16)
### Usage:
```
import json
from typing import Any, Dict, List
from transformers import AutoModelForCausalLM, AutoTokenizer
models = [
'beyoru/Neeru'
]
model_name = models[-1]
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
SYSTEM_PROMPT = """You are a helpful assistant with access to the following functions. Use them if required
You may call one or more functions to assist with the user query.
Before calling a function, ensure that all required parameters are provided. If any required parameter is missing, ask the user for the missing information before proceeding.
You are provided with function:
{tools}
For each function call, return a json object with function name and arguments:
[{{"name": "<function-name>", "arguments": <args-json-object>}}]
"""
TOOL_PROMPT = "{tool_text}"
get_weather_api = {
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "str",
"description": "The city and state",
},
"unit": {
"type": "str",
"enum": ["celsius", "fahrenheit"],
"description": "The unit of temperature to return",
},
},
"required": ["location"],
},
},
}
get_search_api = {
"type": "function",
"function": {
"name": "get_search",
"description": "Search the web for a query",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query"
}
},
"required": ["query"]
}
}
}
TOOL_PROMPT = "{tool_text}"
get_weather_api = {
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "The city and state"},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"], "description": "The unit of temperature to return"},
},
"required": ["location"],
},
},
}
get_search_api = {
"type": "function",
"function": {
"name": "get_search",
"description": "Search the web for a query",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "The search query"}
},
"required": ["query"],
},
},
}
get_warehouse_api = {
"type": "function",
"function": {
"name": "get_warehouse_info",
"description": "Get warehouse information based on multiple parameters",
"parameters": {
"type": "object",
"properties": {
"warehouse_id": {
"type": "string",
"description": "Unique identifier of the warehouse"
},
"location": {
"type": "string",
"description": "Location of the warehouse"
},
"status": {
"type": "string",
"enum": ["active", "inactive", "under maintenance"],
"description": "Operational status of the warehouse"
},
"capacity": {
"type": "integer",
"description": "Total storage capacity of the warehouse"
},
"current_stock": {
"type": "integer",
"description": "Current stock available in the warehouse"
},
"manager": {
"type": "string",
"description": "Name of the warehouse manager"
},
"contact": {
"type": "string",
"description": "Contact details of the warehouse"
},
"operating_hours": {
"type": "string",
"description": "Operating hours of the warehouse"
}
},
"required": ["warehouse_id", "location", "status"],
},
},
}
TOOLS = [get_search_api, get_weather_api, get_warehouse_api]
def convert_tools(tools: List[Dict[str, Any]]) -> str:
return json.dumps(tools, indent=2)
def format_prompt(tools: List[Dict[str, Any]]) -> str:
tool_text = convert_tools(tools)
return f"{SYSTEM_PROMPT.format(tools=tool_text)}"
system_prompt = format_prompt(TOOLS)
# print(system_prompt)
"""
Simple loop for conversations
"""
while True:
user_input = input("User: ")
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_input},
]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
) # You can added more history conversations here
outputs = model.generate(
inputs,
max_new_tokens=2048,
top_p=0.95,
temperature=0.7,
) # Suggest parameters
response_text = tokenizer.decode(outputs[0][len(inputs[0]) :], skip_special_tokens=True)
print("Assistant:", response_text)
```
### Config
```
all_linear layers
r = 64
alpha = 512
```
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