Text Generation
Safetensors
GGUF
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qwen3
function-calling
tool-calling
codex
local-llm
4gb-vram
llama-cpp
code-assistant
api-tools
openai-alternative
qwen
instruct
conversational
custom_code
8-bit precision
Instructions to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex", filename="Qwen3-4B-Function-Calling-Pro.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex # Run inference directly in the terminal: llama cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex # Run inference directly in the terminal: llama cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex # Run inference directly in the terminal: ./llama-cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex # Run inference directly in the terminal: ./build/bin/llama-cli -hf Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
Use Docker
docker model run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
- LM Studio
- Jan
- vLLM
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
- Ollama
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with Ollama:
ollama run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
- Unsloth Studio
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with Docker Model Runner:
docker model run hf.co/Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
- Lemonade
How to use Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Manojb/Qwen3-4b-toolcall-gguf-llamacpp-codex
Run and chat with the model
lemonade run user.Qwen3-4b-toolcall-gguf-llamacpp-codex-{{QUANT_TAG}}List all available models
lemonade list
File size: 8,269 Bytes
812540e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | #!/usr/bin/env python3
"""
Codex Integration Example for Qwen3-4B Tool Calling
This script demonstrates how to use the model with Codex
"""
import requests
import json
import time
from typing import List, Dict, Any, Optional
class CodexClient:
"""Client for interacting with Codex-compatible server"""
def __init__(self, base_url: str = "http://localhost:8000"):
self.base_url = base_url
self.session = requests.Session()
self.model_name = "Qwen3-4B-Function-Calling-Pro"
def chat_completion(self, messages: List[Dict[str, str]], tools: Optional[List[Dict]] = None,
temperature: float = 0.7, max_tokens: int = 512) -> Dict[str, Any]:
"""Send chat completion request to Codex server"""
payload = {
"model": self.model_name,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens,
"stop": ["<|im_end|>", "<|im_start|>", "<tool_call>", "</tool_call>"]
}
if tools:
payload["tools"] = tools
try:
response = self.session.post(
f"{self.base_url}/v1/chat/completions",
json=payload,
headers={"Content-Type": "application/json"},
timeout=60
)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
print(f"Error making request: {e}")
return {"error": str(e)}
def extract_tool_calls(self, response: Dict[str, Any]) -> List[Dict[str, Any]]:
"""Extract tool calls from Codex response"""
tool_calls = []
if "choices" in response and len(response["choices"]) > 0:
message = response["choices"][0]["message"]
if "tool_calls" in message:
tool_calls = message["tool_calls"]
return tool_calls
def is_server_running(self) -> bool:
"""Check if the Codex server is running"""
try:
response = self.session.get(f"{self.base_url}/health", timeout=5)
return response.status_code == 200
except:
return False
def demo_codex_integration():
"""Demonstrate Codex integration"""
print("π Codex Integration Demo")
print("=" * 50)
# Initialize client
codex = CodexClient()
# Check if server is running
if not codex.is_server_running():
print("β Codex server is not running!")
print("π‘ Start the server with: ./run_model.sh server")
return
print("β
Connected to Codex server")
# Define tools for Codex
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current weather for a location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City name or location"
}
},
"required": ["location"]
}
}
},
{
"type": "function",
"function": {
"name": "search_hotels",
"description": "Search for hotels in a city",
"parameters": {
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City name"
},
"check_in": {
"type": "string",
"description": "Check-in date (YYYY-MM-DD)"
},
"check_out": {
"type": "string",
"description": "Check-out date (YYYY-MM-DD)"
}
},
"required": ["city"]
}
}
},
{
"type": "function",
"function": {
"name": "calculate",
"description": "Perform mathematical calculations",
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "Mathematical expression to evaluate"
}
},
"required": ["expression"]
}
}
}
]
# Test cases
test_cases = [
"What's the weather like in London?",
"Find me a hotel in Paris for next week",
"Calculate 25 + 17",
"Get weather for New York and find hotels there"
]
for i, message in enumerate(test_cases, 1):
print(f"\nπ Test {i}: {message}")
print("-" * 40)
# Send request
messages = [{"role": "user", "content": message}]
response = codex.chat_completion(messages, tools=tools)
if "error" in response:
print(f"β Error: {response['error']}")
continue
# Extract response
if "choices" in response and len(response["choices"]) > 0:
assistant_message = response["choices"][0]["message"]
print(f"Response: {assistant_message.get('content', 'No content')}")
# Check for tool calls
tool_calls = codex.extract_tool_calls(response)
if tool_calls:
print(f"\nπ§ Tool Calls ({len(tool_calls)}):")
for j, tool_call in enumerate(tool_calls, 1):
print(f" {j}. {tool_call['function']['name']}")
print(f" Arguments: {tool_call['function'].get('arguments', {})}")
else:
print("\nβ No tool calls detected")
else:
print("β No response received")
def interactive_codex_chat():
"""Interactive chat with Codex server"""
print("π¬ Interactive Codex Chat")
print("=" * 50)
codex = CodexClient()
if not codex.is_server_running():
print("β Codex server is not running!")
print("π‘ Start the server with: ./run_model.sh server")
return
print("β
Connected to Codex server")
print("Type 'quit' to exit")
print("-" * 50)
while True:
try:
user_input = input("\nYou: ").strip()
if user_input.lower() in ['quit', 'exit', 'q']:
break
if not user_input:
continue
# Send request
messages = [{"role": "user", "content": user_input}]
response = codex.chat_completion(messages)
if "error" in response:
print(f"β Error: {response['error']}")
continue
# Display response
if "choices" in response and len(response["choices"]) > 0:
assistant_message = response["choices"][0]["message"]
print(f"\nAssistant: {assistant_message.get('content', 'No content')}")
# Check for tool calls
tool_calls = codex.extract_tool_calls(response)
if tool_calls:
print(f"\nπ§ Tool Calls ({len(tool_calls)}):")
for i, tool_call in enumerate(tool_calls, 1):
print(f" {i}. {tool_call['function']['name']}")
print(f" Arguments: {tool_call['function'].get('arguments', {})}")
except KeyboardInterrupt:
print("\n\nGoodbye! π")
break
except Exception as e:
print(f"Error: {e}")
if __name__ == "__main__":
import sys
if len(sys.argv) > 1 and sys.argv[1] == "demo":
demo_codex_integration()
else:
interactive_codex_chat()
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