Text Generation
Transformers
Safetensors
qwen2
coder
code
agent
conversational
text-generation-inference
Instructions to use AdminReal/NexusCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AdminReal/NexusCoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AdminReal/NexusCoder") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AdminReal/NexusCoder") model = AutoModelForCausalLM.from_pretrained("AdminReal/NexusCoder", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AdminReal/NexusCoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AdminReal/NexusCoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AdminReal/NexusCoder
- SGLang
How to use AdminReal/NexusCoder with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "AdminReal/NexusCoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "AdminReal/NexusCoder" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AdminReal/NexusCoder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AdminReal/NexusCoder with Docker Model Runner:
docker model run hf.co/AdminReal/NexusCoder
File size: 5,406 Bytes
eca5751 | 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 | """
Code Complexity Tool - Tính cyclomatic complexity cho Python functions.
Author: Hieu Louis (2026)
Cyclomatic complexity = 1 + số decision points:
if/elif (each elif), for, while, except, with, assert,
boolean and/or (n values → n-1), ternary if-exp, comprehension clauses.
Reference: McCabe (1976) — complexity ≥ 10 cần refactor.
"""
from __future__ import annotations
import ast
import json
from typing import Any, Dict, List, Optional
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
class _ComplexityVisitor(ast.NodeVisitor):
"""Đếm decision points trong một function body để tính cyclomatic complexity."""
def __init__(self) -> None:
self.complexity: int = 1 # baseline path
def visit_If(self, n: ast.If) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_For(self, n: ast.For) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_AsyncFor(self, n: ast.AsyncFor) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_While(self, n: ast.While) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_ExceptHandler(self, n: ast.ExceptHandler) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_With(self, n: ast.With) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_AsyncWith(self, n: ast.AsyncWith) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_Assert(self, n: ast.Assert) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_BoolOp(self, n: ast.BoolOp) -> None:
# `a and b and c` = 2 decision points
self.complexity += max(0, len(n.values) - 1)
self.generic_visit(n)
def visit_IfExp(self, n: ast.IfExp) -> None:
self.complexity += 1
self.generic_visit(n)
def visit_comprehension(self, n: ast.comprehension) -> None:
# mỗi clause +1, mỗi if-condition +1
self.complexity += 1 + len(n.ifs)
self.generic_visit(n)
def _risk(c: int) -> str:
"""Phân loại risk theo complexity (McCabe thresholds)."""
if c <= 5:
return "low"
if c <= 10:
return "moderate"
if c <= 20:
return "high"
return "very_high"
class CodeComplexityTool(Tool):
"""Tính cyclomatic complexity của các function trong Python file."""
category = ToolCategory.CODE
safety = ToolSafety.SAFE # read-only analysis
@property
def name(self) -> str:
return "code_complexity"
@property
def description(self) -> str:
return (
"Tính cyclomatic complexity của Python functions trong file. "
"Complexity = 1 + số decision points (if/for/while/except/and/or...)."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File Python (.py) để phân tích"},
"function": {
"type": "string",
"description": "Tên function cụ thể (optional). Mặc định tính tất cả.",
},
},
"required": ["path"],
}
def validate_args(self, args: Dict[str, Any]) -> Optional[str]:
if not args.get("path"):
return "Missing required arg: path"
return None
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
path: str = args["path"]
target_fn: Optional[str] = args.get("function")
try:
with open(path, "r", encoding="utf-8") as f:
source = f.read()
except Exception as e:
return ToolResult(success=False, error=f"Không đọc được file: {e}", return_code=1)
try:
tree = ast.parse(source)
except SyntaxError as e:
return ToolResult(
success=False,
error=f"SyntaxError line {e.lineno}: {e.msg}",
return_code=1,
)
results: List[Dict[str, Any]] = []
for node in ast.walk(tree):
if not isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
continue
if target_fn and node.name != target_fn:
continue
visitor = _ComplexityVisitor()
visitor.visit(node)
results.append({
"function": node.name,
"line": node.lineno,
"end_line": getattr(node, "end_lineno", node.lineno),
"complexity": visitor.complexity,
"risk": _risk(visitor.complexity),
})
if target_fn and not results:
return ToolResult(
success=False,
error=f"Không tìm thấy function '{target_fn}' trong {path}",
return_code=1,
)
return ToolResult(
success=True,
output=json.dumps(results, indent=2, ensure_ascii=False),
metadata={
"path": path,
"function": target_fn,
"count": len(results),
"max_complexity": max((r["complexity"] for r in results), default=0),
},
)
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