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
| """ | |
| 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 | |
| def name(self) -> str: | |
| return "code_complexity" | |
| def description(self) -> str: | |
| return ( | |
| "Tính cyclomatic complexity của Python functions trong file. " | |
| "Complexity = 1 + số decision points (if/for/while/except/and/or...)." | |
| ) | |
| 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), | |
| }, | |
| ) | |