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: 6,374 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | """
Code Smells Detector - Phát hiện code smells trong Python file.
Author: Hieu Louis (2026)
Phát hiện các smells:
- long_function : quá nhiều statements/lines
- too_many_params : > 5 parameters
- deep_nesting : nesting > 4 levels
- long_class : class có quá nhiều methods
- duplicate_string_literal : string literal xuất hiện ≥ 3 lần (len ≥ 5)
Dùng `ast` để walk tree. Read-only (SAFE).
"""
from __future__ import annotations
import ast
import json
from collections import Counter
from typing import Any, Dict, List, Optional
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
# Ngưỡng smell // smell thresholds (tunable)
LONG_FUNCTION_STMTS = 50
LONG_FUNCTION_LINES = 50
TOO_MANY_PARAMS = 5
DEEP_NESTING = 4
LONG_CLASS_METHODS = 20
DUP_LIT_MIN_COUNT = 3
DUP_LIT_MIN_LEN = 5
DUP_LIT_MAX_REPORT = 50
class _SmellVisitor(ast.NodeVisitor):
"""Visitor quét AST để phát hiện code smells."""
def __init__(self) -> None:
self.smells: List[Dict[str, Any]] = []
self._str_literals: List[str] = []
def _record(self, kind: str, name: str, line: int, detail: Dict[str, Any]) -> None:
self.smells.append({"kind": kind, "name": name, "line": line, **detail})
def _max_nesting(self, node: ast.AST, depth: int = 0) -> int:
"""Tính độ sâu nesting tối đa trong block."""
max_d = depth
for child in ast.iter_child_nodes(node):
if isinstance(child, (ast.If, ast.For, ast.While, ast.With, ast.Try, ast.ExceptHandler)):
d = self._max_nesting(child, depth + 1)
else:
d = self._max_nesting(child, depth)
if d > max_d:
max_d = d
return max_d
def visit_FunctionDef(self, node: ast.FunctionDef) -> None:
self._check_function(node)
self.generic_visit(node)
def visit_AsyncFunctionDef(self, node: ast.AsyncFunctionDef) -> None:
self._check_function(node)
self.generic_visit(node)
def _check_function(self, node: ast.FunctionDef) -> None:
name = node.name
line = node.lineno
end_line = getattr(node, "end_lineno", line)
# Statement count (approximate)
n_stmts = sum(1 for _ in ast.walk(node) if isinstance(_, ast.stmt))
n_lines = max(1, end_line - line + 1)
if n_stmts > LONG_FUNCTION_STMTS or n_lines > LONG_FUNCTION_LINES:
self._record("long_function", name, line, {"statements": n_stmts, "lines": n_lines})
# Parameters count
n_args = (
len(node.args.args)
+ len(node.args.kwonlyargs)
+ len(node.args.posonlyargs)
)
if node.args.vararg:
n_args += 1
if node.args.kwarg:
n_args += 1
if n_args > TOO_MANY_PARAMS:
self._record("too_many_params", name, line, {"params": n_args})
# Nesting depth
nesting = self._max_nesting(node)
if nesting > DEEP_NESTING:
self._record("deep_nesting", name, line, {"depth": nesting})
def visit_ClassDef(self, node: ast.ClassDef) -> None:
n_methods = sum(
1 for m in node.body
if isinstance(m, (ast.FunctionDef, ast.AsyncFunctionDef))
)
if n_methods > LONG_CLASS_METHODS:
self._record("long_class", node.name, node.lineno, {"methods": n_methods})
self.generic_visit(node)
def visit_Constant(self, node: ast.Constant) -> None:
# ast.Str deprecated in 3.8+; use ast.Constant
if isinstance(node.value, str) and len(node.value) >= DUP_LIT_MIN_LEN:
self._str_literals.append(node.value)
self.generic_visit(node)
class CodeSmellsTool(Tool):
"""Phát hiện code smells (long function, too many params, deep nesting, ...)."""
category = ToolCategory.CODE
safety = ToolSafety.SAFE # read-only analysis
@property
def name(self) -> str:
return "code_smells"
@property
def description(self) -> str:
return (
"Phát hiện code smells trong Python file: long function, too many params, "
"deep nesting, long class, duplicate string literals."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File Python (.py) để phân tích"},
},
"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"]
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,
)
visitor = _SmellVisitor()
visitor.visit(tree)
# Duplicate string literals analysis
dup_lits = [
{"literal": lit, "count": cnt}
for lit, cnt in Counter(visitor._str_literals).most_common()
if cnt >= DUP_LIT_MIN_COUNT
][:DUP_LIT_MAX_REPORT]
for d in dup_lits:
visitor.smells.append({
"kind": "duplicate_string_literal",
"name": "<literal>",
"line": 0,
**d,
})
by_kind: Dict[str, int] = {}
for s in visitor.smells:
by_kind[s["kind"]] = by_kind.get(s["kind"], 0) + 1
return ToolResult(
success=True,
output=json.dumps(
{"smells": visitor.smells, "summary": by_kind},
indent=2, ensure_ascii=False,
),
metadata={
"path": path,
"smell_count": len(visitor.smells),
"by_kind": by_kind,
},
)
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