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: 6,990 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 Formatter Advanced Tool - Format code trong nhiều ngôn ngữ.
Author: Hieu Louis (2026)
- Python : stdlib ast + simple normalizer (rstrip, blank-line cleanup, trailing newline)
- JSON : stdlib json.dumps(indent=2)
- JS : lazy import `jsbeautifier`
- SQL : lazy import `sqlparse`
- HTML : lazy import `beautifulsoup4` (bs4)
- CSS : lazy import `cssbeautifier`
In-place edit nếu cung cấp `path`. MODERATE safety, requires_confirmation.
"""
from __future__ import annotations
import ast
import json
from typing import Any, Dict, Optional
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
SUPPORTED_LANGS = {"python", "javascript", "sql", "html", "css", "json"}
def _format_python(source: str) -> str:
"""Format Python cơ bản: validate syntax, rstrip lines, cleanup blank runs."""
ast.parse(source) # raise SyntaxError if invalid
lines = source.splitlines()
out: list[str] = []
blank_run = 0
for ln in lines:
stripped = ln.rstrip()
if stripped == "":
blank_run += 1
# Tối đa 2 blank lines liên tiếp
if blank_run <= 2:
out.append("")
continue
blank_run = 0
out.append(stripped)
formatted = "\n".join(out).rstrip() + "\n"
return formatted
def _format_json(source: str) -> str:
data = json.loads(source)
return json.dumps(data, indent=2, ensure_ascii=False) + "\n"
class CodeFormatterAdvancedTool(Tool):
"""Format code multi-language: Python/JSON (stdlib) + JS/SQL/HTML/CSS (lazy deps)."""
category = ToolCategory.CODE
safety = ToolSafety.MODERATE # có thể sửa file in-place
requires_confirmation = True
@property
def name(self) -> str:
return "code_formatter_advanced"
@property
def description(self) -> str:
return (
"Format code multi-language: Python (ast), JSON (stdlib), "
"JavaScript (jsbeautifier), SQL (sqlparse), HTML (bs4), CSS (cssbeautifier). "
"In-place edit nếu cung cấp path."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File để format (in-place)"},
"code": {"type": "string", "description": "Code để format (nếu không dùng path)"},
"language": {
"type": "string",
"enum": sorted(SUPPORTED_LANGS),
"description": "Ngôn ngữ (mặc định python)",
},
},
"anyOf": [{"required": ["path"]}, {"required": ["code"]}],
}
def validate_args(self, args: Dict[str, Any]) -> Optional[str]:
if not args.get("path") and not args.get("code"):
return "Missing required arg: path hoặc code"
lang = args.get("language", "python")
if lang not in SUPPORTED_LANGS:
return f"Unsupported language: {lang}. Chọn: {sorted(SUPPORTED_LANGS)}"
return None
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
lang = args.get("language", "python")
path = args.get("path")
code: Optional[str] = args.get("code")
if path:
if context.dry_run:
return ToolResult(
success=True,
output=f"[dry-run] Sẽ format {path} ({lang})",
metadata={"path": path, "language": lang, "dry_run": True},
)
try:
with open(path, "r", encoding="utf-8") as f:
code = f.read()
except Exception as e:
return ToolResult(success=False, error=f"Đọc file lỗi: {e}", return_code=1)
assert code is not None # validated above
try:
if lang == "python":
formatted = _format_python(code)
elif lang == "json":
formatted = _format_json(code)
elif lang == "javascript":
try:
import jsbeautifier # type: ignore
except ImportError as e:
return ToolResult(
success=False,
error=f"jsbeautifier not installed: {e}. Cài: pip install jsbeautifier",
return_code=127,
)
formatted = jsbeautifier.beautify(code)
elif lang == "sql":
try:
import sqlparse # type: ignore
except ImportError as e:
return ToolResult(
success=False,
error=f"sqlparse not installed: {e}. Cài: pip install sqlparse",
return_code=127,
)
formatted = sqlparse.format(
code, reindent=True, keyword_case="upper", indent_width=2,
)
elif lang == "html":
try:
from bs4 import BeautifulSoup # type: ignore
except ImportError as e:
return ToolResult(
success=False,
error=f"beautifulsoup4 not installed: {e}. Cài: pip install beautifulsoup4",
return_code=127,
)
soup = BeautifulSoup(code, "html.parser")
formatted = soup.prettify()
elif lang == "css":
try:
import cssbeautifier # type: ignore
except ImportError as e:
return ToolResult(
success=False,
error=f"cssbeautifier not installed: {e}. Cài: pip install cssbeautifier",
return_code=127,
)
formatted = cssbeautifier.beautify(code)
else:
return ToolResult(success=False, error=f"Unsupported language: {lang}", return_code=1)
except Exception as e:
return ToolResult(success=False, error=f"{type(e).__name__}: {e}", return_code=1)
# Write back if path provided
artifacts = []
if path:
try:
with open(path, "w", encoding="utf-8") as f:
f.write(formatted)
artifacts.append(path)
except Exception as e:
return ToolResult(success=False, error=f"Write file lỗi: {e}", return_code=1)
return ToolResult(
success=True,
output=formatted,
artifacts=artifacts,
metadata={
"language": lang,
"path": path,
"input_length": len(code),
"output_length": len(formatted),
"in_place": bool(path),
},
)
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