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: 5,407 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 | """
Code Minifier Tool - Minify JS/CSS/HTML code.
Author: Hieu Louis (2026)
Lazy import:
- JavaScript : `jsmin`
- CSS : `cssmin`
- HTML : `htmlmin`
MODERATE safety (in-place edit possible), requires_confirmation.
"""
from __future__ import annotations
from typing import Any, Dict, Optional
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
SUPPORTED_LANGS = {"javascript", "css", "html"}
class CodeMinifierTool(Tool):
"""Minify JavaScript/CSS/HTML code (lazy deps)."""
category = ToolCategory.CODE
safety = ToolSafety.MODERATE # in-place edit
requires_confirmation = True
@property
def name(self) -> str:
return "code_minifier"
@property
def description(self) -> str:
return (
"Minify code JS/CSS/HTML. Sử dụng jsmin (JS), cssmin (CSS), htmlmin (HTML). "
"In-place edit nếu cung cấp path. Trả về reduction %."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File để minify (in-place)"},
"code": {"type": "string", "description": "Code để minify (nếu không dùng path)"},
"language": {
"type": "string",
"enum": sorted(SUPPORTED_LANGS),
"description": "Ngôn ngữ: javascript/css/html",
},
},
"anyOf": [{"required": ["path"]}, {"required": ["code"]}],
"required": ["language"],
}
def validate_args(self, args: Dict[str, Any]) -> Optional[str]:
lang = args.get("language")
if not lang:
return "Missing required arg: language"
if lang not in SUPPORTED_LANGS:
return f"Unsupported language: {lang}. Chọn: {sorted(SUPPORTED_LANGS)}"
if not args.get("path") and not args.get("code"):
return "Missing required arg: path hoặc code"
return None
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
lang: str = args["language"]
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 minify {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
try:
if lang == "javascript":
try:
import jsmin # type: ignore
except ImportError as e:
return ToolResult(
success=False,
error=f"jsmin not installed: {e}. Cài: pip install jsmin",
return_code=127,
)
minified = jsmin.jsmin(code)
elif lang == "css":
try:
import cssmin # type: ignore
except ImportError as e:
return ToolResult(
success=False,
error=f"cssmin not installed: {e}. Cài: pip install cssmin",
return_code=127,
)
minified = cssmin.cssmin(code)
elif lang == "html":
try:
import htmlmin # type: ignore
except ImportError as e:
return ToolResult(
success=False,
error=f"htmlmin not installed: {e}. Cài: pip install htmlmin",
return_code=127,
)
minifier = htmlmin.Minifier(
remove_comments=True,
remove_empty_space=True,
remove_optional_attribute_quotes=False,
)
minified = minifier.minify(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(minified)
artifacts.append(path)
except Exception as e:
return ToolResult(success=False, error=f"Write file lỗi: {e}", return_code=1)
ratio = (len(minified) / max(1, len(code))) * 100.0
return ToolResult(
success=True,
output=minified,
artifacts=artifacts,
metadata={
"language": lang,
"path": path,
"input_length": len(code),
"output_length": len(minified),
"reduction_pct": round(100.0 - ratio, 2),
"in_place": bool(path),
},
)
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