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: 6,860 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 | """
Code Runner Tool - Run code trong nhiều ngôn ngữ.
Author: Hieu Louis (2026)
Hỗ trợ:
- python : python3 <file>
- javascript : node <file>
- go : go run <file>
- rust : rustc -o <bin> <file> && <bin>
- c : gcc -o <bin> <file> && <bin>
- cpp : g++ -o <bin> <file> && <bin>
Subprocess có timeout=context.timeout, capture stdout+stderr.
DANGEROUS (executes arbitrary code), requires_confirmation.
"""
from __future__ import annotations
import os
import shlex
import shutil
import subprocess
import tempfile
from typing import Any, Dict, List, Optional
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
# Map language → interpreter/compiler command, file extension, mode
LANGUAGE_RUNNERS: Dict[str, Dict[str, str]] = {
"python": {"cmd": "python3", "ext": ".py", "mode": "interpret"},
"javascript": {"cmd": "node", "ext": ".js", "mode": "interpret"},
"go": {"cmd": "go", "ext": ".go", "mode": "interpret"}, # go run
"rust": {"cmd": "rustc", "ext": ".rs", "mode": "compile_run"},
"c": {"cmd": "gcc", "ext": ".c", "mode": "compile_run"},
"cpp": {"cmd": "g++", "ext": ".cpp", "mode": "compile_run"},
}
class CodeRunnerTool(Tool):
"""Run code Python/JS/Go/Rust/C/C++ trong subprocess sandbox."""
category = ToolCategory.EXEC
safety = ToolSafety.DANGEROUS
requires_confirmation = True
@property
def name(self) -> str:
return "code_runner"
@property
def description(self) -> str:
return (
"Run code trong nhiều ngôn ngữ (Python, JavaScript/node, Go, Rust, C, C++). "
"Capture stdout+stderr. Có timeout. Subprocess sandbox."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"code": {"type": "string", "description": "Source code để chạy"},
"language": {
"type": "string",
"enum": sorted(LANGUAGE_RUNNERS.keys()),
"description": "Ngôn ngữ của code",
},
"stdin": {"type": "string", "description": "Stdin input (optional)"},
},
"required": ["code", "language"],
}
def validate_args(self, args: Dict[str, Any]) -> Optional[str]:
if not args.get("code"):
return "Missing required arg: code"
lang = args.get("language")
if not lang:
return "Missing required arg: language"
if lang not in LANGUAGE_RUNNERS:
return f"Unsupported language: {lang}. Chọn: {sorted(LANGUAGE_RUNNERS.keys())}"
return None
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
code: str = args["code"]
lang: str = args["language"]
stdin_data: Optional[str] = args.get("stdin")
if context.dry_run:
return ToolResult(
success=True,
output=f"[dry-run] Sẽ chạy {lang} code ({len(code)} bytes)",
metadata={"language": lang, "dry_run": True, "code_length": len(code)},
)
spec = LANGUAGE_RUNNERS[lang]
cmd = spec["cmd"]
if not shutil.which(cmd):
return ToolResult(
success=False,
error=(
f"Interpreter/Compiler '{cmd}' không tìm thấy trong PATH. "
f"Cài đặt để chạy {lang}."
),
return_code=127,
metadata={"language": lang, "missing": cmd},
)
# Tạo temp dir + temp source file
tmpdir = tempfile.mkdtemp(prefix="nexus_runner_")
src_path = os.path.join(tmpdir, f"main{spec['ext']}")
try:
with open(src_path, "w", encoding="utf-8") as f:
f.write(code)
except Exception as e:
return ToolResult(success=False, error=f"Write temp file lỗi: {e}", return_code=1)
# Build command
if spec["mode"] == "interpret":
if lang == "go":
full_cmd: List[str] = [cmd, "run", src_path]
else:
full_cmd = [cmd, src_path]
cwd = tmpdir
else: # compile_run
binary = os.path.join(tmpdir, "main_bin")
compile_cmd = [cmd, "-o", binary, src_path]
# Step 1: compile
try:
cp_compile = subprocess.run(
compile_cmd,
capture_output=True,
text=True,
timeout=context.timeout,
cwd=tmpdir,
env={**os.environ, **context.env},
)
except subprocess.TimeoutExpired:
return ToolResult(
success=False,
error=f"Compile timeout ({context.timeout}s)",
return_code=124,
metadata={"language": lang, "phase": "compile"},
)
except FileNotFoundError:
return ToolResult(success=False, error=f"{cmd} not found", return_code=127)
if cp_compile.returncode != 0:
return ToolResult(
success=False,
output=cp_compile.stdout,
error=cp_compile.stderr,
return_code=cp_compile.returncode,
metadata={"phase": "compile", "language": lang, "command": " ".join(compile_cmd)},
)
full_cmd = [binary]
cwd = tmpdir
# Step 2: run
try:
cp = subprocess.run(
full_cmd,
input=stdin_data,
capture_output=True,
text=True,
timeout=context.timeout,
cwd=cwd,
env={**os.environ, **context.env},
)
except subprocess.TimeoutExpired:
return ToolResult(
success=False,
error=f"Runtime timeout ({context.timeout}s)",
return_code=124,
metadata={"language": lang, "phase": "run", "command": " ".join(full_cmd)},
)
except FileNotFoundError:
return ToolResult(success=False, error=f"{full_cmd[0]} not found", return_code=127)
return ToolResult(
success=(cp.returncode == 0),
output=cp.stdout,
error=cp.stderr if cp.stderr else None,
return_code=cp.returncode,
metadata={
"language": lang,
"command": " ".join(full_cmd),
"phase": "run",
"timeout": context.timeout,
},
)
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