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: 6,572 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 | """
Gcloud CLI Tool - Wrap `gcloud` CLI for GCE, GCS, Cloud Run, GKE.
Author: Hieu Louis (2026)
"""
from __future__ import annotations
import os
import subprocess
from typing import Dict, Any, List, Optional
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
# Service được hỗ trợ // Supported GCP services
GCLOUD_SERVICES = {
"compute", "storage", "run", "container", "functions",
"app", "sql", "iam", "projects", "auth", "config",
"pubsub", "bigquery", "logging", "monitoring", "secrets",
}
# Read-only ops
READONLY_OPS = {"list", "describe", "get-credentials", "print-config", "view"}
# Write ops
WRITE_OPS = {"create", "delete", "update", "deploy", "set", "add", "remove", "start", "stop", "run", "call"}
class GcloudCliTool(Tool):
"""Wrap `gcloud` CLI cho GCE/GCS/Cloud Run/GKE/Functions."""
category = ToolCategory.CLOUD
safety = ToolSafety.DANGEROUS
requires_confirmation = True
@property
def name(self) -> str:
return "gcloud_cli"
@property
def description(self) -> str:
return (
"Wrap gcloud CLI: compute (instances/disks), storage (ls/cp/rm), "
"run (deploy/services), container (clusters), functions, app, sql. "
"Hỗ trợ --project, --region, --quiet, dry_run."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"service": {
"type": "string",
"enum": sorted(GCLOUD_SERVICES),
},
"operation": {"type": "string", "description": "CLI operation group e.g. instances, services, ls, deploy"},
"subcommand": {"type": "string", "description": "Specific subcommand e.g. list, describe, create"},
"args": {
"type": "array",
"items": {"type": "string"},
"description": "Positional args",
},
"options": {
"type": "object",
"description": "--key value flags",
},
"project": {"type": "string", "description": "--project"},
"region": {"type": "string", "description": "--region"},
"zone": {"type": "string", "description": "--zone"},
"quiet": {"type": "boolean", "default": False, "description": "--quiet (no prompts)"},
"format": {
"type": "string",
"enum": ["json", "yaml", "text", "table", "csv"],
"default": "json",
},
},
"required": ["service", "operation"],
}
def validate_args(self, args: Dict[str, Any]) -> Optional[str]:
svc = args.get("service")
if not svc:
return "Missing required arg: service"
if not args.get("operation"):
return "Missing required arg: operation"
if svc not in GCLOUD_SERVICES:
return f"Unsupported service: {svc}. Supported: {sorted(GCLOUD_SERVICES)}"
return None
def _is_write_op(self, sub: Optional[str]) -> bool:
if not sub:
return False
sub_lower = sub.lower()
for w in WRITE_OPS:
if w in sub_lower:
return True
return False
def _build_command(self, args: Dict[str, Any]) -> List[str]:
cmd: List[str] = ["gcloud", args["service"], args["operation"]]
if args.get("subcommand"):
cmd.append(args["subcommand"])
for a in (args.get("args") or []):
cmd.append(str(a))
if args.get("project"):
cmd += ["--project", args["project"]]
if args.get("region"):
cmd += ["--region", args["region"]]
if args.get("zone"):
cmd += ["--zone", args["zone"]]
if args.get("quiet"):
cmd.append("--quiet")
cmd += ["--format", args.get("format", "json")]
for k, v in (args.get("options") or {}).items():
if isinstance(v, bool):
if v:
cmd.append(f"--{k}")
elif isinstance(v, (list, tuple)):
for item in v:
cmd += [f"--{k}", str(item)]
else:
cmd += [f"--{k}", str(v)]
return cmd
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
cmd = self._build_command(args)
is_write = self._is_write_op(args.get("subcommand")) or args["operation"] in WRITE_OPS
# Dry-run simulation
if context.dry_run and is_write:
return ToolResult(
success=True,
output=f"[dry-run] Would execute: {' '.join(cmd)}",
metadata={
"dry_run": True,
"command": cmd,
"service": args["service"],
"operation": args["operation"],
},
)
env = dict(os.environ)
env.update(context.env)
try:
result = subprocess.run(
cmd,
capture_output=True,
text=True,
env=env,
timeout=context.timeout,
check=False,
)
return ToolResult(
success=(result.returncode == 0),
output=result.stdout,
error=result.stderr or None,
return_code=result.returncode,
metadata={
"service": args["service"],
"operation": args["operation"],
"subcommand": args.get("subcommand"),
"command": cmd,
"project": args.get("project"),
"region": args.get("region"),
"zone": args.get("zone"),
"dry_run": False,
},
)
except FileNotFoundError:
return ToolResult(
success=False,
error="gcloud CLI not found. Cài đặt Google Cloud SDK.",
return_code=127,
)
except subprocess.TimeoutExpired:
return ToolResult(
success=False,
error=f"gcloud command timed out after {context.timeout}s",
return_code=124,
)
except Exception as e:
return ToolResult(success=False, error=str(e), return_code=1)
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