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: 4,519 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 | """
Code Dependency Tool - Trích xuất import graph từ Python file/thư mục.
Author: Hieu Louis (2026)
Trả về danh sách (file, imported_module) pairs. Hỗ trợ recursive scan
thư mục. Module string chứa dotted path đầy đủ (vd `os.path`, `.foo.bar`
cho relative imports).
"""
from __future__ import annotations
import ast
import json
import os
from typing import Any, Dict, List, Optional
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
def _extract_imports(source: str) -> List[str]:
"""Trích danh sách module imported từ source code (dùng ast)."""
try:
tree = ast.parse(source)
except SyntaxError:
return []
modules: List[str] = []
for n in ast.walk(tree):
if isinstance(n, ast.Import):
for alias in n.names:
modules.append(alias.name)
elif isinstance(n, ast.ImportFrom):
mod = "." * (n.level or 0) + (n.module or "")
if mod:
modules.append(mod)
return modules
def _walk_python_files(path: str, recursive: bool) -> List[str]:
"""Tìm tất cả file .py trong path (file hoặc dir)."""
if os.path.isfile(path):
return [path]
files: List[str] = []
if not os.path.isdir(path):
return files
if recursive:
for root, _dirs, names in os.walk(path):
for name in sorted(names):
if name.endswith(".py"):
files.append(os.path.join(root, name))
else:
for name in sorted(os.listdir(path)):
full = os.path.join(path, name)
if os.path.isfile(full) and name.endswith(".py"):
files.append(full)
return files
class CodeDependencyTool(Tool):
"""Extract import graph từ Python file/thư mục."""
category = ToolCategory.CODE
safety = ToolSafety.SAFE # read-only analysis
@property
def name(self) -> str:
return "code_dependency"
@property
def description(self) -> str:
return (
"Extract import graph từ Python file/dir. Trả về list (file, imported_module). "
"Hỗ trợ recursive scan và top-level package ranking."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File hoặc thư mục Python"},
"recursive": {
"type": "boolean",
"description": "Scan đệ quy nếu là thư mục (default true)",
},
},
"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"]
recursive: bool = bool(args.get("recursive", True))
if not os.path.exists(path):
return ToolResult(
success=False,
error=f"Path không tồn tại: {path}",
return_code=1,
)
files = _walk_python_files(path, recursive)
if not files:
return ToolResult(
success=True,
output="[]",
metadata={"path": path, "file_count": 0, "edge_count": 0},
)
edges: List[Dict[str, str]] = []
for fp in files:
try:
with open(fp, "r", encoding="utf-8") as f:
src = f.read()
except Exception:
continue
for mod in _extract_imports(src):
edges.append({"file": fp, "module": mod})
# Top-level package ranking
top_packages: Dict[str, int] = {}
for e in edges:
top = e["module"].lstrip(".").split(".")[0]
if top:
top_packages[top] = top_packages.get(top, 0) + 1
ranked = dict(sorted(top_packages.items(), key=lambda kv: -kv[1])[:20])
return ToolResult(
success=True,
output=json.dumps(edges, indent=2, ensure_ascii=False),
metadata={
"path": path,
"recursive": recursive,
"file_count": len(files),
"edge_count": len(edges),
"top_packages": ranked,
},
)
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