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: 7,397 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | """
Code AST Tool - Parse Python source thành AST bằng stdlib `ast`.
Author: Hieu Louis (2026)
Operations:
- dump_tree : Dump toàn bộ AST tree (ast.dump indent=2)
- list_functions : Liệt kê FunctionDef / AsyncFunctionDef
- list_classes : Liệt kê ClassDef + methods
- list_imports : Liệt kê Import / ImportFrom
- list_calls : Liệt kê Call sites
Tool read-only (SAFE). Trả về JSON summary trong `output`.
"""
from __future__ import annotations
import ast
import json
from typing import Any, Dict, List, Optional, Tuple
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
# Các operation được hỗ trợ // supported operations
OPERATIONS = {
"dump_tree",
"list_functions",
"list_classes",
"list_imports",
"list_calls",
}
def _load_code(args: Dict[str, Any]) -> Tuple[Optional[str], Optional[str]]:
"""Load source từ `path` hoặc `code`. Trả về (code, error)."""
path = args.get("path")
code = args.get("code")
if not path and not code:
return None, "Missing required arg: path hoặc code"
if path:
try:
with open(path, "r", encoding="utf-8") as f:
return f.read(), None
except Exception as e:
return None, f"Không đọc được file {path}: {e}"
return code, None
class CodeASTTool(Tool):
"""Parse Python source thành AST và trả về summary theo operation."""
category = ToolCategory.CODE
safety = ToolSafety.SAFE # read-only analysis
@property
def name(self) -> str:
return "code_ast"
@property
def description(self) -> str:
return (
"Parse Python source thành AST. Hỗ trợ dump_tree, list_functions, "
"list_classes, list_imports, list_calls. Trả về JSON summary."
)
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string", "description": "Đường dẫn file Python (.py)"},
"code": {"type": "string", "description": "Mã nguồn Python (nếu không dùng path)"},
"operation": {
"type": "string",
"enum": sorted(OPERATIONS),
"description": "Operation (default list_functions)",
},
},
"anyOf": [{"required": ["path"]}, {"required": ["code"]}],
}
def validate_args(self, args: Dict[str, Any]) -> Optional[str]:
op = args.get("operation", "list_functions")
if op not in OPERATIONS:
return f"Unsupported operation: {op}. Chọn một trong: {sorted(OPERATIONS)}"
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:
code, err = _load_code(args)
if err:
return ToolResult(success=False, error=err, return_code=1)
op = args.get("operation", "list_functions")
try:
tree = ast.parse(code)
except SyntaxError as e:
return ToolResult(
success=False,
error=f"SyntaxError (line {e.lineno}): {e.msg}",
return_code=1,
metadata={"path": args.get("path")},
)
try:
if op == "dump_tree":
dump = ast.dump(tree, indent=2)
node_count = sum(1 for _ in ast.walk(tree))
return ToolResult(
success=True,
output=dump,
metadata={
"operation": op,
"path": args.get("path"),
"nodes": node_count,
},
)
if op == "list_functions":
items: List[Dict[str, Any]] = []
for n in ast.walk(tree):
if isinstance(n, (ast.FunctionDef, ast.AsyncFunctionDef)):
items.append({
"name": n.name,
"line": n.lineno,
"end_line": getattr(n, "end_lineno", n.lineno),
"args": [a.arg for a in n.args.args],
"decorators": [ast.unparse(d) for d in n.decorator_list],
"is_async": isinstance(n, ast.AsyncFunctionDef),
})
elif op == "list_classes":
items = []
for n in ast.walk(tree):
if isinstance(n, ast.ClassDef):
methods = [
m.name for m in n.body
if isinstance(m, (ast.FunctionDef, ast.AsyncFunctionDef))
]
items.append({
"name": n.name,
"line": n.lineno,
"end_line": getattr(n, "end_lineno", n.lineno),
"bases": [ast.unparse(b) for b in n.bases],
"methods": methods,
"decorators": [ast.unparse(d) for d in n.decorator_list],
})
elif op == "list_imports":
items = []
for n in ast.walk(tree):
if isinstance(n, ast.Import):
for alias in n.names:
items.append({
"line": n.lineno,
"module": alias.name,
"alias": alias.asname,
"type": "import",
})
elif isinstance(n, ast.ImportFrom):
mod = "." * (n.level or 0) + (n.module or "")
for alias in n.names:
items.append({
"line": n.lineno,
"module": mod,
"name": alias.name,
"alias": alias.asname,
"type": "from",
})
elif op == "list_calls":
items = []
for n in ast.walk(tree):
if isinstance(n, ast.Call):
try:
func_repr = ast.unparse(n.func)
except Exception:
func_repr = "<unknown>"
items.append({"line": n.lineno, "func": func_repr})
else:
return ToolResult(success=False, error=f"Unknown operation: {op}", return_code=1)
return ToolResult(
success=True,
output=json.dumps(items, indent=2, ensure_ascii=False),
metadata={
"operation": op,
"path": args.get("path"),
"count": len(items),
},
)
except Exception as e:
return ToolResult(
success=False,
error=f"{type(e).__name__}: {e}",
return_code=1,
)
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