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: 9,127 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 | """Code Tools - code search, lint, format."""
from __future__ import annotations
import os
import re
import subprocess
from typing import Dict, Any, List
from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety
class CodeSearchTool(Tool):
"""Search code trong files với regex."""
category = ToolCategory.CODE
safety = ToolSafety.SAFE
@property
def name(self) -> str:
return "code_search"
@property
def description(self) -> str:
return "Search trong code files bằng regex. Hỗ trợ file pattern, context lines."
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"pattern": {"type": "string", "description": "Regex pattern"},
"path": {"type": "string", "default": "."},
"file_pattern": {"type": "string", "default": "*.py"},
"case_insensitive": {"type": "boolean", "default": False},
"context": {"type": "integer", "default": 0, "description": "Lines of context"},
"max_results": {"type": "integer", "default": 50},
},
"required": ["pattern"],
}
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
pattern = args["pattern"]
path = args.get("path", ".")
file_pattern = args.get("file_pattern", "*.py")
case_insensitive = args.get("case_insensitive", False)
context_lines = args.get("context", 0)
max_results = args.get("max_results", 50)
flags = re.IGNORECASE if case_insensitive else 0
try:
regex = re.compile(pattern, flags)
except re.error as e:
return ToolResult(success=False, error=f"Invalid regex: {e}", return_code=2)
full_path = path if os.path.isabs(path) else os.path.join(context.working_dir, path)
matches = []
files_scanned = 0
for root, dirs, files in os.walk(full_path):
# Skip hidden dirs, venv, __pycache__, .git
dirs[:] = [d for d in dirs if not d.startswith(".") and d not in (
"venv", "__pycache__", "node_modules", ".git", "dist", "build",
)]
for fname in files:
if not _matches_pattern(fname, file_pattern):
continue
fpath = os.path.join(root, fname)
files_scanned += 1
try:
with open(fpath, "r", encoding="utf-8", errors="replace") as f:
lines = f.readlines()
for i, line in enumerate(lines):
if regex.search(line):
start = max(0, i - context_lines)
end = min(len(lines), i + context_lines + 1)
context_text = "".join(
f" {j+1}: {lines[j]}" for j in range(start, end)
)
matches.append({
"file": fpath,
"line": i + 1,
"match": line.rstrip(),
"context": context_text,
})
if len(matches) >= max_results:
return ToolResult(
success=True,
output=_format_matches(matches),
metadata={
"total_matches": len(matches),
"files_scanned": files_scanned,
"truncated": True,
},
)
except Exception:
continue
return ToolResult(
success=True,
output=_format_matches(matches) if matches else "No matches found.",
metadata={"total_matches": len(matches), "files_scanned": files_scanned},
)
def _matches_pattern(fname: str, pattern: str) -> bool:
"""Simple glob matching."""
import fnmatch
return fnmatch.fnmatch(fname, pattern)
def _format_matches(matches: List[Dict]) -> str:
lines = []
for m in matches:
lines.append(f"📄 {m['file']}:{m['line']}")
lines.append(f" → {m['match']}")
if m.get("context"):
lines.append(m["context"])
lines.append("")
return "\n".join(lines)
class CodeLintTool(Tool):
"""Lint code với nhiều linters."""
category = ToolCategory.CODE
safety = ToolSafety.SAFE
@property
def name(self) -> str:
return "code_lint"
@property
def description(self) -> str:
return "Lint Python code với pyflakes, pycodestyle, hoặc pylint."
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string"},
"linter": {"type": "string", "default": "auto", "enum": ["auto", "pyflakes", "pycodestyle", "pylint", "flake8", "ruff"]},
},
"required": ["path"],
}
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
path = args["path"]
linter = args.get("linter", "auto")
linters_to_try = ["ruff", "flake8", "pyflakes", "pycodestyle"] if linter == "auto" else [linter]
for l in linters_to_try:
try:
result = subprocess.run(
[l, path],
capture_output=True,
text=True,
timeout=context.timeout,
check=False,
)
if result.returncode == 0 or result.stdout or result.stderr:
return ToolResult(
success=(result.returncode == 0),
output=result.stdout or "(no issues)",
error=result.stderr if result.stderr else None,
return_code=result.returncode,
metadata={"linter": l, "path": path},
)
except FileNotFoundError:
continue
except Exception:
continue
return ToolResult(
success=False,
error="No linter available. Install: pip install ruff flake8",
return_code=1,
)
class CodeFormatTool(Tool):
"""Format code với black, autopep8, hoặc isort."""
category = ToolCategory.CODE
safety = ToolSafety.MODERATE
@property
def name(self) -> str:
return "code_format"
@property
def description(self) -> str:
return "Format Python code với black / autopep8 / isort."
@property
def parameters(self) -> Dict[str, Any]:
return {
"type": "object",
"properties": {
"path": {"type": "string"},
"formatter": {"type": "string", "default": "auto", "enum": ["auto", "black", "autopep8", "isort"]},
"check_only": {"type": "boolean", "default": False},
},
"required": ["path"],
}
def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult:
path = args["path"]
formatter = args.get("formatter", "auto")
check_only = args.get("check_only", False)
formatters = ["black", "autopep8", "isort"] if formatter == "auto" else [formatter]
for fmt in formatters:
cmd = [fmt]
if fmt == "black":
cmd.append("--check" if check_only else "--write")
elif fmt == "autopep8":
cmd.append("--in-place" if not check_only else "--diff")
elif fmt == "isort":
cmd.append("--check-only" if check_only else "--write")
cmd.append(path)
try:
result = subprocess.run(
cmd,
capture_output=True,
text=True,
timeout=context.timeout,
check=False,
)
return ToolResult(
success=(result.returncode == 0),
output=result.stdout or f"Formatted with {fmt}",
error=result.stderr if result.stderr else None,
return_code=result.returncode,
metadata={"formatter": fmt, "path": path, "check_only": check_only},
)
except FileNotFoundError:
continue
except Exception:
continue
return ToolResult(
success=False,
error="No formatter available. Install: pip install black",
return_code=1,
)
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