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
| """ | |
| Code Metrics Tool - Tính metrics: LOC, SLOC, comments, blank lines, file count. | |
| Author: Hieu Louis (2026) | |
| Sử dụng stdlib `tokenize` cho Python files để đếm comment chính xác, | |
| fallback heuristic (comment prefix) cho các ngôn ngữ khác. | |
| """ | |
| from __future__ import annotations | |
| import io | |
| import json | |
| import os | |
| import tokenize | |
| from typing import Any, Dict, List, Optional | |
| from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety | |
| DEFAULT_EXTENSIONS = [ | |
| ".py", ".js", ".ts", ".java", ".c", ".cpp", ".h", ".hpp", | |
| ".go", ".rs", ".rb", ".php", ".sql", ".sh", ".yml", ".yaml", | |
| ] | |
| # Map extension → comment prefix (cho non-Python files) | |
| COMMENT_PREFIX = { | |
| ".js": "//", ".ts": "//", ".java": "//", ".c": "//", ".cpp": "//", | |
| ".h": "//", ".hpp": "//", ".go": "//", ".rs": "//", ".php": "//", | |
| ".sh": "#", ".yml": "#", ".yaml": "#", | |
| ".rb": "#", ".sql": "--", | |
| } | |
| def _metrics_python(source: str) -> Dict[str, int]: | |
| """Tính metrics cho file Python dùng tokenize (chính xác).""" | |
| lines = source.splitlines() | |
| loc = len(lines) | |
| blank = sum(1 for ln in lines if not ln.strip()) | |
| comments = 0 | |
| try: | |
| toks = list(tokenize.generate_tokens(io.StringIO(source).readline)) | |
| for tok in toks: | |
| if tok.type == tokenize.COMMENT: | |
| comments += 1 | |
| except tokenize.TokenError: | |
| # Fallback heuristic | |
| comments = sum(1 for ln in lines if ln.strip().startswith("#")) | |
| # SLOC = non-blank, non-pure-comment lines | |
| sloc = 0 | |
| for ln in lines: | |
| s = ln.strip() | |
| if not s: | |
| continue | |
| if s.startswith("#"): | |
| continue | |
| sloc += 1 | |
| return {"loc": loc, "sloc": sloc, "comments": comments, "blank": blank} | |
| def _metrics_text(source: str, comment_prefix: str = "#") -> Dict[str, int]: | |
| """Tính metrics cho file text generic (non-Python) theo prefix.""" | |
| lines = source.splitlines() | |
| loc = len(lines) | |
| blank = sum(1 for ln in lines if not ln.strip()) | |
| comments = sum(1 for ln in lines if ln.strip().startswith(comment_prefix)) | |
| sloc = loc - blank - comments | |
| if sloc < 0: | |
| sloc = 0 | |
| return {"loc": loc, "sloc": sloc, "comments": comments, "blank": blank} | |
| def _walk_files(path: str, extensions: Optional[List[str]]) -> List[str]: | |
| """Walk tất cả files trong path (file hoặc dir), lọc theo extension.""" | |
| if os.path.isfile(path): | |
| return [path] | |
| out: List[str] = [] | |
| for root, _dirs, names in os.walk(path): | |
| for name in sorted(names): | |
| if extensions: | |
| if not any(name.endswith(ext) for ext in extensions): | |
| continue | |
| out.append(os.path.join(root, name)) | |
| return out | |
| class CodeMetricsTool(Tool): | |
| """Tính LOC/SLOC/comments/blank lines/file count cho file hoặc thư mục.""" | |
| category = ToolCategory.CODE | |
| safety = ToolSafety.SAFE # read-only | |
| def name(self) -> str: | |
| return "code_metrics" | |
| def description(self) -> str: | |
| return ( | |
| "Tính metrics: LOC, SLOC, comments, blank lines, file count. " | |
| "Hỗ trợ thư mục hoặc single file. Python dùng tokenize, " | |
| "ngôn ngữ khác dùng heuristic theo comment prefix." | |
| ) | |
| def parameters(self) -> Dict[str, Any]: | |
| return { | |
| "type": "object", | |
| "properties": { | |
| "path": {"type": "string", "description": "File hoặc thư mục"}, | |
| "extensions": { | |
| "type": "array", | |
| "items": {"type": "string"}, | |
| "description": ( | |
| "Lọc theo extension (vd ['.py', '.js']). " | |
| "Mặc định auto-detect nhiều loại code files." | |
| ), | |
| }, | |
| }, | |
| "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"] | |
| extensions: List[str] = args.get("extensions") or DEFAULT_EXTENSIONS | |
| if not os.path.exists(path): | |
| return ToolResult( | |
| success=False, | |
| error=f"Path không tồn tại: {path}", | |
| return_code=1, | |
| ) | |
| files = _walk_files(path, extensions) | |
| if not files: | |
| return ToolResult( | |
| success=True, | |
| output="[]", | |
| metadata={"path": path, "file_count": 0, "totals": {}}, | |
| ) | |
| per_file: List[Dict[str, Any]] = [] | |
| totals: Dict[str, int] = {"loc": 0, "sloc": 0, "comments": 0, "blank": 0} | |
| for fp in files: | |
| try: | |
| with open(fp, "r", encoding="utf-8", errors="replace") as f: | |
| src = f.read() | |
| except Exception: | |
| continue | |
| if fp.endswith(".py"): | |
| m = _metrics_python(src) | |
| else: | |
| # Tìm comment prefix phù hợp | |
| prefix = "#" | |
| for ext, p in COMMENT_PREFIX.items(): | |
| if fp.endswith(ext): | |
| prefix = p | |
| break | |
| m = _metrics_text(src, prefix) | |
| m["file"] = fp | |
| per_file.append(m) | |
| for k in totals: | |
| totals[k] += m.get(k, 0) | |
| return ToolResult( | |
| success=True, | |
| output=json.dumps( | |
| {"files": per_file, "totals": totals}, | |
| indent=2, ensure_ascii=False, | |
| ), | |
| metadata={ | |
| "path": path, | |
| "file_count": len(per_file), | |
| "totals": totals, | |
| }, | |
| ) | |