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 Profiler Tool - Profile Python code với cProfile + pstats. | |
| Author: Hieu Louis (2026) | |
| Chạy code trong cProfile context, xuất top-N functions theo cumulative time. | |
| DANGEROUS (executes Python code), requires_confirmation. | |
| Note: Sử dụng stdlib `cProfile` và `pstats` (không cần lazy import). | |
| """ | |
| from __future__ import annotations | |
| import cProfile | |
| import io | |
| import os | |
| import pstats | |
| from typing import Any, Dict, Optional | |
| from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety | |
| class CodeProfilerTool(Tool): | |
| """Profile Python code với cProfile + pstats (stdlib).""" | |
| category = ToolCategory.EXEC | |
| safety = ToolSafety.DANGEROUS # executes Python code | |
| requires_confirmation = True | |
| def name(self) -> str: | |
| return "code_profiler" | |
| def description(self) -> str: | |
| return ( | |
| "Profile Python code với cProfile + pstats. Trả về bảng top functions " | |
| "theo cumulative time. Hỗ trợ top_n (default 20)." | |
| ) | |
| def parameters(self) -> Dict[str, Any]: | |
| return { | |
| "type": "object", | |
| "properties": { | |
| "path": {"type": "string", "description": "File Python để profile"}, | |
| "code": {"type": "string", "description": "Python code (nếu không dùng path)"}, | |
| "top_n": { | |
| "type": "integer", | |
| "default": 20, | |
| "description": "Số dòng top functions trong output", | |
| }, | |
| }, | |
| "anyOf": [{"required": ["path"]}, {"required": ["code"]}], | |
| } | |
| def validate_args(self, args: Dict[str, Any]) -> Optional[str]: | |
| 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: | |
| path: Optional[str] = args.get("path") | |
| code: Optional[str] = args.get("code") | |
| top_n: int = int(args.get("top_n", 20)) | |
| if path: | |
| if not os.path.isfile(path): | |
| return ToolResult( | |
| success=False, | |
| error=f"File không tồn tại: {path}", | |
| return_code=1, | |
| ) | |
| try: | |
| with open(path, "r", encoding="utf-8") as f: | |
| code = f.read() | |
| except Exception as e: | |
| return ToolResult(success=False, error=f"Đọc file lỗi: {e}", return_code=1) | |
| assert code is not None | |
| if context.dry_run: | |
| return ToolResult( | |
| success=True, | |
| output=f"[dry-run] Sẽ profile {len(code)} bytes Python code (top_n={top_n})", | |
| metadata={"top_n": top_n, "dry_run": True, "code_length": len(code)}, | |
| ) | |
| # Compile code (catch SyntaxError before profiling) | |
| try: | |
| compiled = compile(code, "<profile>", "exec") | |
| except SyntaxError as e: | |
| return ToolResult( | |
| success=False, | |
| error=f"SyntaxError line {e.lineno}: {e.msg}", | |
| return_code=1, | |
| ) | |
| # Run with cProfile | |
| profiler = cProfile.Profile() | |
| globals_dict: Dict[str, Any] = { | |
| "__name__": "__nexus_profile__", | |
| "__builtins__": __builtins__, | |
| } | |
| try: | |
| profiler.enable() | |
| exec(compiled, globals_dict) | |
| profiler.disable() | |
| except Exception as e: | |
| profiler.disable() | |
| # Vẫn xuất partial stats | |
| stats_buf = io.StringIO() | |
| stats = pstats.Stats(profiler, stream=stats_buf) | |
| stats.sort_stats("cumulative").print_stats(top_n) | |
| return ToolResult( | |
| success=False, | |
| error=f"{type(e).__name__}: {e}", | |
| return_code=1, | |
| output=stats_buf.getvalue(), | |
| metadata={ | |
| "top_n": top_n, | |
| "path": path, | |
| "partial": True, | |
| "total_calls": getattr(stats, "total_calls", 0), | |
| "total_time": round(getattr(stats, "total_tt", 0.0), 6), | |
| }, | |
| ) | |
| # Build stats output | |
| stats_buf = io.StringIO() | |
| stats = pstats.Stats(profiler, stream=stats_buf) | |
| stats.sort_stats("cumulative").print_stats(top_n) | |
| output = stats_buf.getvalue() | |
| total_calls = getattr(stats, "total_calls", 0) | |
| total_time = getattr(stats, "total_tt", 0.0) | |
| return ToolResult( | |
| success=True, | |
| output=output, | |
| metadata={ | |
| "top_n": top_n, | |
| "total_calls": total_calls, | |
| "total_time": round(total_time, 6), | |
| "path": path, | |
| }, | |
| ) | |