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: 4,933 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 | """
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
@property
def name(self) -> str:
return "code_profiler"
@property
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)."
)
@property
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,
},
)
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