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
| """DateTime Tool - time/date operations.""" | |
| from __future__ import annotations | |
| from datetime import datetime, timezone, timedelta | |
| from typing import Dict, Any | |
| from .base import Tool, ToolResult, ToolContext, ToolCategory, ToolSafety | |
| class DateTimeTool(Tool): | |
| """DateTime operations: now, parse, format, convert timezone, arithmetic.""" | |
| category = ToolCategory.SYSTEM | |
| safety = ToolSafety.SAFE | |
| def name(self) -> str: | |
| return "datetime" | |
| def description(self) -> str: | |
| return ( | |
| "DateTime operations: now, parse, format, timezone convert, " | |
| "date arithmetic, weekday, days between." | |
| ) | |
| def parameters(self) -> Dict[str, Any]: | |
| return { | |
| "type": "object", | |
| "properties": { | |
| "action": { | |
| "type": "string", | |
| "enum": ["now", "parse", "format", "convert_tz", "add", "diff"], | |
| "default": "now", | |
| }, | |
| "datetime_str": {"type": "string", "description": "For parse/format/convert: input datetime"}, | |
| "format_str": {"type": "string", "description": "strftime/strptime format"}, | |
| "from_tz": {"type": "string", "description": "Source timezone (IANA name)"}, | |
| "to_tz": {"type": "string", "description": "Target timezone"}, | |
| "delta_days": {"type": "integer", "description": "Days to add (can be negative)"}, | |
| "delta_hours": {"type": "integer"}, | |
| "start": {"type": "string", "description": "For diff: start datetime"}, | |
| "end": {"type": "string", "description": "For diff: end datetime"}, | |
| }, | |
| } | |
| def execute(self, args: Dict[str, Any], context: ToolContext) -> ToolResult: | |
| action = args.get("action", "now") | |
| try: | |
| if action == "now": | |
| now_utc = datetime.now(timezone.utc) | |
| now_local = datetime.now().astimezone() | |
| return ToolResult( | |
| success=True, | |
| output=( | |
| f"UTC: {now_utc.isoformat()}\n" | |
| f"Local: {now_local.isoformat()}\n" | |
| f"Timestamp: {now_utc.timestamp()}" | |
| ), | |
| metadata={ | |
| "utc": now_utc.isoformat(), | |
| "local": now_local.isoformat(), | |
| "timestamp": now_utc.timestamp(), | |
| "timezone": str(now_local.tzinfo), | |
| }, | |
| ) | |
| elif action == "parse": | |
| dt_str = args["datetime_str"] | |
| fmt = args.get("format_str") | |
| if fmt: | |
| dt = datetime.strptime(dt_str, fmt) | |
| else: | |
| # Try ISO format | |
| dt = datetime.fromisoformat(dt_str.replace("Z", "+00:00")) | |
| return ToolResult( | |
| success=True, | |
| output=f"Parsed: {dt.isoformat()}", | |
| metadata={"parsed": dt.isoformat(), "weekday": dt.strftime("%A")}, | |
| ) | |
| elif action == "format": | |
| dt_str = args["datetime_str"] | |
| fmt = args.get("format_str", "%Y-%m-%d %H:%M:%S") | |
| dt = datetime.fromisoformat(dt_str.replace("Z", "+00:00")) | |
| return ToolResult( | |
| success=True, | |
| output=dt.strftime(fmt), | |
| metadata={"format": fmt}, | |
| ) | |
| elif action == "convert_tz": | |
| dt_str = args["datetime_str"] | |
| from_tz = args.get("from_tz", "UTC") | |
| to_tz = args["to_tz"] | |
| try: | |
| from zoneinfo import ZoneInfo | |
| tz_from = ZoneInfo(from_tz) | |
| tz_to = ZoneInfo(to_tz) | |
| except ImportError: | |
| return ToolResult( | |
| success=False, | |
| error="zoneinfo not available (Python 3.9+)", | |
| return_code=1, | |
| ) | |
| dt = datetime.fromisoformat(dt_str).replace(tzinfo=tz_from) | |
| converted = dt.astimezone(tz_to) | |
| return ToolResult( | |
| success=True, | |
| output=f"{dt_str} ({from_tz}) → {converted.isoformat()} ({to_tz})", | |
| metadata={"original": dt.isoformat(), "converted": converted.isoformat()}, | |
| ) | |
| elif action == "add": | |
| dt_str = args["datetime_str"] | |
| delta_days = args.get("delta_days", 0) | |
| delta_hours = args.get("delta_hours", 0) | |
| dt = datetime.fromisoformat(dt_str.replace("Z", "+00:00")) | |
| new_dt = dt + timedelta(days=delta_days, hours=delta_hours) | |
| return ToolResult( | |
| success=True, | |
| output=f"{dt.isoformat()} + {delta_days}d {delta_hours}h = {new_dt.isoformat()}", | |
| metadata={"result": new_dt.isoformat()}, | |
| ) | |
| elif action == "diff": | |
| start = datetime.fromisoformat(args["start"].replace("Z", "+00:00")) | |
| end = datetime.fromisoformat(args["end"].replace("Z", "+00:00")) | |
| delta = end - start | |
| return ToolResult( | |
| success=True, | |
| output=( | |
| f"Diff: {delta}\n" | |
| f"Days: {delta.days}\n" | |
| f"Seconds: {delta.total_seconds()}\n" | |
| f"Hours: {delta.total_seconds() / 3600}" | |
| ), | |
| metadata={ | |
| "days": delta.days, | |
| "seconds": delta.total_seconds(), | |
| }, | |
| ) | |
| else: | |
| return ToolResult(success=False, error=f"Unknown action: {action}", return_code=2) | |
| except Exception as e: | |
| return ToolResult(success=False, error=str(e), return_code=1) | |