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: 6,290 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 | """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
@property
def name(self) -> str:
return "datetime"
@property
def description(self) -> str:
return (
"DateTime operations: now, parse, format, timezone convert, "
"date arithmetic, weekday, days between."
)
@property
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)
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