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: 9,664 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 | """Code Documentation Skill - Sinh docstring/comment tự động.
Hỗ trợ Python (Google/NumPy/Sphinx), JS (JSDoc), TS (TSDoc), Go (godoc),
Rust (rustdoc), Java (Javadoc), với template per style.
Author: Hieu Louis (2026)
"""
from __future__ import annotations
from typing import Dict, List
from .base import Skill, SkillContext, SkillCategory, SkillPriority, SkillResult
class CodeDocumentationSkill(Skill):
"""Sinh docstring và comment cho function/class/module."""
category = SkillCategory.DOCUMENTATION
priority = SkillPriority.MEDIUM
keywords: List[str] = [
"docstring", "document function", "document class",
"jsdoc", "javadoc", "godoc", "rustdoc", "tsdoc",
"generate docs", "documentation", "tài liệu",
"viết docstring", "comment code", "annotate",
]
examples = [
"Generate Google-style docstring for this Python function",
"Write JSDoc for this JavaScript function",
"Document all public methods of this class",
]
@property
def name(self) -> str:
return "code_documentation"
@property
def description(self) -> str:
return (
"Sinh docstring/comment cho Python (Google/NumPy/Sphinx), "
"JS (JSDoc), TS (TSDoc), Go (godoc), Rust (rustdoc), Java (Javadoc)."
)
def can_handle(self, prompt: str, context: SkillContext = None) -> float:
prompt_lower = prompt.lower()
score = 0.0
for kw in self.keywords:
if kw in prompt_lower:
score += 0.16
if "def " in prompt or "function " in prompt or "func " in prompt:
score += 0.15
return min(1.0, score)
def execute(self, context: SkillContext) -> SkillResult:
lang = (context.language or "python").lower()
return SkillResult(
success=True,
output=f"[CodeDocumentation/{lang}] Docstring templates ready.",
artifacts=[
{"path": "docs/templates.md", "content": _DOCSTRING_TEMPLATES},
{"path": "docs/jsdoc_template.md", "content": _JSDOC_TEMPLATE},
{"path": "docs/strategy.md", "content": _DOC_STRATEGY},
],
metadata={
"skill": self.name,
"language": lang,
"styles": {
"python": ["google", "numpy", "sphinx", "rest"],
"javascript": ["jsdoc"],
"typescript": ["tsdoc (typedoc)"],
"go": ["godoc (no annotations)"],
"rust": ["rustdoc markdown"],
"java": ["javadoc"],
"kotlin": ["kdoc"],
},
"extraction_targets": [
"purpose (first sentence)",
"parameters (name, type, meaning, default, constraints)",
"return value (type, meaning, conditions)",
"raises/throws (exception types + when)",
"examples (doctest-runnable when possible)",
"side effects",
"deprecated + replacement",
"see also",
],
"tooling": {
"python": "Sphinx + autodoc + napoleon + intersphinx",
"js": "TypeDoc (TS) / JSDoc (JS)",
"go": "godoc / pkg.go.dev",
"rust": "cargo doc",
"java": "Javadoc + Maven Javadoc plugin",
},
"validation": [
"doctest for Python examples",
"mypy/pyright on type annotations",
"lint: every public symbol has docs (CI check)",
],
},
suggestions=[
"Pick style explicitly: 'google' / 'numpy' / 'sphinx' for Python",
"Ask for doctest-runnable examples when applicable",
"Document exceptions explicitly even if not raised directly",
],
)
_DOCSTRING_TEMPLATES = '''# Python Docstring Templates
## Google style
```python
def compute_discount(cart, customer_tier, coupon=None):
"""Compute discount for a cart.
Applies tiered discount rules based on cart total and customer tier.
Discount is capped at 40% for retail customers.
Args:
cart: List of (sku, unit_price, quantity) tuples. Must be non-empty.
customer_tier: One of "bronze", "silver", "gold". Case-insensitive.
coupon: Optional coupon code. None for no coupon.
Returns:
Tuple of (discount_amount, final_total). discount_amount in
[0, cart_subtotal]. final_total is non-negative.
Raises:
ValueError: If cart is empty or customer_tier is unknown.
CouponExpiredError: If coupon code is past its expiry date.
Examples:
>>> compute_discount([("A1", 100, 2)], "gold")
(20.0, 180.0)
"""
...
```
## NumPy style
```python
def compute_discount(cart, customer_tier, coupon=None):
"""Compute discount for a cart.
Applies tiered discount rules based on cart total and customer tier.
Parameters
----------
cart : list[tuple[str, float, int]]
List of (sku, unit_price, quantity) tuples. Must be non-empty.
customer_tier : {"bronze", "silver", "gold"}
Customer loyalty tier. Case-insensitive.
coupon : str, optional
Optional coupon code. None for no coupon.
Returns
-------
tuple[float, float]
(discount_amount, final_total). discount_amount in [0, subtotal].
Raises
------
ValueError
If cart is empty or customer_tier is unknown.
CouponExpiredError
If coupon code is past its expiry date.
Examples
--------
>>> compute_discount([("A1", 100, 2)], "gold")
(20.0, 180.0)
"""
...
```
## Sphinx (reST) style
```python
def compute_discount(cart, customer_tier, coupon=None):
"""Compute discount for a cart.
Applies tiered discount rules based on cart total and customer tier.
:param cart: List of (sku, unit_price, quantity) tuples. Must be non-empty.
:type cart: list[tuple[str, float, int]]
:param customer_tier: One of "bronze", "silver", "gold". Case-insensitive.
:type customer_tier: str
:param coupon: Optional coupon code. None for no coupon.
:type coupon: str | None
:returns: (discount_amount, final_total).
:rtype: tuple[float, float]
:raises ValueError: If cart is empty or customer_tier is unknown.
:raises CouponExpiredError: If coupon code is past expiry.
Example::
>>> compute_discount([("A1", 100, 2)], "gold")
(20.0, 180.0)
"""
...
```
'''
_JSDOC_TEMPLATE = '''# JSDoc / TSDoc Template
```javascript
/**
* Compute discount for a cart.
*
* Applies tiered discount rules based on cart total and customer tier.
* Discount is capped at 40% for retail customers.
*
* @param {Array<{sku: string, unitPrice: number, quantity: number}>} cart
* List of cart items. Must be non-empty.
* @param {"bronze" | "silver" | "gold"} customerTier
* Customer loyalty tier. Case-insensitive.
* @param {string | null} [coupon=null]
* Optional coupon code. Pass null for no coupon.
* @returns {{discountAmount: number, finalTotal: number}}
* Discount amount (0 <= d <= subtotal) and final total.
* @throws {TypeError} If cart is empty.
* @throws {CouponExpiredError} If coupon code is past expiry.
*
* @example
* const { discountAmount, finalTotal } = computeDiscount(
* [{ sku: "A1", unitPrice: 100, quantity: 2 }],
* "gold"
* );
* // => { discountAmount: 20, finalTotal: 180 }
*
* @see {@link applyCoupon} for coupon resolution logic.
* @since 1.2.0
* @public
*/
function computeDiscount(cart, customerTier, coupon = null) {
// ...
}
```
## TSDoc (TypeScript) variant
```typescript
/**
* Compute discount for a cart.
*
* @param cart - List of cart items. Must be non-empty.
* @param customerTier - Customer loyalty tier. Case-insensitive.
* @param coupon - Optional coupon code. Pass null for no coupon.
* @returns Discount amount and final total.
* @throws {TypeError} If cart is empty.
*
* @example
* ```ts
* const r = computeDiscount([{ sku: "A1", unitPrice: 100, quantity: 2 }], "gold");
* ```
*/
function computeDiscount(
cart: CartItem[],
customerTier: "bronze" | "silver" | "gold",
coupon: string | null = null,
): { discountAmount: number; finalTotal: number } {
// ...
}
```
'''
_DOC_STRATEGY = """# Documentation Generation Strategy
## Phase 1: Static Extraction
- Parse AST, collect: function signatures, parameter types, return types,
raised exceptions, decorators, class hierarchy.
- Infer types when missing (mypy/pyright inference).
## Phase 2: Purpose Inference
- Heuristics: function name + first assignment + last return + called functions.
- LLM fallback: ask for one-sentence summary, validate against signature.
## Phase 3: Parameter Description
- Per parameter: infer from usage (read once? written? returned?).
- Look at type hints + constraint annotations.
- Generate description: "<param> is the <role>: <constraint>".
## Phase 4: Examples
- Generate 1 happy-path + 1 error example.
- Make examples doctest-runnable (Python) or runnable snippets (JS).
## Phase 5: Style Compliance
- Match existing docstring style in module (auto-detect: google/numpy/sphinx).
- Match indentation, line length, terminology.
## Phase 6: Validation
- doctest: every `>>>` block must pass.
- darglint / pydocstyle / flake8-docstrings lint.
- Verify all params in signature have `Args:` entries.
"""
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