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: 6,572 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 | """Code Explanation Skill - Giải thích code từng bước.
Framework explain: mục đích, interface, luồng điều khiển, dữ liệu,
edge cases, độ phức tạp, và potential pitfalls.
Author: Hieu Louis (2026)
"""
from __future__ import annotations
from typing import Dict, List
from .base import Skill, SkillContext, SkillCategory, SkillPriority, SkillResult
class CodeExplanationSkill(Skill):
"""Giải thích code tự nhiên từng bước cho developer."""
category = SkillCategory.CODE
priority = SkillPriority.MEDIUM
keywords: List[str] = [
"explain", "giải thích", "what does this code", "walk through",
"walk me through", "describe code", "how does this work",
"hiểu code", "phân tích code", "break down",
"what is this function doing", "comment code",
]
examples = [
"Explain this Python decorator step by step",
"What does this recursive function do?",
"Walk me through this SQL query",
]
@property
def name(self) -> str:
return "code_explanation"
@property
def description(self) -> str:
return (
"Giải thích code tự nhiên: mục đích, luồng điều khiển, "
"biến đổi dữ liệu, edge cases, độ phức tạp, và pitfalls."
)
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 += 0.15
if "```" in prompt or "def " in prompt or "function " in prompt:
score += 0.2
return min(1.0, score)
def execute(self, context: SkillContext) -> SkillResult:
return SkillResult(
success=True,
output="[CodeExplanation] Step-by-step explanation framework ready.",
artifacts=[
{"path": "explanation/framework.md", "content": _EXPLANATION_FRAMEWORK},
{"path": "explanation/example.md", "content": _EXAMPLE_EXPLANATION},
],
metadata={
"skill": self.name,
"explanation_levels": [
"ELI5 (giải thích như mới học code)",
"junior dev (giải thích từng dòng)",
"senior dev (focus kiến trúc + trade-offs)",
"expert (focus correctness + perf characteristics)",
],
"framework_steps": [
"1. One-sentence summary (mục đích)",
"2. Inputs / outputs / side effects",
"3. Step-by-step walkthrough (line hoặc block)",
"4. Data flow diagram (text-based)",
"5. Edge cases & error handling",
"6. Time/space complexity",
"7. Pitfalls / code smells / suggestions",
],
"diagram_styles": ["ascii", "mermaid sequence", "mermaid flowchart"],
"audience_tuning": {
"eli5": "Use analogies, no jargon, 1 concept per paragraph",
"junior": "Explain syntax, link to docs, define jargon",
"senior": "Skip basics, focus on architecture & trade-offs",
"expert": "Focus on correctness, perf, alternatives",
},
},
suggestions=[
"Specify audience level (ELI5 / junior / senior / expert)",
"Provide code in fenced block for accurate line references",
"Ask for specific aspect (complexity, correctness, security)",
],
)
_EXPLANATION_FRAMEWORK = """# Code Explanation Framework
## Level 0: One-Sentence Summary
> "This code does X by Y."
## Level 1: Interface Contract
- **Inputs**: parameters, types, constraints
- **Outputs**: return type, side effects, exceptions
- **Preconditions**: what must be true before calling
- **Postconditions**: what is guaranteed after return
## Level 2: Step-by-Step Walkthrough
For each block:
1. **What** is being done (one sentence)
2. **Why** it's done this way (motivation)
3. **How** it interacts with prior/next blocks
## Level 3: Data Flow
```
input -> [transform 1] -> [filter] -> [aggregate] -> output
```
## Level 4: Edge Cases & Error Handling
- Null / undefined / empty inputs
- Boundary conditions (0, 1, max_int, negative)
- Concurrency / reentrancy
- Resource exhaustion (memory, file handles)
## Level 5: Complexity
- Time: O(?) - best / average / worst
- Space: O(?) - auxiliary vs total
- Practical: cache misses, branch prediction
## Level 6: Pitfalls & Suggestions
- Code smells (long method, deep nesting, magic numbers)
- Common bugs (off-by-one, race conditions)
- Refactor opportunities (extract method, replace conditional with polymorphism)
"""
_EXAMPLE_EXPLANATION = '''# Example Explanation: Binary Search
## Code
```python
def binary_search(arr: list[int], target: int) -> int:
lo, hi = 0, len(arr) - 1
while lo <= hi:
mid = (lo + hi) // 2
if arr[mid] == target:
return mid
elif arr[mid] < target:
lo = mid + 1
else:
hi = mid - 1
return -1
```
## Summary
Binary search finds `target` in `arr` (already sorted ascending), returning its index or -1.
## Interface
- **Inputs**: sorted list `arr`, int `target`
- **Output**: index of `target` in `arr`, or -1 if not found
- **Precondition**: `arr` sorted ascending
- **Postcondition**: returned index i satisfies `arr[i] == target`, or i == -1
## Walkthrough
1. `lo=0, hi=len(arr)-1`: initialize search bounds.
2. `while lo <= hi`: loop until search space empty.
3. `mid = (lo + hi) // 2`: pick middle index.
- Note: risk of overflow in C — Python ints are arbitrary precision so safe.
4. `arr[mid] == target`: hit, return `mid`.
5. `arr[mid] < target`: target in right half, move `lo` past `mid`.
6. `arr[mid] > target`: target in left half, move `hi` before `mid`.
7. `return -1`: search space exhausted, not found.
## Complexity
- Time: O(log n) - halve search space each iteration.
- Space: O(1) - only three variables.
## Pitfalls
- Integer overflow in `mid = (lo + hi) // 2` in C/Java. Use `lo + (hi - lo) // 2`.
- Input MUST be sorted; precondition not enforced.
- Returns first-found index, not necessarily the leftmost duplicate.
## Suggestions
- Add `is_sorted` assertion for debug builds.
- Use `bisect_left` from stdlib for leftmost match.
'''
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