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
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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 | """Code Refactor Skill - Tái cấu trúc code."""
from __future__ import annotations
from typing import List
from .base import Skill, SkillResult, SkillContext, SkillCategory, SkillPriority
class CodeRefactorSkill(Skill):
"""Refactor code: extract functions, simplify logic, apply design patterns."""
category = SkillCategory.CODE
priority = SkillPriority.MEDIUM
keywords: List[str] = [
"refactor", "tái cấu trúc", "cleanup", "dọn dẹp",
"optimize", "tối ưu", "simplify", "đơn giản hóa",
"extract", "tách", "restructure",
]
@property
def name(self) -> str:
return "code_refactor"
@property
def description(self) -> str:
return (
"Refactor code an toàn: extract method/function, rename variables, "
"apply design patterns, reduce complexity, eliminate duplication."
)
def execute(self, context: SkillContext) -> SkillResult:
refactorings = [
"Extract Function / Method",
"Extract Class",
"Rename (variable/function/class)",
"Inline Function / Temp",
"Move Function / Field",
"Replace Conditional with Polymorphism",
"Replace Inheritance with Delegation",
"Replace Nested Conditional with Guard Clauses",
"Introduce Parameter Object",
"Replace Magic Number with Symbolic Constant",
"Decompose Conditional",
"Consolidate Conditional Expression",
]
return SkillResult(
success=True,
output=f"[CodeRefactor] Suggesting {len(refactorings)} refactoring patterns.",
metadata={
"skill": self.name,
"available_refactorings": refactorings,
"preserve_behavior": True,
},
suggestions=[
"Always run tests after refactoring",
"Use git branches for safe refactoring",
],
)
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