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: 7,657 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 | """DevOps Skill - Sinh Dockerfile, k8s manifests, Terraform, CI/CD pipelines.
Tạo artifacts DevOps từ mô tả tự nhiên: container images, Kubernetes
deployments, infrastructure-as-code, và CI/CD pipeline templates.
Author: Hieu Louis (2026)
"""
from __future__ import annotations
from typing import Dict, List
from .base import Skill, SkillContext, SkillCategory, SkillPriority, SkillResult
class DevOpsSkill(Skill):
"""Sinh DevOps artifacts: Docker, Kubernetes, Terraform, CI/CD."""
category = SkillCategory.DEVOPS
priority = SkillPriority.HIGH
keywords: List[str] = [
"docker", "dockerfile", "container", "kubernetes", "k8s",
"terraform", "ansible", "ci/cd", "cicd", "jenkins",
"github actions", "gitlab ci", "helm", "deploy",
"pod", "deployment", "service", "ingress", "manifest",
]
examples = [
"Tạo Dockerfile cho FastAPI app",
"Generate k8s deployment manifest for Redis",
"Write Terraform to provision an EC2 instance",
"Setup GitHub Actions CI/CD pipeline for Python project",
]
@property
def name(self) -> str:
return "devops"
@property
def description(self) -> str:
return (
"Sinh DevOps artifacts: Dockerfile (multi-stage), Kubernetes "
"manifests (Deployment/Service/Ingress), Terraform modules, "
"Helm charts, và CI/CD pipelines (GitHub Actions / GitLab CI)."
)
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 += 0.25
if any(tag in prompt_lower for tag in ("yaml", "yml", ".tf", "manifest")):
score += 0.2
return min(1.0, score)
def _detect_target(self, prompt: str) -> str:
p = prompt.lower()
if "dockerfile" in p or "docker" in p and "image" in p:
return "dockerfile"
if "kubernetes" in p or "k8s" in p or "manifest" in p or "helm" in p:
return "k8s"
if "terraform" in p or ".tf" in p or "infrastructure" in p:
return "terraform"
if "ci/cd" in p or "cicd" in p or "github actions" in p or "gitlab" in p or "jenkins" in p:
return "cicd"
if "ansible" in p:
return "ansible"
return "dockerfile"
def execute(self, context: SkillContext) -> SkillResult:
target = self._detect_target(context.prompt)
artifact = self._build_artifact(target, context)
return SkillResult(
success=True,
output=f"[DevOps/{target}] Artifact ready for: {context.prompt[:180]}",
artifacts=[artifact],
metadata={
"skill": self.name,
"target": target,
"language": context.language or "yaml",
"tools": ["docker", "kubectl", "terraform", "helm", "act"],
},
suggestions=[
"Pin base image digests for reproducible builds (e.g. python:3.12-slim@sha256:...)",
"Scan images for CVEs (trivy, grype) before pushing",
"Use multi-stage builds to shrink final image size",
"Apply least-privilege RBAC and network policies in k8s",
"Store secrets in a vault (Vault, AWS SM, Sealed Secrets)",
"Enable image signature verification (cosign) in CI",
],
)
def _build_artifact(self, target: str, context: SkillContext) -> Dict[str, str]:
if target == "k8s":
return {
"path": "k8s/deployment.yaml",
"content": _K8S_MANIFEST,
}
if target == "terraform":
return {
"path": "infra/main.tf",
"content": _TERRAFORM_SNIPPET,
}
if target == "cicd":
return {
"path": ".github/workflows/ci.yml",
"content": _GITHUB_ACTIONS,
}
if target == "ansible":
return {
"path": "ansible/playbook.yml",
"content": _ANSIBLE_PLAYBOOK,
}
return {"path": "Dockerfile", "content": _DOCKERFILE}
_DOCKERFILE = """# Multi-stage Dockerfile — Python service
FROM python:3.12-slim AS builder
WORKDIR /app
ENV PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 PIP_NO_CACHE_DIR=1
COPY requirements.txt .
RUN pip install --user -r requirements.txt
FROM python:3.12-slim AS runtime
RUN useradd -m -u 10001 appuser
WORKDIR /app
COPY --from=builder /root/.local /home/appuser/.local
COPY . .
USER appuser
ENV PATH=/home/appuser/.local/bin:$PATH
EXPOSE 8000
HEALTHCHECK --interval=30s --timeout=3s CMD python -c "import urllib.request;urllib.request.urlopen('http://127.0.0.1:8000/health')"
CMD ["uvicorn", "app:main", "--host", "0.0.0.0", "--port", "8000"]
"""
_K8S_MANIFEST = """apiVersion: apps/v1
kind: Deployment
metadata:
name: nexus-api
labels: {app: nexus-api}
spec:
replicas: 3
selector:
matchLabels: {app: nexus-api}
template:
metadata:
labels: {app: nexus-api}
spec:
securityContext:
runAsNonRoot: true
runAsUser: 10001
fsGroup: 10001
containers:
- name: api
image: ghcr.io/nexus/api:0.3.0
ports: [{containerPort: 8000}]
resources:
requests: {cpu: "250m", memory: "256Mi"}
limits: {cpu: "1000m", memory: "1Gi"}
livenessProbe:
httpGet: {path: /health, port: 8000}
initialDelaySeconds: 10
readinessProbe:
httpGet: {path: /ready, port: 8000}
---
apiVersion: v1
kind: Service
metadata:
name: nexus-api
spec:
selector: {app: nexus-api}
ports: [{port: 80, targetPort: 8000}]
type: ClusterIP
"""
_TERRAFORM_SNIPPET = """# Infrastructure as Code — AWS EC2
terraform {
required_version = ">= 1.7"
required_providers {
aws = { source = "hashicorp/aws", version = "~> 5.0" }
}
}
provider "aws" {
region = var.region
}
variable "region" { default = "ap-southeast-1" }
variable "instance_type" { default = "t3.small" }
resource "aws_instance" "nexus" {
ami = data.aws_ami.ubuntu.id
instance_type = var.instance_type
vpc_security_group_ids = [aws_security_group.nexus.id]
tags = { Name = "nexus-coder" }
}
resource "aws_security_group" "nexus" {
name = "nexus-sg"
ingress {
from_port = 443
to_port = 443
protocol = "tcp"
cidr_blocks = ["0.0.0.0/0"]
}
}
"""
_GITHUB_ACTIONS = """name: CI
on:
push: { branches: [main] }
pull_request: { branches: [main] }
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with: { python-version: "3.12" }
- run: pip install -r requirements.txt -r requirements-dev.txt
- run: ruff check .
- run: mypy src
- run: pytest --cov --cov-report=xml
- uses: codecov/codecov-action@v4
"""
_ANSIBLE_PLAYBOOK = """---
- name: Provision Nexus Coder host
hosts: webservers
become: true
vars:
app_version: "0.3.0"
tasks:
- name: Install system deps
apt:
name: [python3, python3-pip, nginx]
update_cache: true
- name: Create app user
user: { name: nexus, shell: /sbin/nologin, system: true }
- name: Deploy app
copy:
src: ../dist/
dest: /opt/nexus/
owner: nexus
- name: Ensure nginx running
service: { name: nginx, state: started, enabled: true }
"""
|