Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use my-ai-stack/Stack-2-9-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned") model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned", 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 my-ai-stack/Stack-2-9-finetuned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "my-ai-stack/Stack-2-9-finetuned" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
- SGLang
How to use my-ai-stack/Stack-2-9-finetuned 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 "my-ai-stack/Stack-2-9-finetuned" \ --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": "my-ai-stack/Stack-2-9-finetuned", "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 "my-ai-stack/Stack-2-9-finetuned" \ --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": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
| """ | |
| DevOps Tools Module | |
| Provides cloud and DevOps operation capabilities. | |
| """ | |
| from typing import Dict, List, Optional, Any | |
| import re | |
| class DevOpsTools: | |
| """Cloud and DevOps operation tools.""" | |
| # Cloud provider templates | |
| CLOUD_TEMPLATES = { | |
| "aws": { | |
| "ec2": { | |
| "description": "AWS EC2 instance", | |
| "template": """# AWS EC2 Instance | |
| resource "aws_instance" "app_server" { | |
| ami = "ami-0c55b159cbfafe1f0" | |
| instance_type = "t3.micro" | |
| tags = { | |
| Name = "Stack2.9-App" | |
| } | |
| }""" | |
| }, | |
| "s3": { | |
| "description": "AWS S3 bucket", | |
| "template": """# AWS S3 Bucket | |
| resource "aws_s3_bucket" "data_store" { | |
| bucket = "stack29-data-store" | |
| tags = { | |
| Name = "Stack2.9 Data" | |
| Environment = "production" | |
| } | |
| }""" | |
| }, | |
| "lambda": { | |
| "description": "AWS Lambda function", | |
| "template": """# AWS Lambda Function | |
| resource "aws_lambda_function" "handler" { | |
| filename = "handler.zip" | |
| function_name = "stack29_handler" | |
| role = aws_iam_role.lambda_role.arn | |
| handler = "index.handler" | |
| source_code_hash = filebase64sha256("handler.zip") | |
| runtime = "python3.9" | |
| }""" | |
| }, | |
| }, | |
| "gcp": { | |
| "compute": { | |
| "description": "GCP Compute Engine", | |
| "template": """# GCP Compute Engine | |
| resource "google_compute_instance" "vm_instance" { | |
| name = "stack29-vm" | |
| machine_type = "e2-micro" | |
| zone = "us-central1-a" | |
| boot_disk { | |
| initialize_params { | |
| image = "debian-cloud/debian-11" | |
| } | |
| } | |
| network_interface { | |
| network = "default" | |
| } | |
| }""" | |
| }, | |
| "storage": { | |
| "description": "GCP Cloud Storage", | |
| "template": """# GCP Cloud Storage | |
| resource "google_storage_bucket" "bucket" { | |
| name = "stack29-bucket" | |
| location = "US" | |
| force_destroy = false | |
| labels = { | |
| environment = "production" | |
| } | |
| }""" | |
| }, | |
| }, | |
| "docker": { | |
| "container": { | |
| "description": "Docker container configuration", | |
| "template": """# Dockerfile | |
| FROM python:3.11-slim | |
| WORKDIR /app | |
| # Install dependencies | |
| COPY requirements.txt . | |
| RUN pip install --no-cache-dir -r requirements.txt | |
| # Copy application | |
| COPY . . | |
| # Run application | |
| CMD ["python", "main.py"]""" | |
| }, | |
| "compose": { | |
| "description": "Docker Compose configuration", | |
| "template": """# docker-compose.yml | |
| version: '3.8' | |
| services: | |
| app: | |
| build: . | |
| ports: | |
| - "8000:8000" | |
| environment: | |
| - DATABASE_URL=postgres://db:5432/app | |
| depends_on: | |
| - db | |
| - redis | |
| db: | |
| image: postgres:15 | |
| environment: | |
| - POSTGRES_DB=app | |
| - POSTGRES_PASSWORD=secret | |
| redis: | |
| image: redis:7-alpine | |
| ports: | |
| - "6379:6379" | |
| """ | |
| }, | |
| }, | |
| "kubernetes": { | |
| "deployment": { | |
| "description": "Kubernetes Deployment", | |
| "template": """# k8s-deployment.yaml | |
| apiVersion: apps/v1 | |
| kind: Deployment | |
| metadata: | |
| name: stack29-app | |
| labels: | |
| app: stack29 | |
| spec: | |
| replicas: 3 | |
| selector: | |
| matchLabels: | |
| app: stack29 | |
| template: | |
| metadata: | |
| labels: | |
| app: stack29 | |
| spec: | |
| containers: | |
| - name: app | |
| image: stack29:latest | |
| ports: | |
| - containerPort: 8000 | |
| resources: | |
| limits: | |
| cpu: "500m" | |
| memory: "256Mi" | |
| """ | |
| }, | |
| "service": { | |
| "description": "Kubernetes Service", | |
| "template": """# k8s-service.yaml | |
| apiVersion: v1 | |
| kind: Service | |
| metadata: | |
| name: stack29-service | |
| spec: | |
| selector: | |
| app: stack29 | |
| ports: | |
| - protocol: TCP | |
| port: 80 | |
| targetPort: 8000 | |
| type: LoadBalancer | |
| """ | |
| }, | |
| }, | |
| } | |
| # CI/CD templates | |
| CICD_TEMPLATES = { | |
| "github_actions": { | |
| "description": "GitHub Actions workflow", | |
| "template": """# .github/workflows/ci.yml | |
| name: CI | |
| on: | |
| push: | |
| branches: [ main ] | |
| pull_request: | |
| branches: [ main ] | |
| jobs: | |
| test: | |
| runs-on: ubuntu-latest | |
| steps: | |
| - uses: actions/checkout@v3 | |
| - name: Set up Python | |
| uses: actions/setup-python@v4 | |
| with: | |
| python-version: '3.11' | |
| - name: Install dependencies | |
| run: | | |
| pip install -r requirements.txt | |
| - name: Run tests | |
| run: | | |
| pytest tests/ | |
| - name: Lint | |
| run: | | |
| ruff check . | |
| """ | |
| }, | |
| "gitlab_ci": { | |
| "description": "GitLab CI pipeline", | |
| "template": """# .gitlab-ci.yml | |
| stages: | |
| - test | |
| - build | |
| - deploy | |
| test: | |
| stage: test | |
| script: | |
| - pip install -r requirements.txt | |
| - pytest tests/ | |
| rules: | |
| - if: $CI_PIPELINE_SOURCE == "merge_request_event" | |
| build: | |
| stage: build | |
| script: | |
| - docker build -t stack29:$CI_COMMIT_SHA . | |
| rules: | |
| - if: $CI_COMMIT_BRANCH == "main" | |
| deploy: | |
| stage: deploy | |
| script: | |
| - kubectl apply -f k8s/ | |
| environment: | |
| name: production | |
| rules: | |
| - if: $CI_COMMIT_BRANCH == "main" | |
| """ | |
| }, | |
| } | |
| # Infrastructure as Code templates | |
| TERRAFORM_VARIABLES = { | |
| "description": "Terraform variables", | |
| "template": """# variables.tf | |
| variable "region" { | |
| description = "AWS region" | |
| type = string | |
| default = "us-east-1" | |
| } | |
| variable "environment" { | |
| description = "Environment name" | |
| type = string | |
| default = "production" | |
| } | |
| variable "instance_type" { | |
| description = "EC2 instance type" | |
| type = string | |
| default = "t3.micro" | |
| } | |
| """ | |
| } | |
| def __init__(self): | |
| """Initialize DevOps tools.""" | |
| pass | |
| def get_cloud_template( | |
| self, | |
| provider: str, | |
| service: str, | |
| ) -> Optional[str]: | |
| """Get a cloud infrastructure template.""" | |
| return self.CLOUD_TEMPLATES.get(provider, {}).get(service, {}).get("template") | |
| def get_cicd_template(self, platform: str) -> Optional[str]: | |
| """Get a CI/CD pipeline template.""" | |
| return self.CICD_TEMPLATES.get(platform, {}).get("template") | |
| def list_available_templates(self) -> Dict[str, List[str]]: | |
| """List all available templates.""" | |
| return { | |
| "cloud_providers": list(self.CLOUD_TEMPLATES.keys()), | |
| "cicd": list(self.CICD_TEMPLATES.keys()), | |
| } | |
| def generate_kubernetes_manifest( | |
| self, | |
| app_name: str, | |
| image: str, | |
| replicas: int = 3, | |
| port: int = 8000, | |
| ) -> str: | |
| """Generate a Kubernetes deployment manifest.""" | |
| return f"""apiVersion: apps/v1 | |
| kind: Deployment | |
| metadata: | |
| name: {app_name} | |
| labels: | |
| app: {app_name} | |
| spec: | |
| replicas: {replicas} | |
| selector: | |
| matchLabels: | |
| app: {app_name} | |
| template: | |
| metadata: | |
| labels: | |
| app: {app_name} | |
| spec: | |
| containers: | |
| - name: {app_name} | |
| image: {image} | |
| ports: | |
| - containerPort: {port} | |
| resources: | |
| limits: | |
| cpu: "1000m" | |
| memory: "512Mi" | |
| requests: | |
| cpu: "100m" | |
| memory: "128Mi" | |
| --- | |
| apiVersion: v1 | |
| kind: Service | |
| metadata: | |
| name: {app_name}-service | |
| spec: | |
| selector: | |
| app: {app_name} | |
| ports: | |
| - protocol: TCP | |
| port: 80 | |
| targetPort: {port} | |
| type: LoadBalancer | |
| """ | |
| def generate_dockerfile( | |
| self, | |
| language: str = "python", | |
| version: str = "3.11", | |
| port: int = 8000, | |
| ) -> str: | |
| """Generate a Dockerfile.""" | |
| base_images = { | |
| "python": f"python:{version}-slim", | |
| "node": f"node:{version}-slim", | |
| "go": f"golang:{version}", | |
| "rust": f"rust:{version}-slim", | |
| } | |
| base = base_images.get(language, f"python:{version}-slim") | |
| return f"""FROM {base} | |
| WORKDIR /app | |
| # Install dependencies | |
| COPY requirements.txt . | |
| RUN pip install --no-cache-dir -r requirements.txt | |
| # Copy application | |
| COPY . . | |
| # Expose port | |
| EXPOSE {port} | |
| # Run application | |
| CMD ["python", "main.py"] | |
| """ | |
| def parse_docker_compose(self, compose_content: str) -> Dict[str, Any]: | |
| """Parse docker-compose content to extract services.""" | |
| services = re.findall(r'^ (\w+):$', compose_content, re.MULTILINE) | |
| return {"services": services, "count": len(services)} | |
| def __repr__(self) -> str: | |
| templates = self.list_available_templates() | |
| return f"DevOpsTools(cloud={templates['cloud_providers']}, cicd={templates['cicd']})" |