Text Generation
Transformers
TensorBoard
Safetensors
Chinese
English
code
qwen
lora
repository-understanding
code-assistant
fine-tuning
multi-agent-systems
Eval Results (legacy)
Instructions to use tensense/code_repo_finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensense/code_repo_finetuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tensense/code_repo_finetuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensense/code_repo_finetuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tensense/code_repo_finetuning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensense/code_repo_finetuning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensense/code_repo_finetuning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tensense/code_repo_finetuning
- SGLang
How to use tensense/code_repo_finetuning 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 "tensense/code_repo_finetuning" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensense/code_repo_finetuning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "tensense/code_repo_finetuning" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensense/code_repo_finetuning", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tensense/code_repo_finetuning with Docker Model Runner:
docker model run hf.co/tensense/code_repo_finetuning
|
Download repos/Laddr/docs/getting-started.md from tensense/code_repo_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 2.53 kB
-
https://huggingface.co/tensense/code_repo_finetuning/resolve/main/repos/Laddr/docs/getting-started.md
- Command line
-
hf download hf://tensense/code_repo_finetuning/repos/Laddr/docs/getting-started.md
-
curl -L -o getting-started.md https://huggingface.co/tensense/code_repo_finetuning/resolve/main/repos/Laddr/docs/getting-started.md
2.53 kB
| # Getting Started | |
| This guide walks you through everything you need to get started with the framework — from environment setup to running your first agent. | |
| --- | |
| ## Docker Setup | |
| For the full experience (including the web UI and isolated environments), we recommend using **Docker**. | |
| Download it from the [official Docker website](https://www.docker.com/products/docker-desktop). | |
| --- | |
| ## Python Virtual Environment | |
| Before installing dependencies, it’s best to create a Python virtual environment. | |
| **Create one:** | |
| ```bash | |
| python -m venv venv | |
| ``` | |
| **Activate it:** | |
| On **Windows (Git Bash or CMD)**: | |
| ```bash | |
| .\venv\Scripts\activate | |
| ``` | |
| On **macOS/Linux**: | |
| ```bash | |
| source venv/bin/activate | |
| ``` | |
| --- | |
| ## Installation | |
| Install **Laddr** (CLI, core, and API): | |
| ```bash | |
| pip install laddr | |
| ``` | |
| Or develop against the local repository (editable mode): | |
| ```bash | |
| pip install -e lib/laddr | |
| ``` | |
| --- | |
| ## Create a New Project | |
| Initialize a new project and move into it: | |
| ```bash | |
| laddr init my_agent_system | |
| cd my_agent_system | |
| ``` | |
| The project includes: | |
| - `agents/` — Agent modules | |
| - `workers/` — Worker scripts | |
| - `Dockerfile` — Build configuration | |
| - `docker-compose.yml` — Multi-service orchestration | |
| - `main.py` — Application runner | |
| --- | |
| ## Set API Keys | |
| To enable integrations, add your API keys to a `.env` file in your project root: | |
| ```bash | |
| # .env | |
| GEMINI_API_KEY=your_gemini_api_key | |
| SERPER_API_KEY=your_serper_api_key | |
| ``` | |
| > **Note:** | |
| > - The `web_search` tool requires a Serper API key. | |
| > - Gemini is used for LLM integrations. | |
| > - Set both keys before running the stack. | |
| --- | |
| ## Run the Stack (Docker) | |
| Start the stack using either of these commands: | |
| ```bash | |
| laddr run dev -d | |
| ``` | |
| or | |
| ```bash | |
| docker compose up -d | |
| ``` | |
| Once running, open: | |
| - Dashboard → `http://localhost:5173` | |
| - API → `http://localhost:8000` | |
| To run agents **without Docker**, see [Local Setup](config/local-runtime). | |
| --- | |
| ## Add an Agent and Tool | |
| Create a new agent and attach a tool to it: | |
| ```bash | |
| laddr add agent researcher --role "Researcher" --goal "Find facts" --llm-model gemini-2.5-flash | |
| laddr add tool web_search --agent researcher --description "Search the web" | |
| ``` | |
| --- | |
| ## Quick Run | |
| Run your agent with a quick test command: | |
| ```bash | |
| laddr run researcher '{"topic": "Latest AI agent trends"}' | |
| ``` | |
| This executes a local run of your `researcher` agent using the default configuration. | |
| --- | |
| You’re all set! | |
| You’ve installed **Laddr**, initialized a project, configured API keys, and run your first agent. | |