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
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**Usage**:
```console
$ laddr [OPTIONS] COMMAND [ARGS]...
```
**Options**:
* `--help`: Show this message and exit.
**Commands**:
* `init`: Initialize a new laddr project
* `add agent`: Add a new agent to your project
* `add tool`: Add a new tool to an existing agent
* `run`: Run an agent or coordinator locally
* `check`: Validate project structure and configuration
* `infra up`: Start infrastructure services
* `infra down`: Stop infrastructure services
* `worker`: Start a worker process for an agent
* `prompt view`: View an agent’s system prompt
* `prompt edit`: Edit an agent’s system prompt
---
## `laddr init`
Initialize a new laddr project.
**Usage**:
```console
$ laddr init my-project
$ cd my-project
```
**Creates:**
- `laddr.yml` – Main config
- `.env` – Environment variables (Redis, Postgres, MinIO)
- `agents/` – Agent folders
- `workers/` – Worker modules
**Options:**
- Project is created in a subdirectory with the given name.
---
## `laddr add agent`
Add a new agent to your project.
**Usage**:
```console
$ laddr add agent researcher
```
**Creates:**
- `agents/researcher/handler.py` – Agent handler with tools
- `agents/researcher/prompt.md` – System prompt
**Options:**
- Agent name must be lowercase, `snake_case`.
---
## `laddr add tool`
Add a new tool to an existing agent.
**Usage**:
```console
$ laddr add tool researcher
```
**Creates:**
Tool files inside the specified agent directory and updates `handler.py`.
**Options:**
- Tool name must be lowercase, `snake_case`.
---
## `laddr run`
Run an agent or coordinator locally (without Redis/Postgres).
**Usage**:
```console
$ laddr run researcher "Search for latest AI news"
```
**Options:**
- `--trace` – Enable tracing output
- `--trace-mask <tool1,tool2>` – Mask specific tools from traces
**Environment:**
- Uses in-memory implementations (no Redis/Postgres required)
- Loads `.env` for LLM credentials.
---
## `laddr check`
Validate project structure and configuration.
**Usage**:
```console
$ laddr check
```
**Verifies:**
- All agents have `handler.py` and `prompt.md`
- All tools are valid Python modules
- Environment variables are set correctly
- `laddr.yml` structure is valid.
---
## `laddr infra up`
Start infrastructure services (Redis, Postgres, MinIO) via Docker Compose.
**Usage**:
```console
$ laddr infra up
```
**Options:**
- `-d`, `--detach` – Run in background.
---
## `laddr infra down`
Stop infrastructure services.
**Usage**:
```console
$ laddr infra down
```
---
## `laddr worker`
Start a worker process for an agent.
**Usage**:
```console
$ laddr worker researcher
```
**Options:**
- Connects to Redis queue specified in `laddr.yml`
- Loads agent from `agents/researcher/handler.py`
- Processes tasks from the coordinator.
---
## `laddr prompt view`
View an agent’s system prompt.
**Usage**:
```console
$ laddr prompt view researcher
```
---
## `laddr prompt edit`
Edit an agent’s system prompt.
**Usage**:
```console
$ laddr prompt edit researcher
```
**Behavior:**
- Opens `prompt.md` in your default editor.
---
## Edge Cases
### Running without infrastructure
For local testing without Redis/Postgres:
```console
$ laddr run researcher "test query"
```
Uses in-memory implementations and does not require `laddr infra up`.
### Tracing specific agents
Enable tracing for debugging:
```console
$ laddr run researcher "query" --trace
```
Mask noisy tools:
```console
$ laddr run researcher "query" --trace --trace-mask web_search,file_read
```
### Custom environment file
Use a custom `.env` file:
```console
$ export LADDR_ENV_FILE=.env.production
$ laddr check
```
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