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/api-reference.md from tensense/code_repo_finetuning: direct link, hf CLI and curl.
- Browser
- Download file 7.97 kB
-
https://huggingface.co/tensense/code_repo_finetuning/resolve/main/repos/Laddr/docs/api-reference.md
- Command line
-
hf download hf://tensense/code_repo_finetuning/repos/Laddr/docs/api-reference.md
-
curl -L -o api-reference.md https://huggingface.co/tensense/code_repo_finetuning/resolve/main/repos/Laddr/docs/api-reference.md
7.97 kB
| # Laddr REST API | |
| **Base URL:** `http://localhost:8000` | |
| --- | |
| ## `GET /` | |
| Returns basic API information such as name, version, and documentation links. | |
| **Response Example:** | |
| ```json | |
| { | |
| "service": "Laddr API", | |
| "version": "0.8.0", | |
| "status": "running", | |
| "dashboard": "http://localhost:5173", | |
| "docs": "/docs" | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/health` | |
| Checks the health of the Laddr system and its connected services such as the database, message bus, and storage backend. | |
| **Response Example:** | |
| ```json | |
| { | |
| "status": "ok", | |
| "version": "0.8.0", | |
| "components": { | |
| "database": db_type, | |
| "storage": storage_type, | |
| "message_bus": queue_type | |
| } | |
| } | |
| ``` | |
| --- | |
| ## `POST /api/jobs` | |
| Submit a new job for execution by providing the pipeline name and input data. | |
| **Request Body Example:** | |
| ```json | |
| { | |
| "pipeline_name": "text_analysis_agent", | |
| "inputs": { | |
| "text": "Analyze this message for sentiment" | |
| } | |
| } | |
| ``` | |
| **Response Example:** | |
| ```json | |
| { | |
| "job_id": "job_1b203f", | |
| "status": "queued", | |
| "created_at": "2025-11-08T18:45:00Z" | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/jobs` | |
| Fetch a list of recent jobs with pagination support. | |
| **Query Parameters:** | |
| - `limit` (optional): Number of jobs to return (default: 50) | |
| - `offset` (optional): Skip a number of jobs (default: 0) | |
| **Response Example:** | |
| ```json | |
| [ | |
| { | |
| "job_id": "job_1b203f", | |
| "pipeline_name": "text_analysis_agent", | |
| "status": "completed", | |
| "created_at": "2025-11-08T18:45:00Z" | |
| }, | |
| { | |
| "job_id": "job_1b202b", | |
| "pipeline_name": "image_classification_agent", | |
| "status": "failed", | |
| "created_at": "2025-11-08T18:40:00Z" | |
| } | |
| ] | |
| ``` | |
| --- | |
| ## `GET /api/jobs/{job_id}` | |
| Retrieve details for a specific job, including inputs, outputs, and execution metadata. | |
| **Response Example:** | |
| ```json | |
| { | |
| "job_id": "uuid", | |
| "status": "completed", | |
| "pipeline_name": "analyzer", | |
| "inputs": {"numbers": [1,2,3,4,5]}, | |
| "outputs": {"sum": 15, "average": 3.0}, | |
| "error": null, | |
| "created_at": "2025-11-03T16:40:04.630542Z", | |
| "completed_at": "2025-11-03T16:40:05.234720Z", | |
| "token_usage": { | |
| "prompt_tokens": 100, | |
| "completion_tokens": 50, | |
| "total_tokens": 150 | |
| } | |
| } | |
| ``` | |
| --- | |
| ## `POST /api/jobs/{job_id}/replay` | |
| Replay a previous job. | |
| **Request Body Example:** | |
| ```json | |
| { | |
| "job_id": "...", | |
| "request: True | |
| } | |
| ``` | |
| **Response Example:** | |
| ```json | |
| { | |
| "job_id": "uuid (new or same)", | |
| "status": "completed", | |
| "result": "Job result", | |
| "replayed": true, | |
| "original_job_id": "original-uuid" | |
| } | |
| ``` | |
| --- | |
| ## `POST /api/prompts` | |
| Submit a new prompt execution request. | |
| Supports both **single-agent** and **sequential multi-agent** execution modes. | |
| **Request Body Example:** | |
| ```json | |
| { | |
| "prompt_name": "writer", | |
| "inputs": {"task": "Write a haiku"} | |
| "mode": "single" // or "sequential" | |
| "agents": ["analyzer", "writer"] // optional for sequential | |
| } | |
| ``` | |
| **Response Example:** | |
| ```json | |
| { | |
| "prompt_id": "uuid", | |
| "status": "running", | |
| "agent": "writer", | |
| "mode": "single", | |
| "agents": ["writer"] | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/prompts?limit=50` | |
| Retrieve a list of recent prompt executions. | |
| **Query Parameters:** | |
| - `limit` (optional): Number of prompts to return (default: 50) | |
| - `offset` (optional): Skip a number of prompts (default: 0) | |
| **Response Example:** | |
| ```json | |
| { | |
| "prompts": [ | |
| { | |
| "prompt_id": "uuid", | |
| "status": "completed", | |
| "prompt_name": "writer", | |
| "created_at": "2025-11-03T16:39:52.113651Z", | |
| "completed_at": "2025-11-03T16:39:54.234720Z" | |
| } | |
| ], | |
| "limit": 50 | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/prompts/{prompt_id}` | |
| Get the current status, outputs, and trace summary of a specific prompt execution. | |
| **Response Example:** | |
| ```json | |
| { | |
| "prompt_id": "uuid", | |
| "status": "completed", | |
| "prompt_name": "writer", | |
| "inputs": {"task": "Write a haiku"}, | |
| "outputs": {"result": "Haiku text..."}, | |
| "error": null, | |
| "created_at": "2025-11-03T16:39:52.113651Z", | |
| "completed_at": "2025-11-03T16:39:54.234720Z", | |
| "token_usage": { | |
| "prompt_tokens": 150, | |
| "completion_tokens": 75, | |
| "total_tokens": 225 | |
| } | |
| } | |
| ``` | |
| ## `GET /api/agents` | |
| List all registered agents with metadata and available tools. | |
| **Response Example:** | |
| ```json | |
| { | |
| "agents": [ | |
| { | |
| "name": "writer", | |
| "role": "Content Writer", | |
| "goal": "Generate high-quality content", | |
| "status": "active", | |
| "tools": ["format_json", "parse_csv"], | |
| "last_seen": "2025-11-03T16:30:00.000000Z" | |
| } | |
| ] | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/agents/{agent_name}/chat` | |
| Send a chat message to a specific agent and receive its reply. | |
| **Query Parameters:** | |
| - `message`: The text message to send | |
| - `timeout`: Optional response timeout (seconds) | |
| - `wait`: boolean value | |
| **Response Example:** | |
| ```json | |
| // wait=true (successful) | |
| { | |
| "task_id": "uuid", | |
| "status": "completed", | |
| "result": "Agent's response text or structured data", | |
| "agent": "writer" | |
| } | |
| // wait=false (async) | |
| { | |
| "task_id": "uuid", | |
| "status": "submitted" | |
| } | |
| // timeout | |
| { | |
| "task_id": "uuid", | |
| "status": "timeout", | |
| "message": "Agent did not respond in time" | |
| } | |
| ``` | |
| ## `GET /api/traces` | |
| Retrieve a list of trace events across all agents and jobs. | |
| **Query Parameters:** | |
| - `limit` (optional): Number of traces to return | |
| - `job_id` (optional): Filter by job ID | |
| - `agent` (optional): Filter by agent name | |
| **Response Example:** | |
| ```json | |
| { | |
| "traces": [ | |
| { | |
| "id": 510, | |
| "job_id": "d780d103-a860-4536-98a7-1deed7097cb9", | |
| "agent_name": "writer", | |
| "event_type": "task_error", | |
| "payload": { | |
| "error": "LLM generation failed: ...", | |
| "worker": "writer", | |
| "ended_at": "2025-11-03T16:40:07.323933Z" | |
| }, | |
| "timestamp": "2025-11-03T16:40:07.324541Z" | |
| } | |
| ] | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/traces/grouped?limit=50` | |
| Retrieve traces grouped by job ID, showing all events related to each execution. | |
| **Query Parameters:** | |
| - `limit` (optional): Number of traces to return | |
| **Response Example:** | |
| ```json | |
| { | |
| "grouped_traces": [ | |
| { | |
| "job_id": "d780d103-a860-4536-98a7-1deed7097cb9", | |
| "trace_count": 2, | |
| "agents": ["writer"], | |
| "start_time": "2025-11-03T16:40:07.315138Z", | |
| "end_time": "2025-11-03T16:40:07.324541Z", | |
| "traces": [ | |
| { | |
| "id": 509, | |
| "job_id": "d780d103-a860-4536-98a7-1deed7097cb9", | |
| "agent_name": "writer", | |
| "event_type": "task_start", | |
| "payload": {...}, | |
| "timestamp": "2025-11-03T16:40:07.315138Z" | |
| }, | |
| { | |
| "id": 510, | |
| "job_id": "d780d103-a860-4536-98a7-1deed7097cb9", | |
| "agent_name": "writer", | |
| "event_type": "task_error", | |
| "payload": {...}, | |
| "timestamp": "2025-11-03T16:40:07.324541Z" | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/traces/{trace_id}` | |
| Retrieve details of a specific trace, including payload, timestamps, and linked job. | |
| **Response Example:** | |
| ```json | |
| { | |
| "id": 510, | |
| "job_id": "d780d103-a860-4536-98a7-1deed7097cb9", | |
| "agent_name": "writer", | |
| "event_type": "task_error", | |
| "payload": { | |
| "error": "LLM generation failed: openai package not installed", | |
| "worker": "writer", | |
| "ended_at": "2025-11-03T16:40:07.323933Z" | |
| }, | |
| "timestamp": "2025-11-03T16:40:07.324541Z" | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/metrics` | |
| Get aggregated system metrics. | |
| **Response Example:** | |
| ```json | |
| { | |
| "total_jobs": 16, | |
| "avg_latency_ms": 0, | |
| "active_agents_count": 5, | |
| "cache_hits": 0, | |
| "tool_calls": 0, | |
| "timestamp": "2025-11-03T16:40:30.429696Z" | |
| } | |
| ``` | |
| --- | |
| ## `GET /api/responses/{task_id}/resolved` | |
| Retrieve a resolved response payload, including any data that was offloaded to object storage. | |
| **Response Example:** | |
| ```json | |
| // Inline response | |
| { | |
| "task_id": "uuid", | |
| "offloaded": false, | |
| "pointer": null, | |
| "data": { | |
| "result": "Agent response data" | |
| } | |
| } | |
| // Offloaded response | |
| { | |
| "task_id": "uuid", | |
| "offloaded": true, | |
| "pointer": { | |
| "bucket": "laddr", | |
| "key": "responses/task-uuid", | |
| "size_bytes": 524288 | |
| }, | |
| "data": { | |
| "result": "Large agent response data..." | |
| } | |
| } | |
| ``` | |