Text Generation
Transformers
Safetensors
qwen3
llama-factory
full
Generated from Trainer
conversational
text-generation-inference
Instructions to use ayh015/myLightningOPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ayh015/myLightningOPD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ayh015/myLightningOPD") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ayh015/myLightningOPD") model = AutoModelForCausalLM.from_pretrained("ayh015/myLightningOPD", 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 ayh015/myLightningOPD with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ayh015/myLightningOPD" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ayh015/myLightningOPD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ayh015/myLightningOPD
- SGLang
How to use ayh015/myLightningOPD 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 "ayh015/myLightningOPD" \ --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": "ayh015/myLightningOPD", "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 "ayh015/myLightningOPD" \ --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": "ayh015/myLightningOPD", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ayh015/myLightningOPD with Docker Model Runner:
docker model run hf.co/ayh015/myLightningOPD
| # Rollout Buffer | |
| ## Overview | |
| Rollout Buffer is an independent component for asynchronous agent trajectory generation, with the main function of using the LLM OpenAI Server launched by slime training to generate agent trajectories. | |
| ### Workflow | |
| ``` | |
| slime Training Process ββββ HTTP API ββββ Rollout Buffer | |
| β β | |
| LLM Server ββββββββ HTTP Requests βββββββ Agent Framework | |
| β β | |
| Model Response βββββββββββββββββββββββ Trajectory Generation | |
| ``` | |
| For each different Agent task, there should be a corresponding independent Generator class, responsible for generating trajectories for that type of task. Rollout Buffer automatically reads and loads different types of Generators. | |
| ## Quick Start | |
| ### Basic Usage Process | |
| 1. **Copy Template**: Copy `base_generator.py` as a template | |
| 2. **Modify Task Type**: Change `TASK_TYPE` to your task name (cannot duplicate with other Generators) | |
| 3. **Implement Core Function**: Implement the `run_rollout()` function | |
| 4. **Optional Customization**: Rewrite five optional functions as needed | |
| Generator files must end with `_generator.py` and be placed in the `generator/` directory: | |
| ``` | |
| generator/ | |
| βββ base_generator.py # Math task implementation (default template) | |
| βββ your_task_generator.py # Your custom task | |
| ``` | |
| Each Generator file must define `TASK_TYPE` and `run_rollout()`. | |
| In addition, Rollout Buffer also provides some customizable functions to meet special needs of different tasks. If no custom implementation is provided, the system will use default implementations (located in `slime_plugins/rollout_buffer/default_func.py`). | |
| ### Example Script | |
| First, you need to follow [Example: Qwen3-4B Model](../../docs/en/models/qwen3-4B.md) to configure the environment, download data and convert model checkpoints. And then run the following scripts: | |
| ```bash | |
| cd slime_plugins/rollout_buffer | |
| bash rollout_buffer_example.sh | |
| # In a different terminal | |
| python buffer.py | |
| ``` | |