Instructions to use Malte0621/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Malte0621/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Malte0621/test")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Malte0621/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Malte0621/test with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Malte0621/test" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Malte0621/test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Malte0621/test
- SGLang
How to use Malte0621/test 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 "Malte0621/test" \ --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": "Malte0621/test", "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 "Malte0621/test" \ --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": "Malte0621/test", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Malte0621/test with Docker Model Runner:
docker model run hf.co/Malte0621/test
| pipeline_tag: text-generation | |
| library_name: transformers | |
| language: | |
| - en | |
| license: fair-noncommercial-research-license | |
| datasets: | |
| - malte0621/test-dataset | |
| # Test Model | |
| This is a model created for testing purposes. It is one of the smallest models available on Hugging Face, designed to experiment with the capabilities of small/tiny models. | |
| ## Model Structure | |
| The model is structured to handle text generation tasks. It is trained on a small dataset that consists of user prompts and AI responses, allowing it to generate text based on given inputs. | |
| ## Usage | |
| You can use this model for text generation tasks, particularly for testing and experimentation with small models. It is not intended for production use or serious applications. | |
| ### Prompt format | |
| ``` | |
| <|user|>Your input prompt here<|ai|>The model's response will be generated here<|stop|> | |
| ``` | |
| ### Example Usage | |
| #### Transformers | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "Malte0621/test-model" | |
| model = AutoModelForCausalLM.from_pretrained(model_name, gguf_file="model.gguf") | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| prompt = "<|user|>Hi there!<|ai|>" | |
| response = model.generate(tokenizer(prompt, return_tensors="pt").input_ids) | |
| print(tokenizer.decode(response[0], skip_special_tokens=True)) | |
| ``` | |
| #### llama.cpp | |
| ```bash | |
| ./llama-cli -m model.gguf -p "<|user|>Hi there!<|ai|>" -n 128 -no-cnv --rope-freq-scale 0.125 | |
| ``` | |
| #### LM Studio | |
| https://model.lmstudio.ai/download/Malte0621/test | |
| **(Make sure to set the RoPE frequency scale to 0.125 in the model settings.)** | |
| ## License | |
| This model is released under the [Fair Noncommercial Research License](https://huggingface.co/Malte0621/test/blob/main/LICENSE). | |
| ## Citation | |
| If you use this model in your research, please cite it as follows: | |
| ``` | |
| @misc{test-model, | |
| author = {Malte0621}, | |
| title = {Test-Model}, | |
| year = {2025}, | |
| url = {https://huggingface.co/Malte0621/test} | |
| } | |
| ``` | |
| ## Acknowledgements | |
| This model was created as part of a personal project to explore the capabilities of small language models. It is not affiliated with any organization or commercial entity. |