Instructions to use MultivexAI/Plyx-15M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MultivexAI/Plyx-15M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MultivexAI/Plyx-15M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MultivexAI/Plyx-15M") model = AutoModelForCausalLM.from_pretrained("MultivexAI/Plyx-15M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MultivexAI/Plyx-15M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MultivexAI/Plyx-15M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MultivexAI/Plyx-15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MultivexAI/Plyx-15M
- SGLang
How to use MultivexAI/Plyx-15M 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 "MultivexAI/Plyx-15M" \ --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": "MultivexAI/Plyx-15M", "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 "MultivexAI/Plyx-15M" \ --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": "MultivexAI/Plyx-15M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MultivexAI/Plyx-15M with Docker Model Runner:
docker model run hf.co/MultivexAI/Plyx-15M
Update README.md
Browse files
README.md
CHANGED
|
@@ -26,6 +26,10 @@ The model was trained on a carefully curated mix of data to build a great founda
|
|
| 26 |
|
| 27 |
To set the right expectations: **Plyx-15M is a 15-million-parameter model, which is quite small.** Its performance won't be comparable to models with billions of parameters. It's best used for research, highly specific tasks, or as a base for fine-tuning - not as a drop-in replacement for a large, general-purpose model.
|
| 28 |
|
|
|
|
|
|
|
|
|
|
|
|
|
| 29 |
## License
|
| 30 |
|
| 31 |
The data used for pre-training (`fineweb-pro`, `fineweb-edu`, and `finepdfs`) is derived from sources made available under the **ODC-By 1.0 license**. Users must also abide by the [CommonCrawl Terms of Use](https://commoncrawl.org/terms-of-use/). We do not alter the license of any of the underlying data.
|
|
|
|
| 26 |
|
| 27 |
To set the right expectations: **Plyx-15M is a 15-million-parameter model, which is quite small.** Its performance won't be comparable to models with billions of parameters. It's best used for research, highly specific tasks, or as a base for fine-tuning - not as a drop-in replacement for a large, general-purpose model.
|
| 28 |
|
| 29 |
+
## Limitations
|
| 30 |
+
|
| 31 |
+
Users should be aware of the biases and limitations of this model, as no model is truly perfect.
|
| 32 |
+
|
| 33 |
## License
|
| 34 |
|
| 35 |
The data used for pre-training (`fineweb-pro`, `fineweb-edu`, and `finepdfs`) is derived from sources made available under the **ODC-By 1.0 license**. Users must also abide by the [CommonCrawl Terms of Use](https://commoncrawl.org/terms-of-use/). We do not alter the license of any of the underlying data.
|