Instructions to use Xwin-LM/Xwin-LM-13B-V0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xwin-LM/Xwin-LM-13B-V0.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Xwin-LM/Xwin-LM-13B-V0.2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Xwin-LM/Xwin-LM-13B-V0.2") model = AutoModelForCausalLM.from_pretrained("Xwin-LM/Xwin-LM-13B-V0.2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Xwin-LM/Xwin-LM-13B-V0.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Xwin-LM/Xwin-LM-13B-V0.2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xwin-LM/Xwin-LM-13B-V0.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Xwin-LM/Xwin-LM-13B-V0.2
- SGLang
How to use Xwin-LM/Xwin-LM-13B-V0.2 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 "Xwin-LM/Xwin-LM-13B-V0.2" \ --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": "Xwin-LM/Xwin-LM-13B-V0.2", "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 "Xwin-LM/Xwin-LM-13B-V0.2" \ --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": "Xwin-LM/Xwin-LM-13B-V0.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Xwin-LM/Xwin-LM-13B-V0.2 with Docker Model Runner:
docker model run hf.co/Xwin-LM/Xwin-LM-13B-V0.2
You're insane
I've been extremely impressed with the result from the xwin models. The 0.2 version of 13b has been great, looking forward to the 70b.
I have tested the Mistral Orca model, and while it provides good performance, it tends to repeat itself frequently and
lacks depth in its responses. In comparison, the AI model I have been using for this project, version 13b, offers more
varied and indexed answers, and it appears to have a preference for phrasing things in the form of equations. However,
the model is not always accurate in the relationships between variables, especially in complex scenarios. Despite this,
it is an impressively strong model that performs exceptionally well.
According to the benchmarks, the 13b model outperforms some 70b models, which is truly remarkable. Additionally, there
is a 70b weights version available, which should be approaching light speed in terms of performance. I encourage you to
explore these models and their capabilities further, as they have shown significant potential.
