Instructions to use swype/deepshard-13B-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swype/deepshard-13B-ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="swype/deepshard-13B-ft")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("swype/deepshard-13B-ft") model = AutoModelForCausalLM.from_pretrained("swype/deepshard-13B-ft") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use swype/deepshard-13B-ft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "swype/deepshard-13B-ft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "swype/deepshard-13B-ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/swype/deepshard-13B-ft
- SGLang
How to use swype/deepshard-13B-ft 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 "swype/deepshard-13B-ft" \ --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": "swype/deepshard-13B-ft", "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 "swype/deepshard-13B-ft" \ --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": "swype/deepshard-13B-ft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use swype/deepshard-13B-ft with Docker Model Runner:
docker model run hf.co/swype/deepshard-13B-ft
Model capabilities
#2
by Whitepaper - opened
Do I understand correctly that your current weights are better than the GPT3.5 in the tests done so far?
Good luck with your project!
The "good" version isn't finished yet. This version still has biases and produces a lot of unwanted text after it's done outputting "good" text.
We're training the next one rn.