Text Generation
Transformers
Safetensors
llama
llama2
fused
cpu
context-8000
fusion-all2one
tensor-fusion
bias-removal
decode
coherence-enhancement
logging
custom-code
text-generation-inference
Instructions to use jnjj/xddd_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jnjj/xddd_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jnjj/xddd_v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("jnjj/xddd_v1") model = AutoModelForMultimodalLM.from_pretrained("jnjj/xddd_v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jnjj/xddd_v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jnjj/xddd_v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jnjj/xddd_v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jnjj/xddd_v1
- SGLang
How to use jnjj/xddd_v1 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 "jnjj/xddd_v1" \ --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": "jnjj/xddd_v1", "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 "jnjj/xddd_v1" \ --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": "jnjj/xddd_v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jnjj/xddd_v1 with Docker Model Runner:
docker model run hf.co/jnjj/xddd_v1
- Xet hash:
- c533a424c07bdd20692fdf85976fa80f92431c664a43b4660cb1a156da4f2192
- Size of remote file:
- 5 GB
- SHA256:
- 7d1e9a6fa648259147ba5527197bdffcdaad1b81d742c161b5d0d5ef66c346ca
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