Text Generation
Transformers
Safetensors
qwen2
rl-swarm
genrl-swarm
grpo
gensyn
I am dappled_exotic_elk
text-generation-inference
Instructions to use ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk") model = AutoModelForCausalLM.from_pretrained("ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk
- SGLang
How to use ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk 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 "ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk" \ --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": "ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk", "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 "ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk" \ --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": "ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk with Docker Model Runner:
docker model run hf.co/ongon/AceInstruct-1.5B-Gensyn-Swarm-dappled_exotic_elk
- Xet hash:
- f1e920657a86563cd68ae99c4fcef4f06d9d038071fc0b638634f7ac3c3a0c8b
- Size of remote file:
- 5 GB
- SHA256:
- 910747509f95ec43a2664dab2a03dd0c5558c0fdfc6d4e25cb6fe03db9d92ae7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.