Instructions to use CLMBR/existential-there-quantifier-transformer-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLMBR/existential-there-quantifier-transformer-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CLMBR/existential-there-quantifier-transformer-3")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CLMBR/existential-there-quantifier-transformer-3") model = AutoModelForCausalLM.from_pretrained("CLMBR/existential-there-quantifier-transformer-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CLMBR/existential-there-quantifier-transformer-3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CLMBR/existential-there-quantifier-transformer-3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CLMBR/existential-there-quantifier-transformer-3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CLMBR/existential-there-quantifier-transformer-3
- SGLang
How to use CLMBR/existential-there-quantifier-transformer-3 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 "CLMBR/existential-there-quantifier-transformer-3" \ --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": "CLMBR/existential-there-quantifier-transformer-3", "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 "CLMBR/existential-there-quantifier-transformer-3" \ --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": "CLMBR/existential-there-quantifier-transformer-3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CLMBR/existential-there-quantifier-transformer-3 with Docker Model Runner:
docker model run hf.co/CLMBR/existential-there-quantifier-transformer-3
Download checkpoint-1831680/training_args.bin from CLMBR/existential-there-quantifier-transformer-3: direct link, hf CLI and curl.
- Browser
- Download file 4.35 kB
-
https://huggingface.co/CLMBR/existential-there-quantifier-transformer-3/resolve/main/checkpoint-1831680/training_args.bin
- Command line
-
hf download hf://CLMBR/existential-there-quantifier-transformer-3/checkpoint-1831680/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CLMBR/existential-there-quantifier-transformer-3/resolve/main/checkpoint-1831680/training_args.bin
4.35 kB
- Xet hash:
- 23525e891986a96aab9bb1b660c99529c25cde5bc248b1e19af2ef84adc31036
- Size of remote file:
- 4.35 kB
- SHA256:
- df1abad225d842e0282b349432c4d273337487f7d12c9b346b66e6350b79f59c
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