Instructions to use bane007/causal_lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bane007/causal_lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bane007/causal_lm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("bane007/causal_lm") model = AutoModelForCausalLM.from_pretrained("bane007/causal_lm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bane007/causal_lm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bane007/causal_lm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bane007/causal_lm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bane007/causal_lm
- SGLang
How to use bane007/causal_lm 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 "bane007/causal_lm" \ --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": "bane007/causal_lm", "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 "bane007/causal_lm" \ --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": "bane007/causal_lm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bane007/causal_lm with Docker Model Runner:
docker model run hf.co/bane007/causal_lm
Download training_args.bin from bane007/causal_lm: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/bane007/causal_lm/resolve/main/training_args.bin
- Command line
-
hf download hf://bane007/causal_lm/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bane007/causal_lm/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 817760b46e07f71202754f95153cce0dd20206a9e4bd534d46633fd5ce562360
- Size of remote file:
- 5.3 kB
- SHA256:
- cf60878daaa253ea4f0a6899153c688f5b71b0bd6dad899ece00f7856f08498f
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