Instructions to use ulas742/kl_gm_005 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ulas742/kl_gm_005 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ulas742/kl_gm_005")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ulas742/kl_gm_005") model = AutoModelForCausalLM.from_pretrained("ulas742/kl_gm_005", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ulas742/kl_gm_005 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ulas742/kl_gm_005" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ulas742/kl_gm_005", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ulas742/kl_gm_005
- SGLang
How to use ulas742/kl_gm_005 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 "ulas742/kl_gm_005" \ --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": "ulas742/kl_gm_005", "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 "ulas742/kl_gm_005" \ --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": "ulas742/kl_gm_005", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ulas742/kl_gm_005 with Docker Model Runner:
docker model run hf.co/ulas742/kl_gm_005
Download trainer_state.json from ulas742/kl_gm_005: direct link, hf CLI and curl.
- Browser
- Download file 163 kB
-
https://huggingface.co/ulas742/kl_gm_005/resolve/main/trainer_state.json
- Command line
-
hf download hf://ulas742/kl_gm_005/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/ulas742/kl_gm_005/resolve/main/trainer_state.json
163 kB
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