Instructions to use JulianKrgd/julian-600m-10b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JulianKrgd/julian-600m-10b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JulianKrgd/julian-600m-10b")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("JulianKrgd/julian-600m-10b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JulianKrgd/julian-600m-10b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JulianKrgd/julian-600m-10b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JulianKrgd/julian-600m-10b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JulianKrgd/julian-600m-10b
- SGLang
How to use JulianKrgd/julian-600m-10b 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 "JulianKrgd/julian-600m-10b" \ --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": "JulianKrgd/julian-600m-10b", "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 "JulianKrgd/julian-600m-10b" \ --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": "JulianKrgd/julian-600m-10b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JulianKrgd/julian-600m-10b with Docker Model Runner:
docker model run hf.co/JulianKrgd/julian-600m-10b
Download model.safetensors from JulianKrgd/julian-600m-10b: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/JulianKrgd/julian-600m-10b/resolve/main/model.safetensors
- Command line
-
hf download hf://JulianKrgd/julian-600m-10b/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/JulianKrgd/julian-600m-10b/resolve/main/model.safetensors
1.2 GB
- Xet hash:
- a5a80a6f39bbb3366e66a795b54d39bfce01c3b10d3998dbcf3ae6885b195677
- Size of remote file:
- 1.2 GB
- SHA256:
- 28547c8f2e66ed86fb6dbc58cfbcd37393771e1218f3d6473e21d0dc576aabb0
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