Instructions to use IlyasMoutawwakil/tiny-random-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IlyasMoutawwakil/tiny-random-llama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IlyasMoutawwakil/tiny-random-llama")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IlyasMoutawwakil/tiny-random-llama") model = AutoModelForCausalLM.from_pretrained("IlyasMoutawwakil/tiny-random-llama", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IlyasMoutawwakil/tiny-random-llama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IlyasMoutawwakil/tiny-random-llama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IlyasMoutawwakil/tiny-random-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IlyasMoutawwakil/tiny-random-llama
- SGLang
How to use IlyasMoutawwakil/tiny-random-llama 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 "IlyasMoutawwakil/tiny-random-llama" \ --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": "IlyasMoutawwakil/tiny-random-llama", "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 "IlyasMoutawwakil/tiny-random-llama" \ --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": "IlyasMoutawwakil/tiny-random-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IlyasMoutawwakil/tiny-random-llama with Docker Model Runner:
docker model run hf.co/IlyasMoutawwakil/tiny-random-llama
Download pytorch_model.bin from IlyasMoutawwakil/tiny-random-llama: direct link, hf CLI and curl.
- Browser
- Download file 4.14 MB
-
https://huggingface.co/IlyasMoutawwakil/tiny-random-llama/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://IlyasMoutawwakil/tiny-random-llama/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/IlyasMoutawwakil/tiny-random-llama/resolve/main/pytorch_model.bin
4.14 MB
- Xet hash:
- 94d13cc3364a308edf72c530e81be3e19d85b7dd2b67db8e6f3441a0df517a6d
- Size of remote file:
- 4.14 MB
- SHA256:
- 484092e9e0379098e916f4a50c63c73fcd6238375f434d57dcde6993eec52635
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