Text Generation
Transformers
Safetensors
ouro
looped-language-model
reasoning
recurrent-depth
conversational
custom_code
Instructions to use Matt300209/Ouro-1B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Matt300209/Ouro-1B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Matt300209/Ouro-1B-Base", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Matt300209/Ouro-1B-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Matt300209/Ouro-1B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Matt300209/Ouro-1B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Matt300209/Ouro-1B-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Matt300209/Ouro-1B-Base
- SGLang
How to use Matt300209/Ouro-1B-Base 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 "Matt300209/Ouro-1B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Matt300209/Ouro-1B-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Matt300209/Ouro-1B-Base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Matt300209/Ouro-1B-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Matt300209/Ouro-1B-Base with Docker Model Runner:
docker model run hf.co/Matt300209/Ouro-1B-Base
Download model.safetensors from Matt300209/Ouro-1B-Base: direct link, hf CLI and curl.
- Browser
- Download file 2.87 GB
-
https://huggingface.co/Matt300209/Ouro-1B-Base/resolve/main/model.safetensors
- Command line
-
hf download hf://Matt300209/Ouro-1B-Base/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Matt300209/Ouro-1B-Base/resolve/main/model.safetensors
2.87 GB
- Xet hash:
- c99031baf4ea1fe9be29455f758e35c150c00b2ca160a4dab071c8bb97c322e7
- Size of remote file:
- 2.87 GB
- SHA256:
- 58872a72616c736595b8b7662079c5b12c5a162ec16eae94f21c348dfa9885af
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