Text Generation
Transformers
Safetensors
ouro
looped-language-model
reasoning
recurrent-depth
thinking
chain-of-thought
conversational
custom_code
Instructions to use lettersandpatterns/Ouro-1.4B-Thinking-patched with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lettersandpatterns/Ouro-1.4B-Thinking-patched with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lettersandpatterns/Ouro-1.4B-Thinking-patched", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("lettersandpatterns/Ouro-1.4B-Thinking-patched", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lettersandpatterns/Ouro-1.4B-Thinking-patched with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lettersandpatterns/Ouro-1.4B-Thinking-patched" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lettersandpatterns/Ouro-1.4B-Thinking-patched", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/lettersandpatterns/Ouro-1.4B-Thinking-patched
- SGLang
How to use lettersandpatterns/Ouro-1.4B-Thinking-patched 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 "lettersandpatterns/Ouro-1.4B-Thinking-patched" \ --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": "lettersandpatterns/Ouro-1.4B-Thinking-patched", "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 "lettersandpatterns/Ouro-1.4B-Thinking-patched" \ --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": "lettersandpatterns/Ouro-1.4B-Thinking-patched", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use lettersandpatterns/Ouro-1.4B-Thinking-patched with Docker Model Runner:
docker model run hf.co/lettersandpatterns/Ouro-1.4B-Thinking-patched
Download model.safetensors from lettersandpatterns/Ouro-1.4B-Thinking-patched: direct link, hf CLI and curl.
- Browser
- Download file 2.87 GB
-
https://huggingface.co/lettersandpatterns/Ouro-1.4B-Thinking-patched/resolve/main/model.safetensors
- Command line
-
hf download hf://lettersandpatterns/Ouro-1.4B-Thinking-patched/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/lettersandpatterns/Ouro-1.4B-Thinking-patched/resolve/main/model.safetensors
2.87 GB
- Xet hash:
- 84696a9444990f4a5dfa1ed946a118991abdd4d10ce3d9034e0721982d3b5917
- Size of remote file:
- 2.87 GB
- SHA256:
- 6730581fd366f6d1689728c61637520228385cdfc17c0b24f19aa840b00565fb
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