Instructions to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT

SGLang

How to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT 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 "LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT" \
    --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": "LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT",
		"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 "LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT" \
        --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": "LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/Ouro-1.4B-Thinking-Terminal-SFT
```

Ouro-1.4B-Thinking-Terminal-SFT

File size: 1,565 Bytes

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{
  "architectures": [
    "OuroForCausalLM"
  ],
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoConfig": "configuration_ouro.OuroConfig",
    "AutoModel": "modeling_ouro.OuroModel",
    "AutoModelForCausalLM": "modeling_ouro.OuroForCausalLM"
  },
  "bos_token_id": 1,
  "dtype": "bfloat16",
  "early_exit_threshold": 1.0,
  "eos_token_id": 2,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 5632,
  "layer_types": [
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    "full_attention",
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    "full_attention",
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    "full_attention",
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    "full_attention"
  ],
  "max_position_embeddings": 65536,
  "max_window_layers": 24,
  "model_type": "ouro",
  "num_attention_heads": 16,
  "num_hidden_layers": 24,
  "num_key_value_heads": 16,
  "pad_token_id": 2,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "factor": 1.0,
    "rope_theta": 1000000.0,
    "rope_type": "linear"
  },
  "rope_theta": 1000000.0,
  "sliding_window": null,
  "tie_word_embeddings": false,
  "total_ut_steps": 4,
  "transformers_version": "5.5.4",
  "use_cache": false,
  "use_sliding_window": false,
  "vocab_size": 49152
}