Instructions to use daedalus2027/Ouro-1.4B-bucket with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use daedalus2027/Ouro-1.4B-bucket with Transformers:

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

pipe = pipeline("text-generation", model="daedalus2027/Ouro-1.4B-bucket", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("daedalus2027/Ouro-1.4B-bucket", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use daedalus2027/Ouro-1.4B-bucket with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "daedalus2027/Ouro-1.4B-bucket"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "daedalus2027/Ouro-1.4B-bucket",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/daedalus2027/Ouro-1.4B-bucket

SGLang

How to use daedalus2027/Ouro-1.4B-bucket 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 "daedalus2027/Ouro-1.4B-bucket" \
    --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": "daedalus2027/Ouro-1.4B-bucket",
		"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 "daedalus2027/Ouro-1.4B-bucket" \
        --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": "daedalus2027/Ouro-1.4B-bucket",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use daedalus2027/Ouro-1.4B-bucket with Docker Model Runner:
```
docker model run hf.co/daedalus2027/Ouro-1.4B-bucket
```

daedalus2027 commited on Jun 22

Commit

2354157

1 Parent(s): 69e7537

docs: update transformers version support to >=4.56.0

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -93,7 +93,7 @@ Ouro-1.4B is based on the decoder-only Transformer architecture with parameter s
 ## Quick Start
-**⚠️ IMPORTANT**: Please use `transformers<4.56.0` to avoid compatibility issues. We recommend `transformers==4.54.1` or earlier versions.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer

 ## Quick Start
+**✅ Supported**: `transformers>=4.56.0` is now fully supported after the KV cache fix. For best results, use `transformers>=4.56.0`.
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer