Text Generation
Transformers
Safetensors
ouro
looped-language-model
recurrent-depth
sft
math
conversational
custom_code
Instructions to use omar81939/Ouro-1.4B-Thinking-depth-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omar81939/Ouro-1.4B-Thinking-depth-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="omar81939/Ouro-1.4B-Thinking-depth-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("omar81939/Ouro-1.4B-Thinking-depth-SFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use omar81939/Ouro-1.4B-Thinking-depth-SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "omar81939/Ouro-1.4B-Thinking-depth-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": "omar81939/Ouro-1.4B-Thinking-depth-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/omar81939/Ouro-1.4B-Thinking-depth-SFT
- SGLang
How to use omar81939/Ouro-1.4B-Thinking-depth-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 "omar81939/Ouro-1.4B-Thinking-depth-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": "omar81939/Ouro-1.4B-Thinking-depth-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 "omar81939/Ouro-1.4B-Thinking-depth-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": "omar81939/Ouro-1.4B-Thinking-depth-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use omar81939/Ouro-1.4B-Thinking-depth-SFT with Docker Model Runner:
docker model run hf.co/omar81939/Ouro-1.4B-Thinking-depth-SFT
File size: 606 Bytes
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license: apache-2.0
base_model: ByteDance/Ouro-1.4B-Thinking
library_name: transformers
pipeline_tag: text-generation
tags:
- looped-language-model
- recurrent-depth
- sft
- math
---
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
REPO = "omar81939/Ouro-1.4B-Thinking-depth-SFT"
DEPTH = 16
tokenizer = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForCausalLM.from_pretrained(
REPO,
trust_remote_code=True,
dtype="bfloat16",
total_ut_steps=DEPTH,
)
```
With vLLM, set `hf_overrides={"total_ut_steps": DEPTH}` when creating the engine.
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