Instructions to use MatteoKhan/Cerebras-OPT-Fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MatteoKhan/Cerebras-OPT-Fusion with Transformers:

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

pipe = pipeline("text-generation", model="MatteoKhan/Cerebras-OPT-Fusion")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MatteoKhan/Cerebras-OPT-Fusion")
model = AutoModelForCausalLM.from_pretrained("MatteoKhan/Cerebras-OPT-Fusion", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MatteoKhan/Cerebras-OPT-Fusion with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MatteoKhan/Cerebras-OPT-Fusion"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatteoKhan/Cerebras-OPT-Fusion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MatteoKhan/Cerebras-OPT-Fusion

SGLang

How to use MatteoKhan/Cerebras-OPT-Fusion 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 "MatteoKhan/Cerebras-OPT-Fusion" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatteoKhan/Cerebras-OPT-Fusion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "MatteoKhan/Cerebras-OPT-Fusion" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatteoKhan/Cerebras-OPT-Fusion",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MatteoKhan/Cerebras-OPT-Fusion with Docker Model Runner:
```
docker model run hf.co/MatteoKhan/Cerebras-OPT-Fusion
```

MatteoKhan commited on Mar 4, 2025

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@@ -19,10 +19,11 @@ tags:
 ## 📌 Overview
 **CerebrasOPT-Hybrid-6.7B** is an **experimental hybrid language model** that merges the capabilities of **Cerebras-GPT-6.7B** and **OPT-6.7B** using the **Linear Merge technique**. This approach aims to enhance performance while maintaining efficiency, leveraging the best of both parent models.
-🔗 **Created by**: [Your Name]
-🎓 **Affiliation**: [Your Affiliation]
 📍 **License**: MIT
 🔗 [Model on Hugging Face](https://huggingface.co/YourProfile/CerebrasOPT-Hybrid-6.7B)
 ## 🧠 Model Details

 ## 📌 Overview
 **CerebrasOPT-Hybrid-6.7B** is an **experimental hybrid language model** that merges the capabilities of **Cerebras-GPT-6.7B** and **OPT-6.7B** using the **Linear Merge technique**. This approach aims to enhance performance while maintaining efficiency, leveraging the best of both parent models.
+🔗 **Created by**: [Matteo Khan]
+🎓 **Affiliation**: Apprentice at TW3 Partners (Generative AI Research)
 📍 **License**: MIT
+🔗 [Connect with me on LinkedIn](https://www.linkedin.com/in/matteo-khan-a10309263/)
 🔗 [Model on Hugging Face](https://huggingface.co/YourProfile/CerebrasOPT-Hybrid-6.7B)
 ## 🧠 Model Details