Instructions to use thehekimoghlu/QCOP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thehekimoghlu/QCOP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="thehekimoghlu/QCOP")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("thehekimoghlu/QCOP", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use thehekimoghlu/QCOP with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thehekimoghlu/QCOP" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thehekimoghlu/QCOP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thehekimoghlu/QCOP
- SGLang
How to use thehekimoghlu/QCOP 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 "thehekimoghlu/QCOP" \ --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": "thehekimoghlu/QCOP", "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 "thehekimoghlu/QCOP" \ --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": "thehekimoghlu/QCOP", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thehekimoghlu/QCOP with Docker Model Runner:
docker model run hf.co/thehekimoghlu/QCOP
Update README.md
Browse files
README.md
CHANGED
|
@@ -27,7 +27,7 @@ pipeline_tag: image-text-to-text
|
|
| 27 |
| **Minimum vRAM** | 24GB (Custom runtime and Modded firmware) |
|
| 28 |
| **Release Date** | April 20, 2026 |
|
| 29 |
| **License** | MIT
|
| 30 |
-
| **Paper:** | *A Unified Framework for Calibration, Decoding, and Resource-Aware Processing in Hybrid Quantum-Classical Machine Learning — Tunjay Akbarli (April 2026)*
|
| 31 |
|
| 32 |
## Description
|
| 33 |
The QCOPM analyzes quantum computing calibration experiment plots and generates structured technical text across six analysis question categories. `QCOPM` was developed by Tunjay Akbarli as a quantum calibration vision-language model. This model is ready for commercial/non-commercial use. <br>
|
|
|
|
| 27 |
| **Minimum vRAM** | 24GB (Custom runtime and Modded firmware) |
|
| 28 |
| **Release Date** | April 20, 2026 |
|
| 29 |
| **License** | MIT
|
| 30 |
+
| **Paper(-s):** | *A Unified Framework for Calibration, Decoding, and Resource-Aware Processing in Hybrid Quantum-Classical Machine Learning — Tunjay Akbarli (April 2026)*
|
| 31 |
|
| 32 |
## Description
|
| 33 |
The QCOPM analyzes quantum computing calibration experiment plots and generates structured technical text across six analysis question categories. `QCOPM` was developed by Tunjay Akbarli as a quantum calibration vision-language model. This model is ready for commercial/non-commercial use. <br>
|