Instructions to use Qiskit/Qwen2.5-Coder-14B-Qiskit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qiskit/Qwen2.5-Coder-14B-Qiskit with Transformers:

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

pipe = pipeline("text-generation", model="Qiskit/Qwen2.5-Coder-14B-Qiskit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Qiskit/Qwen2.5-Coder-14B-Qiskit", dtype="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Qiskit/Qwen2.5-Coder-14B-Qiskit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qiskit/Qwen2.5-Coder-14B-Qiskit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qiskit/Qwen2.5-Coder-14B-Qiskit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qiskit/Qwen2.5-Coder-14B-Qiskit

SGLang

How to use Qiskit/Qwen2.5-Coder-14B-Qiskit 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 "Qiskit/Qwen2.5-Coder-14B-Qiskit" \
    --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": "Qiskit/Qwen2.5-Coder-14B-Qiskit",
		"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 "Qiskit/Qwen2.5-Coder-14B-Qiskit" \
        --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": "Qiskit/Qwen2.5-Coder-14B-Qiskit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qiskit/Qwen2.5-Coder-14B-Qiskit with Docker Model Runner:
```
docker model run hf.co/Qiskit/Qwen2.5-Coder-14B-Qiskit
```

tidealwari commited on Oct 28, 2025

Commit

e5bc0bb

verified ·

1 Parent(s): ba692d5

Fix metrics table

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -133,10 +133,10 @@ We advise adding the `rope_scaling` configuration only when processing long cont
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-          QiskitHumanEval
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         <th style="text-align:center; padding:12px 16px; background:linear-gradient(90deg,#f6f8fb,#eef3f9); color:#0b1220; font-weight:700; border-bottom:1px solid rgba(15,23,42,0.06);">
-          QiskitHumanEval-Hard
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           HumanEval

           Model
         </th>
         <th style="text-align:center; padding:12px 16px; background:linear-gradient(90deg,#f6f8fb,#eef3f9); color:#0b1220; font-weight:700; border-bottom:1px solid rgba(15,23,42,0.06);">
+          QiskitHumanEval-Hard
         </th>
         <th style="text-align:center; padding:12px 16px; background:linear-gradient(90deg,#f6f8fb,#eef3f9); color:#0b1220; font-weight:700; border-bottom:1px solid rgba(15,23,42,0.06);">
+          QiskitHumanEval
         </th>
         <th style="text-align:center; padding:12px 16px; background:linear-gradient(90deg,#f6f8fb,#eef3f9); color:#0b1220; font-weight:700; border-bottom:1px solid rgba(15,23,42,0.06);">
           HumanEval