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")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 Nov 18, 2025

Commit

ca2a8ff

verified ·

1 Parent(s): 3d7f46a

Fix qwen mathqa metric

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -175,7 +175,7 @@ We advise adding the `rope_scaling` configuration only when processing long cont
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">49.01</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">91.46</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">4.21</td>
-        <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">53.87</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">97.00</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">77.60</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">49.64</td>

         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">49.01</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">91.46</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">4.21</td>
+        <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">53.90</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">97.00</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">77.60</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">49.64</td>