Instructions to use Qiskit/granite-3.2-8b-qiskit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qiskit/granite-3.2-8b-qiskit with Transformers:

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

pipe = pipeline("text-generation", model="Qiskit/granite-3.2-8b-qiskit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qiskit/granite-3.2-8b-qiskit")
model = AutoModelForCausalLM.from_pretrained("Qiskit/granite-3.2-8b-qiskit", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qiskit/granite-3.2-8b-qiskit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qiskit/granite-3.2-8b-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/granite-3.2-8b-qiskit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qiskit/granite-3.2-8b-qiskit

SGLang

How to use Qiskit/granite-3.2-8b-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/granite-3.2-8b-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/granite-3.2-8b-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/granite-3.2-8b-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/granite-3.2-8b-qiskit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qiskit/granite-3.2-8b-qiskit with Docker Model Runner:
```
docker model run hf.co/Qiskit/granite-3.2-8b-qiskit
```

tidealwari commited on Nov 18, 2025

Commit

a785a65

verified ·

1 Parent(s): dc17af9

Fix rounding errors

Browse files

Files changed (1) hide show

README.md +3 -4

README.md CHANGED Viewed

@@ -128,7 +128,7 @@ for i in output:
     <tbody>
       <tr style="background:#f7fafc;">
         <td style="padding:12px 16px; font-weight:700; color:#07102a;">Qwen2.5-Coder-14B-Qiskit</td>
-        <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">25.16</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>
@@ -156,7 +156,7 @@ for i in output:
       </tr>
       <tr style="background:#ffffff;">
         <td style="padding:12px 16px; color:#0f172a;">granite-3.3-8b-qiskit</td>
-        <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">14.56</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">27.15</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">62.80</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">0.48</td>
@@ -172,7 +172,7 @@ for i in output:
         <td style="padding:12px 16px; color:#0f172a;">granite-3.2-8b-qiskit</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">9.93</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">24.50</td>
-        <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">57.31</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">0.09</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">41.41</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">96.30</td>
@@ -187,7 +187,6 @@ for i in output:
 *Note: All models listed in the benchmark table were evaluated using their respective system prompt, defined in their Hugging Face model.*
 ## Training Data
 - **Data Collection and Filtering:** Our code data is sourced from a combination of publicly available datasets (e.g., Code available on <https://github.com>), and additional synthetic data generated at IBM Quantum. We exclude code that is older than 2023.

     <tbody>
       <tr style="background:#f7fafc;">
         <td style="padding:12px 16px; font-weight:700; color:#07102a;">Qwen2.5-Coder-14B-Qiskit</td>
+        <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">25.17</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>
       </tr>
       <tr style="background:#ffffff;">
         <td style="padding:12px 16px; color:#0f172a;">granite-3.3-8b-qiskit</td>
+        <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">14.57</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">27.15</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">62.80</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">0.48</td>
         <td style="padding:12px 16px; color:#0f172a;">granite-3.2-8b-qiskit</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">9.93</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">24.50</td>
+        <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">57.32</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">0.09</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">41.41</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">96.30</td>
 *Note: All models listed in the benchmark table were evaluated using their respective system prompt, defined in their Hugging Face model.*
 ## Training Data
 - **Data Collection and Filtering:** Our code data is sourced from a combination of publicly available datasets (e.g., Code available on <https://github.com>), and additional synthetic data generated at IBM Quantum. We exclude code that is older than 2023.