Instructions to use Qiskit/mistral-small-3.2-24b-qiskit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qiskit/mistral-small-3.2-24b-qiskit with Transformers:

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

pipe = pipeline("text-generation", model="Qiskit/mistral-small-3.2-24b-qiskit")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Qiskit/mistral-small-3.2-24b-qiskit")
model = AutoModelForMultimodalLM.from_pretrained("Qiskit/mistral-small-3.2-24b-qiskit", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qiskit/mistral-small-3.2-24b-qiskit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Install mistral-common:
pip install --upgrade mistral-common
# Start the vLLM server:
vllm serve "Qiskit/mistral-small-3.2-24b-qiskit" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
# 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/mistral-small-3.2-24b-qiskit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qiskit/mistral-small-3.2-24b-qiskit

SGLang

How to use Qiskit/mistral-small-3.2-24b-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/mistral-small-3.2-24b-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/mistral-small-3.2-24b-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/mistral-small-3.2-24b-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/mistral-small-3.2-24b-qiskit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qiskit/mistral-small-3.2-24b-qiskit with Docker Model Runner:
```
docker model run hf.co/Qiskit/mistral-small-3.2-24b-qiskit
```

tidealwari commited on Nov 18, 2025

Commit

f1452b1

verified ·

1 Parent(s): 6e0438e

Remove CrowsPairs french metrics

Browse files

Files changed (1) hide show

README.md +1 -8

README.md CHANGED Viewed

@@ -122,9 +122,6 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
         <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);">
           CrowsPairs (English)
         </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);">
-          CrowsPairs (French)
-        </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);">
           TruthfulQA (MC1 acc)
         </th>
@@ -142,7 +139,6 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
         <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-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">65.18</td>
-        <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">54.32</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">37.82</td>
       </tr>
       <tr style="background:#ffffff;">
@@ -156,7 +152,6 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">63.40</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">31.66</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">67.56</td>
-        <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">66.07</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">42.84</td>
       </tr>
       <tr style="background:#ffffff;">
@@ -170,7 +165,6 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">52.40</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">59.71</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">59.75</td>
-        <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">50.15</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">39.05</td>
       </tr>
       <tr style="background:#fbfdff;">
@@ -184,12 +178,11 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">51.80</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">60.79</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">66.79</td>
-        <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">58.68</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">40.51</td>
       </tr>
     </tbody>
   </table>
 *Note: All models listed in the benchmark table were evaluated using their respective system prompt, defined in their Hugging Face model.*
 ## Training Data

         <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);">
           CrowsPairs (English)
         </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);">
           TruthfulQA (MC1 acc)
         </th>
         <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-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">65.18</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">37.82</td>
       </tr>
       <tr style="background:#ffffff;">
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">63.40</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">31.66</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">67.56</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">42.84</td>
       </tr>
       <tr style="background:#ffffff;">
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">52.40</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">59.71</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">59.75</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">39.05</td>
       </tr>
       <tr style="background:#fbfdff;">
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">51.80</td>
         <td style="padding:12px 16px; text-align:center; font-weight:700; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">60.79</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">66.79</td>
         <td style="padding:12px 16px; text-align:center; font-family:ui-monospace, SFMono-Regular, Menlo, Monaco, monospace;">40.51</td>
       </tr>
     </tbody>
   </table>
 *Note: All models listed in the benchmark table were evaluated using their respective system prompt, defined in their Hugging Face model.*
 ## Training Data