Instructions to use SUSTech/Qwen3-8B-CalibSFT-RLCR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SUSTech/Qwen3-8B-CalibSFT-RLCR with Transformers:

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

pipe = pipeline("text-generation", model="SUSTech/Qwen3-8B-CalibSFT-RLCR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SUSTech/Qwen3-8B-CalibSFT-RLCR")
model = AutoModelForCausalLM.from_pretrained("SUSTech/Qwen3-8B-CalibSFT-RLCR", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SUSTech/Qwen3-8B-CalibSFT-RLCR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SUSTech/Qwen3-8B-CalibSFT-RLCR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SUSTech/Qwen3-8B-CalibSFT-RLCR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SUSTech/Qwen3-8B-CalibSFT-RLCR

SGLang

How to use SUSTech/Qwen3-8B-CalibSFT-RLCR 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 "SUSTech/Qwen3-8B-CalibSFT-RLCR" \
    --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": "SUSTech/Qwen3-8B-CalibSFT-RLCR",
		"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 "SUSTech/Qwen3-8B-CalibSFT-RLCR" \
        --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": "SUSTech/Qwen3-8B-CalibSFT-RLCR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SUSTech/Qwen3-8B-CalibSFT-RLCR with Docker Model Runner:
```
docker model run hf.co/SUSTech/Qwen3-8B-CalibSFT-RLCR
```

claytonwang commited on about 1 month ago

Commit

1f8ecfe

verified ·

1 Parent(s): d0f7033

Group ID/OOD columns in results table

Browse files

Files changed (1) hide show

README.md +18 -5

README.md CHANGED Viewed

@@ -66,11 +66,24 @@ print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_toke
 Results are averaged over 8 in-distribution math benchmarks (DeepScaleR-Eval, MATH-500, MinervaMath, OlympiadBench, GSM8K, AIME 2024–2026) and 8 out-of-distribution benchmarks (HotpotQA, TriviaQA, DROP, MuSiQue, LiveBenchReasoning, NQOpen, PopQA, WebQuestions), sampling at temperature 0.6 with 4 responses per question (32 on AIME). DeepScaleR-Eval consists of 2,000 DeepScaleR questions held out from training.
-| Model | ID Pass@1 ↑ | ID AUROC ↑ | ID Brier ↓ | ID ECE ↓ | OOD Pass@1 ↑ | OOD AUROC ↑ | OOD Brier ↓ | OOD ECE ↓ |
-| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
-| Qwen3-8B | 59.32 | 71.90 | 33.20 | 34.36 | 45.36 | 65.71 | 45.15 | 46.94 |
-| Qwen3-8B-RLCR | 65.33 | 82.36 | 14.43 | 14.46 | 43.96 | 69.38 | 31.14 | 31.17 |
-| **Qwen3-8B-CalibSFT-RLCR** | 67.20 | 86.32 | 12.77 | 8.59 | 45.71 | 71.31 | 23.24 | 18.20 |
 ## Citation

 Results are averaged over 8 in-distribution math benchmarks (DeepScaleR-Eval, MATH-500, MinervaMath, OlympiadBench, GSM8K, AIME 2024–2026) and 8 out-of-distribution benchmarks (HotpotQA, TriviaQA, DROP, MuSiQue, LiveBenchReasoning, NQOpen, PopQA, WebQuestions), sampling at temperature 0.6 with 4 responses per question (32 on AIME). DeepScaleR-Eval consists of 2,000 DeepScaleR questions held out from training.
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">Model</th>
+      <th colspan="4">In-distribution</th>
+      <th colspan="4">Out-of-distribution</th>
+    </tr>
+    <tr>
+      <th>Pass@1 ↑</th><th>AUROC ↑</th><th>Brier ↓</th><th>ECE ↓</th>
+      <th>Pass@1 ↑</th><th>AUROC ↑</th><th>Brier ↓</th><th>ECE ↓</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td>Qwen3-8B</td><td>59.32</td><td>71.90</td><td>33.20</td><td>34.36</td><td>45.36</td><td>65.71</td><td>45.15</td><td>46.94</td></tr>
+    <tr><td>Qwen3-8B-RLCR</td><td>65.33</td><td>82.36</td><td>14.43</td><td>14.46</td><td>43.96</td><td>69.38</td><td>31.14</td><td>31.17</td></tr>
+    <tr><td><b>Qwen3-8B-CalibSFT-RLCR</b></td><td>67.20</td><td>86.32</td><td>12.77</td><td>8.59</td><td>45.71</td><td>71.31</td><td>23.24</td><td>18.20</td></tr>
+  </tbody>
+</table>
 ## Citation