Instructions to use IQuestLab/Fleming-R1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IQuestLab/Fleming-R1-7B with Transformers:

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

pipe = pipeline("text-generation", model="IQuestLab/Fleming-R1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("IQuestLab/Fleming-R1-7B")
model = AutoModelForMultimodalLM.from_pretrained("IQuestLab/Fleming-R1-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IQuestLab/Fleming-R1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IQuestLab/Fleming-R1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IQuestLab/Fleming-R1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IQuestLab/Fleming-R1-7B

SGLang

How to use IQuestLab/Fleming-R1-7B 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 "IQuestLab/Fleming-R1-7B" \
    --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": "IQuestLab/Fleming-R1-7B",
		"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 "IQuestLab/Fleming-R1-7B" \
        --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": "IQuestLab/Fleming-R1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IQuestLab/Fleming-R1-7B with Docker Model Runner:
```
docker model run hf.co/IQuestLab/Fleming-R1-7B
```

thinksoso commited on Sep 22, 2025

Commit

11898bb

verified ·

1 Parent(s): 31daa33

Update README.md

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Files changed (1) hide show

README.md +9 -6

README.md CHANGED Viewed

@@ -18,7 +18,7 @@ pipeline_tag: text-generation
     </span>
   </a>
   <span style="margin:0 .75em;opacity:.6;">•</span>
-  <a href="https://github.com/UbiquantAI/Fleming-R1/blob/main/paper/Fleming-R1.pdf" aria-label="Paper">📑&nbsp;Paper</a>
 </p>
 ## Highlights
@@ -110,10 +110,13 @@ In medical scenarios, results must be reviewed and approved by qualified profess
 ## 📚 Citation
 ```bibtex
-@misc{fleming-r1,
-  title         = {Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning},
-  author        = {Chi Liu and Derek Li and Yan Shu and Robin Chen and Derek Duan and Teng Fang and Bryan Dai},
-  year          = {2025},
-  url           = {https://github.com/UbiquantAI/Fleming-R1/blob/main/paper/Fleming-R1.pdf},
 }
 ```

     </span>
   </a>
   <span style="margin:0 .75em;opacity:.6;">•</span>
+  <a href="https://arxiv.org/abs/2509.15279" aria-label="Paper">📑&nbsp;Paper</a>
 </p>
 ## Highlights
 ## 📚 Citation
 ```bibtex
+@misc{flemingr1,
+      title={Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning},
+      author={Chi Liu and Derek Li and Yan Shu and Robin Chen and Derek Duan and Teng Fang and Bryan Dai},
+      year={2025},
+      eprint={2509.15279},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2509.15279},
 }
 ```