Instructions to use TachyHealth/Gazal-R1-32B-GRPO-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TachyHealth/Gazal-R1-32B-GRPO-preview with Transformers:

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

pipe = pipeline("text-generation", model="TachyHealth/Gazal-R1-32B-GRPO-preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TachyHealth/Gazal-R1-32B-GRPO-preview")
model = AutoModelForCausalLM.from_pretrained("TachyHealth/Gazal-R1-32B-GRPO-preview")
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 TachyHealth/Gazal-R1-32B-GRPO-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TachyHealth/Gazal-R1-32B-GRPO-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TachyHealth/Gazal-R1-32B-GRPO-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TachyHealth/Gazal-R1-32B-GRPO-preview

SGLang

How to use TachyHealth/Gazal-R1-32B-GRPO-preview 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 "TachyHealth/Gazal-R1-32B-GRPO-preview" \
    --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": "TachyHealth/Gazal-R1-32B-GRPO-preview",
		"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 "TachyHealth/Gazal-R1-32B-GRPO-preview" \
        --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": "TachyHealth/Gazal-R1-32B-GRPO-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TachyHealth/Gazal-R1-32B-GRPO-preview with Docker Model Runner:
```
docker model run hf.co/TachyHealth/Gazal-R1-32B-GRPO-preview
```

Add Hugging Face paper link for improved discoverability

by nielsr HF Staff - opened Jul 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -1,13 +1,13 @@
 ---
-license: apache-2.0
-license_link: https://huggingface.co/TachyHealth/Gazal-R1-32B-GRPO-preview/blob/main/LICENSE
 datasets:
 - TachyHealth/medical_grpo
 - TachyHealth/structured_medical
-base_model:
-- TachyHealth/Gazal-R1-32B-sft-merged-preview
-pipeline_tag: text-generation
 library_name: transformers
 tags:
 - gazal-r1
 - grpo
@@ -21,6 +21,8 @@ tags:
 # Gazal-R1-32B: Medical Reasoning Language Model
 <a href="https://gazal.ai/" target="_blank" style="margin: 0px;">
     <img alt="Gazal AI" src="./logo.png" style=" width: 70%;" />
 </a>

 ---
+base_model:
+- TachyHealth/Gazal-R1-32B-sft-merged-preview
 datasets:
 - TachyHealth/medical_grpo
 - TachyHealth/structured_medical
 library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/TachyHealth/Gazal-R1-32B-GRPO-preview/blob/main/LICENSE
+pipeline_tag: text-generation
 tags:
 - gazal-r1
 - grpo
 # Gazal-R1-32B: Medical Reasoning Language Model
+The model was presented in the paper [Gazal-R1: Achieving State-of-the-Art Medical Reasoning with Parameter-Efficient Two-Stage Training](https://huggingface.co/papers/2506.21594).
 <a href="https://gazal.ai/" target="_blank" style="margin: 0px;">
     <img alt="Gazal AI" src="./logo.png" style=" width: 70%;" />
 </a>