Instructions to use oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa with Transformers:

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

pipe = pipeline("text-generation", model="oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa")
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("oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa")
model = AutoModelForCausalLM.from_pretrained("oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa", 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 oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa

SGLang

How to use oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa 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 "oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa" \
    --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": "oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa",
		"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 "oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa" \
        --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": "oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa with Docker Model Runner:
```
docker model run hf.co/oof-baroomf/csrsef-thinking-20260325T021216Z-it01-pubmedqa
```

csrsef-thinking-20260325T021216Z-it01-pubmedqa

File size: 1,095 Bytes

---
base_model:
- Qwen/Qwen3-4B-Thinking-2507
- Qwen/Qwen3-4B-Instruct-2507
library_name: transformers
tags:
- mergekit
- merge

---
# thinking_merged

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the NuSLERP merge method using [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) as a base.

### Models Merged

The following models were included in the merge:
* [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
* /workspace/csrsef/runs/20260325T021216Z/iteration_01/pubmedqa/instruct_merged

### Configuration

The following YAML configuration was used to produce this model:

```yaml
merge_method: nuslerp
base_model: Qwen/Qwen3-4B-Instruct-2507
models:
- model: Qwen/Qwen3-4B-Thinking-2507
  parameters:
    weight: 1.0
- model: /workspace/csrsef/runs/20260325T021216Z/iteration_01/pubmedqa/instruct_merged
  parameters:
    weight: 1.0
dtype: float16
tokenizer_source: Qwen/Qwen3-4B-Thinking-2507

```