Instructions to use May2222/Fisher-R1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use May2222/Fisher-R1-7B with Transformers:

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

pipe = pipeline("text-generation", model="May2222/Fisher-R1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("May2222/Fisher-R1-7B")
model = AutoModelForCausalLM.from_pretrained("May2222/Fisher-R1-7B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use May2222/Fisher-R1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "May2222/Fisher-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": "May2222/Fisher-R1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/May2222/Fisher-R1-7B

SGLang

How to use May2222/Fisher-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 "May2222/Fisher-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": "May2222/Fisher-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 "May2222/Fisher-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": "May2222/Fisher-R1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use May2222/Fisher-R1-7B with Docker Model Runner:
```
docker model run hf.co/May2222/Fisher-R1-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Fisher-R1-7B

Fisher-R1-7B is an open-weight LLM agent for reliable hypothesis testing. It is post-trained from Qwen2.5-Coder-7B-Instruct on synthetic executable statistical tasks, using supervised fine-tuning followed by reinforcement learning with verified statistical rewards.

The model is designed to inspect data, select and execute an appropriate statistical test, report a p-value, and draw a conclusion. It is evaluated on P-Bench.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "May2222/Fisher-R1-7B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

For the training setup, evaluation protocol, and results, see Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing.

Citation

@article{miao2026fisherr1,
  title   = {Fisher-R1: Training LLM Agents for Reliable Hypothesis Testing},
  author  = {Miao, Jiacheng and Mu, Jin and Chen, Guanhua and Zou, James},
  journal = {arXiv preprint arXiv:2608.07437},
  year    = {2026},
  url     = {https://arxiv.org/abs/2608.07437}
}