Instructions to use khazarai/Scie-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use khazarai/Scie-R1 with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/Scie-R1")
model = AutoModelForCausalLM.from_pretrained("khazarai/Scie-R1")
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

vLLM

How to use khazarai/Scie-R1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/khazarai/Scie-R1

SGLang

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

Unsloth Studio new

How to use khazarai/Scie-R1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/Scie-R1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for khazarai/Scie-R1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khazarai/Scie-R1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="khazarai/Scie-R1",
    max_seq_length=2048,
)

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

Model Card for Qwen3-CoT-Scientific-Research

Model Details

Model Description

Base Model: Qwen3-1.7B
Task: Scientific Reasoning with Chain-of-Thought (CoT)
Dataset: CoT_Reasoning_Scientific_Discovery_and_Research (custom dataset focusing on step-by-step scientific reasoning tasks)
Training Objective: Encourage step-by-step logical deductions for scientific reasoning problems

Uses

Direct Use

This fine-tuned model is designed for:

Assisting in teaching and learning scientific reasoning
Supporting educational AI assistants in science classrooms
Demonstrating step-by-step scientific reasoning in research training contexts
Serving as a resource for automated reasoning systems to better emulate structured scientific logic

It is not intended to replace human researchers, perform advanced analytics, or generate novel scientific discoveries.

Bias, Risks, and Limitations

May oversimplify complex or interdisciplinary problems
Performance limited by the scope of training data (primarily introductory-level scientific reasoning tasks)
Does not handle real-world experimentation or advanced statistical modeling
May produce incorrect reasoning if the prompt is highly ambiguous

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("khazarai/Scie-R1")
model = AutoModelForCausalLM.from_pretrained(
    "khazarai/Scie-R1",
    device_map={"": 0}
)

question = """
How are microfluidic devices revolutionizing laboratory analysis techniques, and what are the primary advantages they offer over traditional methods?
"""

messages = [
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 1800,
    temperature = 0.6,
    top_p = 0.95,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

Training Details

Training Data

Scope

This model was fine-tuned on tasks that involve core scientific reasoning:

Formulating testable hypotheses
Identifying independent and dependent variables
Designing simple controlled experiments
Interpreting graphs, tables, and basic data representations
Understanding relationships between evidence and conclusions
Recognizing simple logical fallacies in scientific arguments

Illustrative Examples

Drawing conclusions from experimental results
Evaluating alternative explanations for observed data
Explaining step-by-step reasoning behind scientific conclusions

Emphasis on Chain-of-Thought (CoT)

The dataset highlights explicit reasoning steps, making the model better at producing step-by-step explanations when solving scientific reasoning tasks.
Focus on Foundational Knowledge
The dataset aims to strengthen models in foundational scientific reasoning skills rather than covering all domains of scientific knowledge.

Focus on Foundational Knowledge

The dataset aims to strengthen models in foundational scientific reasoning skills rather than covering all domains of scientific knowledge.

Dataset: moremilk/CoT_Reasoning_Scientific_Discovery_and_Research