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

Libraries

How to use Rustamshry/Scie-R1-GGUF with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Rustamshry/Scie-R1-GGUF", dtype="auto")

llama-cpp-python

How to use Rustamshry/Scie-R1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rustamshry/Scie-R1-GGUF",
	filename="Scie-R1-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Rustamshry/Scie-R1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/Scie-R1-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/Scie-R1-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rustamshry/Scie-R1-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Rustamshry/Scie-R1-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Rustamshry/Scie-R1-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Rustamshry/Scie-R1-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Rustamshry/Scie-R1-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rustamshry/Scie-R1-GGUF:F16

Use Docker

docker model run hf.co/Rustamshry/Scie-R1-GGUF:F16

LM Studio
Jan

vLLM

How to use Rustamshry/Scie-R1-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Rustamshry/Scie-R1-GGUF:F16

SGLang

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

Ollama
How to use Rustamshry/Scie-R1-GGUF with Ollama:
```
ollama run hf.co/Rustamshry/Scie-R1-GGUF:F16
```

Unsloth Studio new

How to use Rustamshry/Scie-R1-GGUF 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 Rustamshry/Scie-R1-GGUF 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 Rustamshry/Scie-R1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use Rustamshry/Scie-R1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rustamshry/Scie-R1-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Rustamshry/Scie-R1-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Rustamshry/Scie-R1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Rustamshry/Scie-R1-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Rustamshry/Scie-R1-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use Rustamshry/Scie-R1-GGUF with Docker Model Runner:
```
docker model run hf.co/Rustamshry/Scie-R1-GGUF:F16
```

Lemonade

How to use Rustamshry/Scie-R1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rustamshry/Scie-R1-GGUF:F16

Run and chat with the model

lemonade run user.Scie-R1-GGUF-F16

List all available models

lemonade list

Model Card for Qwen3-CoT-Scientific-Research

Model Description

GGUF version of https://huggingface.co/khazarai/Scie-R1

Base Model: Qwen3-1.7B
Task: Scientific Reasoning with Chain-of-Thought (CoT)
Dataset: moremilk/CoT_Reasoning_Scientific_Discovery_and_Research
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

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.