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 Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

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

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 serve -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 serve -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

Atomic Chat new
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

Scie-R1-GGUF

File size: 2,737 Bytes

---
library_name: transformers
tags:
- sft
- unsloth
- science
- reasoning
license: apache-2.0
datasets:
- mattwesney/CoT_Reasoning_Scientific_Discovery_and_Research
language:
- en
base_model:
- khazarai/Scie-R1
pipeline_tag: text-generation
---

# 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](https://huggingface.co/datasets/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.