Instructions to use prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF",
	filename="GCIRS-Reasoning-1.5B-R1.BF16.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 prithivMLmods/GCIRS-Reasoning-1.5B-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 prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M

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 prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M

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 prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/GCIRS-Reasoning-1.5B-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": "prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/GCIRS-Reasoning-1.5B-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 "prithivMLmods/GCIRS-Reasoning-1.5B-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": "prithivMLmods/GCIRS-Reasoning-1.5B-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 "prithivMLmods/GCIRS-Reasoning-1.5B-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": "prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/GCIRS-Reasoning-1.5B-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 prithivMLmods/GCIRS-Reasoning-1.5B-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 prithivMLmods/GCIRS-Reasoning-1.5B-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 prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/GCIRS-Reasoning-1.5B-R1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GCIRS-Reasoning-1.5B-R1-GGUF-Q4_K_M

List all available models

lemonade list

GCIRS-Reasoning-1.5B-R1-GGUF

GCIRS-Reasoning-1.5B-R1 is a research-grade reasoning model fine-tuned from Qwen2.5-1.5B-Instruct, focused on non-fictional reasoning, factual consistency, and scientific depth. Trained with reinforcement learning using the Big Reasoning Traces dataset from DeepSeek, this model is tailored for complex analytical tasks and scientific rigor in high-stakes or research environments.

Model Files

File Name	Format	Size	Precision	Use Case
`GCIRS-Reasoning-1.5B-R1.F32.gguf`	GGUF	7.11 GB	F32	Highest precision, research use
`GCIRS-Reasoning-1.5B-R1.BF16.gguf`	GGUF	3.56 GB	BF16	High precision, balanced performance
`GCIRS-Reasoning-1.5B-R1.F16.gguf`	GGUF	3.56 GB	F16	High precision, memory efficient
`GCIRS-Reasoning-1.5B-R1.Q8_0.gguf`	GGUF	1.89 GB	Q8_0	Excellent quality, moderate compression
`GCIRS-Reasoning-1.5B-R1.Q6_K.gguf`	GGUF	1.46 GB	Q6_K	Very good quality, good compression
`GCIRS-Reasoning-1.5B-R1.Q5_K_M.gguf`	GGUF	1.29 GB	Q5_K_M	Balanced quality/size (recommended)
`GCIRS-Reasoning-1.5B-R1.Q5_K_S.gguf`	GGUF	1.26 GB	Q5_K_S	Good quality, smaller size
`GCIRS-Reasoning-1.5B-R1.Q4_K_M.gguf`	GGUF	1.12 GB	Q4_K_M	Good balance for most users
`GCIRS-Reasoning-1.5B-R1.Q4_K_S.gguf`	GGUF	1.07 GB	Q4_K_S	Decent quality, compact size
`GCIRS-Reasoning-1.5B-R1.Q3_K_L.gguf`	GGUF	980 MB	Q3_K_L	Lower quality, very compact
`GCIRS-Reasoning-1.5B-R1.Q3_K_M.gguf`	GGUF	924 MB	Q3_K_M	Fast inference, limited quality
`GCIRS-Reasoning-1.5B-R1.Q3_K_S.gguf`	GGUF	861 MB	Q3_K_S	Fastest inference, basic quality
`GCIRS-Reasoning-1.5B-R1.Q2_K.gguf`	GGUF	753 MB	Q2_K	Minimal size, experimental use

Quick Selection Guide

For Research/Development: Use F32 or BF16 for maximum accuracy
For Production (Recommended): Use Q5_K_M or Q6_K for best quality/performance balance
For General Use: Use Q4_K_M or Q4_K_S for good performance
For Resource-Constrained Environments: Use Q3_K_M or Q3_K_L
For Edge Devices: Use Q2_K for minimal footprint