Instructions to use prithivMLmods/OpenRHO-2B-Thinker-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/OpenRHO-2B-Thinker-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/OpenRHO-2B-Thinker-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/OpenRHO-2B-Thinker-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/OpenRHO-2B-Thinker-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/OpenRHO-2B-Thinker-GGUF",
	filename="OpenRHO-2B-Thinker.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/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/OpenRHO-2B-Thinker-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/OpenRHO-2B-Thinker-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/OpenRHO-2B-Thinker-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/OpenRHO-2B-Thinker-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/OpenRHO-2B-Thinker-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/OpenRHO-2B-Thinker-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-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/OpenRHO-2B-Thinker-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/OpenRHO-2B-Thinker-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/OpenRHO-2B-Thinker-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/OpenRHO-2B-Thinker-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/OpenRHO-2B-Thinker-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenRHO-2B-Thinker-GGUF-Q4_K_M

List all available models

lemonade list

OpenRHO-2B-Thinker-GGUF

OpenRHO-2B-Thinker is a general-purpose reasoning model designed to enhance the cognitive abilities of edge-deployed large language models (LLMs) through reinforcement learning (RL). Fine-tuned from Qwen2-1.5B-Instruct using the QwQ distill dataset, it delivers refined improvements in logical reasoning, structured problem-solving, and lightweight coding — making it highly efficient for resource-constrained environments.

Model Files

File Name	Size	Quantization	Format	Description
`OpenRHO-2B-Thinker.BF16.gguf`	3.56 GB	BF16	GGUF	BFloat16 precision version
`OpenRHO-2B-Thinker.F16.gguf`	3.56 GB	FP16	GGUF	Float16 precision version
`OpenRHO-2B-Thinker.F32.gguf`	7.11 GB	FP32	GGUF	Float32 precision version
`OpenRHO-2B-Thinker.Q2_K.gguf`	753 MB	Q2_K	GGUF	2-bit quantized (K variant)
`OpenRHO-2B-Thinker.Q3_K_M.gguf`	924 MB	Q3_K_M	GGUF	3-bit quantized (K M variant)
`OpenRHO-2B-Thinker.Q4_K_M.gguf`	1.12 GB	Q4_K_M	GGUF	4-bit quantized (K M variant)
`OpenRHO-2B-Thinker.Q4_K_S.gguf`	1.07 GB	Q4_K_S	GGUF	4-bit quantized (K S variant)
`OpenRHO-2B-Thinker.Q5_K_M.gguf`	1.29 GB	Q5_K_M	GGUF	5-bit quantized (K M variant)
`OpenRHO-2B-Thinker.Q5_K_S.gguf`	1.26 GB	Q5_K_S	GGUF	5-bit quantized (K S variant)
`OpenRHO-2B-Thinker.Q6_K.gguf`	1.46 GB	Q6_K	GGUF	6-bit quantized (K variant)
`OpenRHO-2B-Thinker.Q8_0.gguf`	1.89 GB	Q8_0	GGUF	8-bit quantized
`.gitattributes`	2.24 kB	—	—	Git LFS tracking file
`config.json`	31 B	—	—	Configuration file
`README.md`	670 B	—	—	Model documentation

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	0.4
GGUF	Q3_K_S	0.5
GGUF	Q3_K_M	0.5	lower quality
GGUF	Q3_K_L	0.5
GGUF	IQ4_XS	0.6
GGUF	Q4_K_S	0.6	fast, recommended
GGUF	Q4_K_M	0.6	fast, recommended
GGUF	Q5_K_S	0.6
GGUF	Q5_K_M	0.7
GGUF	Q6_K	0.7	very good quality
GGUF	Q8_0	0.9	fast, best quality
GGUF	f16	1.6	16 bpw, overkill