Instructions to use prithivMLmods/Research-Reasoning-Qwen-F32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Research-Reasoning-Qwen-F32-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Research-Reasoning-Qwen-F32-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Research-Reasoning-Qwen-F32-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Research-Reasoning-Qwen-F32-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 prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Research-Reasoning-Qwen-F32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Research-Reasoning-Qwen-F32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-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/Research-Reasoning-Qwen-F32-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Research-Reasoning-Qwen-F32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Research-Reasoning-Qwen-F32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Research-Reasoning-Qwen-F32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Research-Reasoning-Qwen-F32-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Nemotron-Research-Reasoning-Qwen-1.5B-GGUF

Nemotron-Research-Reasoning-Qwen-1.5B is the world’s leading 1.5B open-weight model for complex reasoning tasks such as mathematical problems, coding challenges, scientific questions, and logic puzzles. It is trained using the ProRL algorithm on a diverse and comprehensive set of datasets. Our model has achieved impressive results, outperforming Deepseek’s 1.5B model by a large margin on a broad range of tasks, including math, coding, and GPQA.

Model Files

File Name	Format	Size	Precision	Use Case
`Nemotron-Research-Reasoning-Qwen-1.5B.F32.gguf`	GGUF	7.11 GB	F32	Highest precision, research use
`Nemotron-Research-Reasoning-Qwen-1.5B.BF16.gguf`	GGUF	3.56 GB	BF16	High precision, balanced performance
`Nemotron-Research-Reasoning-Qwen-1.5B.F16.gguf`	GGUF	3.56 GB	F16	High precision, memory efficient
`Nemotron-Research-Reasoning-Qwen-1.5B.Q8_0.gguf`	GGUF	1.89 GB	Q8_0	Good quality, moderate compression
`Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_M.gguf`	GGUF	1.29 GB	Q5_K_M	Balanced quality/size (recommended)
`Nemotron-Research-Reasoning-Qwen-1.5B.Q5_K_S.gguf`	GGUF	1.26 GB	Q5_K_S	Good quality, smaller size
`Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_M.gguf`	GGUF	1.12 GB	Q4_K_M	Good balance for most users
`Nemotron-Research-Reasoning-Qwen-1.5B.Q4_K_S.gguf`	GGUF	1.07 GB	Q4_K_S	Decent quality, compact size
`Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_L.gguf`	GGUF	980 MB	Q3_K_L	Lower quality, very compact
`Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_M.gguf`	GGUF	924 MB	Q3_K_M	Fast inference, limited quality
`Nemotron-Research-Reasoning-Qwen-1.5B.Q3_K_S.gguf`	GGUF	861 MB	Q3_K_S	Fastest inference, basic quality
`Nemotron-Research-Reasoning-Qwen-1.5B.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 for best quality/performance balance
For Resource-Constrained Environments: Use Q4_K_M or Q4_K_S
For Edge Devices: Use Q3_K_M or Q2_K for minimal footprint

Quants Usage

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

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 274

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/Research-Reasoning-Qwen-F32-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Finetuned

nvidia/Nemotron-Research-Reasoning-Qwen-1.5B

Quantized

(22)

this model