Instructions to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Klear-Reasoner-8B-f32-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Klear-Reasoner-8B-f32-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Klear-Reasoner-8B-f32-GGUF",
	filename="Klear-Reasoner-8B.BF16.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 prithivMLmods/Klear-Reasoner-8B-f32-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/Klear-Reasoner-8B-f32-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

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/Klear-Reasoner-8B-f32-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

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/Klear-Reasoner-8B-f32-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

Use Docker

docker model run hf.co/prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Klear-Reasoner-8B-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/Klear-Reasoner-8B-f32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

SGLang

How to use prithivMLmods/Klear-Reasoner-8B-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/Klear-Reasoner-8B-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/Klear-Reasoner-8B-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/Klear-Reasoner-8B-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/Klear-Reasoner-8B-f32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16
```

Unsloth Studio

How to use prithivMLmods/Klear-Reasoner-8B-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/Klear-Reasoner-8B-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/Klear-Reasoner-8B-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/Klear-Reasoner-8B-f32-GGUF to start chatting

How to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

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": "prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Klear-Reasoner-8B-f32-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 prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

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 prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16
```

Lemonade

How to use prithivMLmods/Klear-Reasoner-8B-f32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

Run and chat with the model

lemonade run user.Klear-Reasoner-8B-f32-GGUF-BF16

List all available models

lemonade list

Klear-Reasoner-8B-f32-GGUF

The Klear-Reasoner-8B is an 8-billion-parameter language model based on the Qwen3-8B-Base model, specially fine-tuned for advanced long-chain-of-thought reasoning and enhanced problem-solving in math and coding tasks. It combines quality-centric long CoT supervised fine-tuning with a novel reinforcement learning method called Gradient-Preserving Clipping Policy Optimization (GPPO), which maintains gradients from clipped tokens to improve learning and exploration efficiency. The model demonstrates state-of-the-art performance on challenging benchmarks such as AIME 2024/2025 and LiveCodeBench, achieving top scores significantly surpassing many community models using larger inference budgets. It supports extended inference lengths up to 64K tokens, enabling deeper and more complex reasoning processes. Klear-Reasoner-8B is designed for careful deliberation during problem solving and excels at incremental quality improvements in tasks involving both mathematical reasoning and code generation.

Execute using Ollama

run ->

ollama run hf.co/prithivMLmods/Klear-Reasoner-8B-f32-GGUF:BF16

Model Files

File Name	Quant Type	File Size
Klear-Reasoner-8B.BF16.gguf	BF16	16.4 GB
Klear-Reasoner-8B.F16.gguf	F16	16.4 GB
Klear-Reasoner-8B.F32.gguf	F32	32.8 GB

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: 10

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

16-bit

32-bit

Model tree for prithivMLmods/Klear-Reasoner-8B-f32-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Kwai-Klear/Klear-Reasoner-8B-SFT

Finetuned

Kwai-Klear/Klear-Reasoner-8B

Quantized

(4)

this model