Instructions to use mradermacher/WebThinker-R1-32B-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/WebThinker-R1-32B-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/WebThinker-R1-32B-i1-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/WebThinker-R1-32B-i1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/WebThinker-R1-32B-i1-GGUF",
	filename="WebThinker-R1-32B.i1-IQ1_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mradermacher/WebThinker-R1-32B-i1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/WebThinker-R1-32B-i1-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 mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/WebThinker-R1-32B-i1-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 mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/WebThinker-R1-32B-i1-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 mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/WebThinker-R1-32B-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use mradermacher/WebThinker-R1-32B-i1-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 mradermacher/WebThinker-R1-32B-i1-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 mradermacher/WebThinker-R1-32B-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/WebThinker-R1-32B-i1-GGUF to start chatting

Docker Model Runner
How to use mradermacher/WebThinker-R1-32B-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/WebThinker-R1-32B-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/WebThinker-R1-32B-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WebThinker-R1-32B-i1-GGUF-Q4_K_M

List all available models

lemonade list

About

weighted/imatrix quants of https://huggingface.co/lixiaoxi45/WebThinker-R1-32B

static quants are available at https://huggingface.co/mradermacher/WebThinker-R1-32B-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

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

Link	Type	Size/GB	Notes
GGUF	i1-IQ1_S	7.4	for the desperate
GGUF	i1-IQ1_M	8.0	mostly desperate
GGUF	i1-IQ2_XXS	9.1
GGUF	i1-IQ2_XS	10.1
GGUF	i1-IQ2_S	10.5
GGUF	i1-IQ2_M	11.4
GGUF	i1-Q2_K_S	11.6	very low quality
GGUF	i1-Q2_K	12.4	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	12.9	lower quality
GGUF	i1-IQ3_XS	13.8
GGUF	i1-Q3_K_S	14.5	IQ3_XS probably better
GGUF	i1-IQ3_S	14.5	beats Q3_K*
GGUF	i1-IQ3_M	14.9
GGUF	i1-Q3_K_M	16.0	IQ3_S probably better
GGUF	i1-Q3_K_L	17.3	IQ3_M probably better
GGUF	i1-IQ4_XS	17.8
GGUF	i1-Q4_0	18.8	fast, low quality
GGUF	i1-Q4_K_S	18.9	optimal size/speed/quality
GGUF	i1-Q4_K_M	20.0	fast, recommended
GGUF	i1-Q4_1	20.7
GGUF	i1-Q5_K_S	22.7
GGUF	i1-Q5_K_M	23.4
GGUF	i1-Q6_K	27.0	practically like static Q6_K

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

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

Downloads last month: 31

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

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Model tree for mradermacher/WebThinker-R1-32B-i1-GGUF

Base model

lixiaoxi45/WebThinker-R1-32B

Quantized

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