Instructions to use mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Qwen2.5-7B-R1-Bespoke-Task-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 mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Qwen2.5-7B-R1-Bespoke-Task-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 mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Qwen2.5-7B-R1-Bespoke-Task-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/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Qwen2.5-7B-R1-Bespoke-Task-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/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Qwen2.5-7B-R1-Bespoke-Task-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-R1-Bespoke-Task-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Qwen2.5-7B-R1-Bespoke-Task-GGUF / Qwen2.5-7B-R1-Bespoke-Task.IQ4_XS.gguf

mradermacher

uploaded from leia

52c44ad verified over 1 year ago

History Blame GGUF Editor new Delete

4.25 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 13e20dd0be1f8d4a50cbb3f06de0c393364fcb98dc4d9fade108f521b104db3a
Size of remote file:: 4.25 GB
SHA256:: f8b349c93e48f3bf76cf42e5786149089c02b60281c6786ee2eb1a574b137ce6

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.