Instructions to use EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-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 EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-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 EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-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 EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-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 EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF with Ollama:
```
ollama run hf.co/EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M
```

Lemonade

How to use EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
  - model: vtriple/Qwen-2.5-7B-Threatflux
merge_method: slerp
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
dtype: bfloat16
parameters:
  t: [0, 0.5, 0.25]

Downloads last month: 143

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

vtriple/Qwen-2.5-7B-Threatflux

Merge model

this model

Collection including EryriLabs/DeepSeek-R1-Distill-Qwen-YARA-Thinker-7B-GGUF

Detection Engineering & Cyber

Collection

Local models for blue-team work: rule authoring, translation and tuning • 4 items • Updated Aug 13