DeepSeek-R1-Distill-Llama-8B-GGUF

Instructions to use fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF", dtype="auto")

llama-cpp-python

How to use fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF",
	filename="DeepSeek-R1-Distill-Llama-8B-Abliterated.IQ4_XS.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf fairy322/DeepSeek-R1-Distill-Llama-8B-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 fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf fairy322/DeepSeek-R1-Distill-Llama-8B-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 fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fairy322/DeepSeek-R1-Distill-Llama-8B-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 fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF with Ollama:
```
ollama run hf.co/fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use fairy322/DeepSeek-R1-Distill-Llama-8B-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 fairy322/DeepSeek-R1-Distill-Llama-8B-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 fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M
```

Lemonade

How to use fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Llama-8B-GGUF-Q4_K_M

List all available models

lemonade list

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About

static quants of https://huggingface.co/stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated

weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B-Abliterated-i1-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
PART 1 PART 2	Q2_K	6.5
PART 1 PART 2	Q3_K_S	7.4
PART 1 PART 2	Q3_K_M	8.1	lower quality
PART 1 PART 2	Q3_K_L	8.7
PART 1 PART 2	IQ4_XS	9.1
PART 1 PART 2	Q4_K_S	9.5	fast, recommended
PART 1 PART 2	Q4_K_M	9.9	fast, recommended
PART 1 PART 2	Q5_K_S	11.3
PART 1 PART 2	Q5_K_M	11.6
PART 1 PART 2	Q6_K	13.3	very good quality
PART 1 PART 2	Q8_0	17.2	fast, best quality
PART 1 PART 2	f16	32.2	16 bpw, overkill

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.

Downloads last month: 14

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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Model tree for fairy322/DeepSeek-R1-Distill-Llama-8B-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Llama-8B

Finetuned

stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated

Quantized

(12)

this model