Instructions to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF", dtype="auto")

llama-cpp-python

How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF",
	filename="DeepSeek-R1-Distill-Qwen-1.5B-F16.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 hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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 hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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 hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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 hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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": "hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M

SGLang

How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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 "hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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": "hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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 "hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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": "hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with Ollama:
```
ollama run hf.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
```

Unsloth Studio

How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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 hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-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 hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M
```

Lemonade

How to use hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Qwen-1.5B-GGUF-Q4_K_M

List all available models

lemonade list

DeepSeek-R1-Distill-Qwen-1.5B GGUF llama.cpp quantization by Henry Navarro 🧠🤖

This repository contains GGUF format model files for DeepSeek-R1-Distill-Qwen-1.5B, quantized using llama.cpp.

All the models have been quantized following the instructions provided by llama.cpp. This is:

# obtain the official LLaMA model weights and place them in ./models
ls ./models
llama-2-7b tokenizer_checklist.chk tokenizer.model
# [Optional] for models using BPE tokenizers
ls ./models
<folder containing weights and tokenizer json> vocab.json
# [Optional] for PyTorch .bin models like Mistral-7B
ls ./models
<folder containing weights and tokenizer json>

# install Python dependencies
python3 -m pip install -r requirements.txt

# convert the model to ggml FP16 format
python3 convert_hf_to_gguf.py models/mymodel/

# quantize the model to 4-bits (using Q4_K_M method)
./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M

# update the gguf filetype to current version if older version is now unsupported
./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY

Model Details

Original model: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Summary models 📋

Filename	Quant type	Description
DeepSeek-R1-Distill-Qwen-1.5B-F16.gguf	F16	Half precision, no quantization applied
DeepSeek-R1-Distill-Qwen-1.5B-Q8_0.gguf	Q8_0	8-bit quantization, highest quality, largest size
DeepSeek-R1-Distill-Qwen-1.5B-Q6_K.gguf	Q6_K	6-bit quantization, very high quality
DeepSeek-R1-Distill-Qwen-1.5B-Q5_1.gguf	Q5_1	5-bit quantization, good balance of quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q5_K_M.gguf	Q5_K_M	5-bit quantization, good balance of quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q5_K_S.gguf	Q5_K_S	5-bit quantization, good balance of quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q5_0.gguf	Q5_0	5-bit quantization, good balance of quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q4_1.gguf	Q4_1	4-bit quantization, balanced quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf	Q4_K_M	4-bit quantization, balanced quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_S.gguf	Q4_K_S	4-bit quantization, balanced quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q4_0.gguf	Q4_0	4-bit quantization, balanced quality and size
DeepSeek-R1-Distill-Qwen-1.5B-Q3_K_L.gguf	Q3_K_L	3-bit quantization, smaller size, lower quality
DeepSeek-R1-Distill-Qwen-1.5B-Q3_K_M.gguf	Q3_K_M	3-bit quantization, smaller size, lower quality
DeepSeek-R1-Distill-Qwen-1.5B-Q3_K_S.gguf	Q3_K_S	3-bit quantization, smaller size, lower quality
DeepSeek-R1-Distill-Qwen-1.5B-Q2_K.gguf	Q2_K	2-bit quantization, smallest size, lowest quality

Usage with Ollama 🦙

Direct from Ollama

ollama run hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B

Download Models Using huggingface-cli 🤗

Installation of `huggingface_hub[cli]`

pip install -U "huggingface_hub[cli]"

Downloading Specific Model Files

huggingface-cli download hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B --include "DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf" --local-dir ./

Which File Should I Choose? 📈

A comprehensive analysis with performance charts is provided by Artefact2 here.

Assessing System Capabilities

Determine Your Model Size: Start by checking the amount of RAM and VRAM available in your system. This will help you decide the largest possible model you can run.
Optimizing for Speed:
- GPU Utilization: To run your model as quickly as possible, aim to fit the entire model into your GPU's VRAM. Pick a version that’s 1-2GB smaller than the total VRAM.
Maximizing Quality:
- Combined Memory: For the highest possible quality, sum your system RAM and GPU's VRAM. Then choose a model that's 1-2GB smaller than this combined total.

Deciding Between 'I-Quant' and 'K-Quant'

Simplicity:
- K-Quant: If you prefer a straightforward approach, select a K-quant model. These are labeled as 'QX_K_X', such as Q5_K_M.
Advanced Configuration:
- Feature Chart: For a more nuanced choice, refer to the llama.cpp feature matrix.
- I-Quant Models: Best suited for configurations below Q4 and for systems running cuBLAS (Nvidia) or rocBLAS (AMD). These are labeled 'IQX_X', such as IQ3_M, and offer better performance for their size.
- Compatibility Considerations:
  - I-Quant Models: While usable on CPU and Apple Metal, they perform slower compared to their K-quant counterparts. The choice between speed and performance becomes a significant tradeoff.
  - AMD Cards: Verify if you are using the rocBLAS build or the Vulkan build. I-quants are not compatible with Vulkan.
  - Current Support: At the time of writing, LM Studio offers a preview with ROCm support, and other inference engines provide specific ROCm builds.

By following these guidelines, you can make an informed decision on which file best suits your system and performance needs.

Contact 🌐

Website: henrynavarro.org

Email: public.contact.rerun407@simplelogin.com

Downloads last month: 409

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

Quantized

(239)

this model