---
license: apache-2.0
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
tags:
  - deepseek
  - llama.cpp
library_name: transformers
pipeline_tag: text-generation
quantized_by: hdnh2006
---

# DeepSeek-R1-Distill-Qwen-1.5B GGUF llama.cpp quantization by [Henry Navarro](https://henrynavarro.org) 🧠🤖


This repository contains GGUF format model files for DeepSeek-R1-Distill-Qwen-1.5B, quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp).

All the models have been quantized following the [instructions](https://github.com/ggerganov/llama.cpp/blob/master/examples/quantize/README.md#quantize) provided by llama.cpp. This is:
```bash
# 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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/DeepSeek-R1-Distill-Qwen-1.5B-F16.gguf) | F16 | Half precision, no quantization applied |
| [DeepSeek-R1-Distill-Qwen-1.5B-Q8_0.gguf](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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](https://huggingface.co/hdnh2006/DeepSeek-R1-Distill-Qwen-1.5B/blob/main/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]`
```bash
pip install -U "huggingface_hub[cli]"
```

### Downloading Specific Model Files
```bash
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](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9).

### Assessing System Capabilities
1. **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.
2. **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.
3. **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'
1. **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.
2. **Advanced Configuration**:
    - **Feature Chart**: For a more nuanced choice, refer to the [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/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