---
license: mit
base_model:
- deepseek-ai/DeepSeek-R1
---
# Lightweight Deepseek R1 (3 Hidden Layers Version)

This project is created using the official **Deepseek R1** model script (`modeling_deepseek.py`) from [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1/blob/main/modeling_deepseek.py). It implements a **3-layer version** of Deepseek R1 with randomly initialized weights.

## Model Structure
The three hidden layers consist of:
- **A hidden layer: MLA + Dense MLP**
- **A hidden layer: MLA + MoE (Mixture of Experts) MLP**
- **A MTP (Multi-Token Pretraining) layer (MTP can be regarded or used for speculative decoding in inference)**

## Purpose
The purpose of these weights is to provide a lightweight implementation for researchers who want to study the model architecture and run experiments quickly.

The original **Deepseek R1 model** requires an **8x H200 GPU setup** and runs on the **vLLM/SGLang framework**, making it difficult to deploy on standard hardware.

## Usage

```python
from transformers import AutoConfig, AutoModelForCausalLM
from transformers import AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained('silence09/DeepSeek-R1-3layers', torch_dtype=torch.bfloat16).cuda()
tokenizer = AutoTokenizer.from_pretrained('silence09/DeepSeek-R1-3layers')

prompt = "Who are u?"
messages = []
messages.append({"role": "user", "content": prompt})
prompt_tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
generated_ids = model.generate(prompt_tokens, max_new_tokens=100, do_sample=False)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(prompt_tokens, generated_ids)
]
completion = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(completion)
messages.append({"role": "assistant", "content": completion})

```

## More Info
It was created using the python script available at [this repository](https://github.com/silencelamb/naked_llama/blob/main/hf_example/create_deepseek_r1_3layers.py)