README.md

---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
library_name: transformers
tags:
- reasoning
- chain-of-thought
- deepseek
- qwen
- unsloth
- chatml
- agent
- code
- thinking
- distilled
license: apache-2.0
---

# 🌌 DeepLink-R1

**DeepLink-R1** is a highly specialized, reasoning-focused large language model designed to act as a **"Logical Architect."** Built on top of the **`deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`** architecture, this model doesn't just provide answers—it constructs transparent, mathematically rigorous blueprints of thought.

It is trained to "think" before it speaks using `<think>` tags, exposing its internal logical deduction process before delivering a final, refined response. 

Created by **3amthoughts**.

### ⚡ Model Highlights
* **Architecture:** 7B Parameters (Base: `deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`)
* **Format:** Available in BF16/FP16 (Transformers) and GGUF (Q4_K_M for local execution via llama.cpp/Ollama)
* **Capabilities:** Deep logical reasoning, mathematical rigor, complex problem-solving, and transparent thought generation.
* **Training:** Fine-tuned using QLoRA via Unsloth on a high-fidelity mixture of elite reasoning datasets.

### 🎯 Key Capabilities
* **Transparent Reasoning:** Trained to output its internal thought processes inside `<think>` tags, drastically reducing hallucinations and logical leaps.
* **High-Tier Logic:** Excels at math, coding, and multi-step deduction by breaking down complex problems into manageable architectural blueprints.
* **Lightweight & Fast:** Distilled into a 7B parameter footprint, making elite-level reasoning runnable on consumer hardware (MacBooks, RTX 3090/4090s).

---

### 🛠️ How it Works (Prompting & Output)
To get the model to trigger its reasoning engine, you must use the standard ChatML template and prompt it to think step-by-step.

**System Prompt:**
> "You are a logical architect. Think step-by-step."

**Example Interaction**
**User:**
> "If I have a 5-liter jug and a 3-liter jug, how can I measure exactly 4 liters of water?"

**DeepLink-R1 Output:**
```text
<think>
To measure exactly 4 liters using a 5-liter jug (J5) and a 3-liter jug (J3):
1. Fill J5 to the top (J5 = 5L, J3 = 0L).
2. Pour water from J5 into J3 until J3 is full. This takes 3 liters. (J5 = 2L, J3 = 3L).
3. Empty J3 completely. (J5 = 2L, J3 = 0L).
4. Pour the remaining 2 liters from J5 into J3. (J5 = 0L, J3 = 2L).
5. Fill J5 to the top again. (J5 = 5L, J3 = 2L).
6. Pour water from J5 into J3 until J3 is full. Since J3 already has 2 liters, it only takes 1 liter to fill it. (J5 = 4L, J3 = 3L).
Now, J5 contains exactly 4 liters.
</think>
To measure exactly 4 liters, follow these steps:
1. Fill the 5-liter jug completely.
2. Pour water from the 5-liter jug into the 3-liter jug until it is full, leaving exactly 2 liters in the 5-liter jug.
3. Empty the 3-liter jug.
4. Pour the 2 liters from the 5-liter jug into the empty 3-liter jug.
5. Refill the 5-liter jug completely.
6. Carefully pour water from the 5-liter jug into the 3-liter jug until the 3-liter jug is full. Since it already contains 2 liters, it will only take 1 liter to fill.
You now have exactly 4 liters of water remaining in the 5-liter jug.
```


💻 Prompt Format (ChatML)
DeepLink-R1 strictly uses the ChatML prompt format.
``` code
Text
<|im_start|>system
You are a logical architect. Think step-by-step.<|im_end|>
<|im_start|>user
How many 'r's are in the word strawberry?<|im_end|>
<|im_start|>assistant
<think>
...
</think>
...<|im_end|>
```

🚀 Usage
Using transformers (Python)
```code
Python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "3amthoughts/DeepLink-R1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a logical architect. Think step-by-step."},
    {"role": "user", "content": "How many 'r's are in the word strawberry?"}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
outputs = model.generate(inputs, max_new_tokens=1024, temperature=0.6)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```