README.md

---
license: llama3
language:
- en
- zh
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---
# **Deepthink-Llama-3-8B-Preview**  
The **Deepthink-Llama-3-8B-Preview** is a fine-tuned version of the **Llama-3.1-8B** base model, further enhanced with the **Rethinking R1 Dataset Logits** for superior text generation. This model is designed for advanced reasoning, structured problem-solving, and contextually rich outputs, making it an excellent choice for applications in **education, programming, research, and creative writing**.  

With its optimized architecture, **Deepthink-Llama-3-8B-Preview** excels at:  
- **Logical reasoning** and **step-by-step problem solving**  
- **Mathematical and coding tasks**, leveraging specialized expert models  
- **Generating long-form content** (up to 8K tokens) with improved coherence  
- **Understanding structured data**, including tables and JSON outputs  
- **Instruction following** and **adapting to diverse system prompts**, making it ideal for chatbots and AI assistants  

### **Key Features**  
- **Supports long-context processing** of up to **128K tokens**  
- **Multilingual capabilities** for 29+ languages, including English, Chinese, Spanish, French, German, Arabic, and more  
- **Fine-tuned using Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF)**  

### **Model Architecture**  
Deepthink-Llama-3-8B-Preview is built on the optimized transformer architecture of **Llama-3.1-8B**, integrating **enhanced dataset logits from Rethinking R1** for better contextual understanding and output quality.  

### **Use with transformers**  
To run conversational inference using `transformers >= 4.43.0`, use the `pipeline` abstraction or leverage the `generate()` function with the Auto classes.  

Ensure your environment is updated with:  
```bash
pip install --upgrade transformers
```  

#### **Example Usage**  
```python
import torch
from transformers import pipeline

model_id = "prithivMLmods/Deepthink-Llama-3-8B-Preview"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```

### **Intended Use**  
**Deepthink-Llama-3-8B-Preview** is designed for a wide range of applications requiring deep reasoning, structured outputs, and logical text generation. It is particularly suited for:  

- **Education & Research**: Generating detailed explanations, step-by-step solutions, and structured academic content.  
- **Programming & Code Generation**: Assisting in code writing, debugging, and algorithm explanations with improved logic structuring.  
- **AI Chatbots & Assistants**: Providing context-aware, instruction-following responses for conversational AI applications.  
- **Creative Writing**: Generating high-quality stories, articles, and structured narratives with coherence.  
- **Data Analysis & Structured Output Generation**: Interpreting and generating JSON, tables, and formatted outputs for structured data processing.  

### **Limitations**  
While **Deepthink-Llama-3-8B-Preview** is optimized for deep reasoning and structured outputs, it has some limitations:  

1. **Not a Real-time Knowledge Source**  
   - The model is trained on a fixed dataset and does not have real-time internet access. It may not provide up-to-date information on rapidly evolving topics.  

2. **Potential Biases**  
   - As with all AI models, responses may reflect biases present in the training data. Users should critically evaluate outputs, especially in sensitive domains.  

3. **Mathematical & Logical Reasoning Constraints**  
   - While strong in step-by-step reasoning, it may occasionally produce incorrect mathematical calculations or logical inconsistencies. External verification is recommended for critical applications.  

4. **Handling of Extremely Long Contexts**  
   - While it supports up to 128K tokens, efficiency and coherence may degrade when processing very long documents or conversations.  

5. **Limited Handling of Ambiguity**  
   - The model may struggle with highly ambiguous or context-dependent queries, sometimes generating plausible but incorrect responses.  

6. **Ethical & Compliance Considerations**  
   - Not intended for generating misinformation, automating legal or medical decisions, or other high-risk applications without human oversight.