---
license: apache-2.0
tags:
- text-generation-inference
- Reasoning
- Raptor
- X1
- Coder
- Html
- Css
- React
- Python
- Java
- Qwen
datasets:
- Tesslate/UIGEN-T1.5-Dataset
- Tesslate/Tessa-T1-Dataset
- smirki/UI_Reasoning_Dataset
- reasoning-machines/gsm-hard
language:
- en
base_model:
- prithivMLmods/Viper-Coder-v1.4
pipeline_tag: text-generation
library_name: transformers
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XAtKoqGtIgfLgRnsty94g.png)

# **Raptor X1**

>  [!warning]
>  Raptor X1 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for advanced coding reasoning and UI coding. It excels in contextual understanding, logical deduction, and multi-step problem-solving. Raptor X1 has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.

Key improvements include:  
1. **Enhanced Coding Reasoning**: Provides in-depth explanations and optimizations for complex coding problems, making it useful for developers and engineers.
2. **Advanced UI Coding Support**: Excels in generating and refining front-end code for web and mobile applications.
3. **General-Purpose Coding**: Capable of generating, debugging, and optimizing code across multiple programming languages, supporting software development and automation.
4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses.
5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

> [!WARNING]
>Prompt Style : 
>
>  Make a dark-themed minimalist dashboard for an **oil rig**.
>
>  [HTML, CSS, and more if required].

# **Quickstart with transformers**

Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Raptor-X1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "How do I optimize React performance?"
messages = [
    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```

# **Intended Use**  
1. **Coding Reasoning**:  
   Designed for providing explanations, optimizations, and best practices for coding problems.  
2. **UI Coding and Development**:  
   Excels in front-end development, including React, Vue, and other UI frameworks.  
3. **Programming and Software Development**:  
   Capable of generating, analyzing, and optimizing code in multiple programming languages.  
4. **Educational Assistance**:  
   Helps developers by providing coding tutorials, debugging assistance, and structured learning material.  
5. **Multilingual Applications**:  
   Supports global communication, translations, and multilingual content generation.  
6. **Long-Form Content Generation**:  
   Can generate extended responses, including documentation, technical reports, and coding guides.  

# **Limitations**  
1. **Hardware Requirements**:  
   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
2. **Potential Bias in Responses**:  
   While designed to be neutral, outputs may still reflect biases present in training data.  
3. **Complexity in Some Advanced Topics**:  
   While proficient in general coding, highly specialized fields may require verification.  
4. **Limited Real-World Awareness**:  
   Does not have access to real-time events beyond its training cutoff.  
5. **Error Propagation in Extended Outputs**:  
   Minor errors in early responses may affect overall coherence in long-form outputs.  
6. **Prompt Sensitivity**:  
   The effectiveness of responses may depend on how well the input prompt is structured.