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---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Viper-Coder-v1.1
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- coder
- trl
- sft
datasets:
- smirki/UIGEN-T1.1-TAILWIND
- smirki/UI_Reasoning_Dataset
- smirki/UI_REASONING_v1.01
- smirki/Parkytest
---
# **Viper-OneCoder-UIGEN**
Viper-OneCoder-UIGEN is based on the Qwen 2.5 14B modality architecture, designed to be the **best** for web development and structured coding logic. It has been fine-tuned on a synthetic dataset leveraging the latest coding logits and CoT datasets, further optimizing its **step-by-step logic breakdown** and **front-end problem-solving** abilities. The model demonstrates significant improvements in **context understanding, structured UI development, and long-context comprehension**, making it ideal for **web-based coding tasks, HTML/CSS/Tailwind development, and detailed instruction following**.
### **Key Improvements**
1. **Best-in-Class Web Development Proficiency**: Advanced understanding of **HTML, CSS, Tailwind, JavaScript**, and front-end frameworks.
2. **Fine-Tuned Step-by-Step Logic Breakdown**: Optimized for structured explanations, component-based UI coding, and logic-driven development.
3. **Advanced Instruction Following**: Delivers precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (**8K+ tokens**).
4. **Long-Context Mastery**: Handles up to **128K tokens** with an output capability of **8K tokens** per response.
5. **Multilingual Code Support**: Excels in **HTML, CSS, JavaScript, React, Tailwind CSS, Python**, and other major web-related languages, with documentation in **29+ languages**.
### **Quickstart with Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Viper-OneCoder-UIGEN"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Create a responsive navigation bar using Tailwind CSS."
messages = [
{"role": "system", "content": "You are an advanced AI assistant with expert-level UI coding and reasoning abilities."},
{"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]
print(response)
```
### **Intended Use**
- **Elite Web Development & UI Design**: Best-in-class model for writing, analyzing, and optimizing front-end code.
- **Step-by-Step Coding Logic Breakdown**: Guides developers through structured programming approaches and best practices.
- **Component-Based UI Development**: Generates reusable Tailwind and React components with clear explanations.
- **Structured Data Processing**: Handles JSON, XML, and structured UI component automation.
- **Multilingual Programming Support**: Proficient in HTML, CSS, Tailwind, JavaScript, React, Python, and Go.
- **Extended Technical Content Generation**: Ideal for writing documentation, blog posts, and front-end tutorials.
### **Limitations**
1. **High Computational Demand**: Requires powerful GPUs/TPUs for smooth inference due to **14B parameters**.
2. **Framework-Specific Variability**: Performance may vary across different front-end frameworks.
3. **Possible Error Propagation**: Extended text outputs might introduce logical inconsistencies.
4. **Limited Real-World Awareness**: The model does not have access to real-time internet updates.
5. **Prompt Sensitivity**: Performance depends on how well the prompt is structured. |