Viper-OneCoder-UIGEN/README.md

license, language, base_model, pipeline_tag, library_name, tags, datasets

license	language	base_model	pipeline_tag	library_name	tags	datasets
apache-2.0	en	prithivMLmods/Viper-Coder-v1.1	text-generation	transformers	text-generation-inference coder trl sft	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

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.