Viper Coder V1.5 R999

🚀 Viper-Coder-v1.5-r999

Viper-Coder-v1.5-r999 is based on the Qwen 2.5 14B modality architecture. It's designed to excel in coding and reasoning tasks. Fine - tuned on synthetic datasets, it enhances chain - of - thought reasoning and logical problem - solving. With improvements in context understanding and long - context comprehension, it's perfect for complex coding, instruction - following, and text generation.

🚀 Quick Start

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Viper-Coder-v1.5-r999"

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

prompt = "Write a Python function to merge two sorted lists."
messages = [
    {"role": "system", "content": "You are an advanced AI assistant with expert-level 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)

✨ Features

Key Improvements

1. Best - in - Class Coding Proficiency: It has an enhanced understanding of programming languages, debugging, and code generation.
2. Fine - Tuned Instruction Following: Optimized for precise responses, structured outputs (e.g., JSON, YAML), and extended text generation (8K+ tokens).
3. Advanced Logical & Mathematical Reasoning: Improved multi - step problem - solving and theorem proving.
4. Long - Context Mastery: Handles up to 128K tokens with an output capability of 8K tokens per response.
5. Multilingual Code Support: Excels in Python, JavaScript, C++, Java, SQL, and other major programming languages, with documentation in 29+ languages.

Intended Use

• Elite Coding & Debugging: It's the best - in - class model for writing, analyzing, and optimizing code.
• Complex Algorithmic Reasoning: Solves intricate logic problems and algorithm - based challenges.
• Scientific & Mathematical Computation: Offers advanced support for formulas, equations, and theorem verification.
• Structured Data Processing: Seamlessly handles JSON, XML, SQL, and data pipeline automation.
• Multilingual Programming Support: Proficient in Python, JavaScript, C++, Java, Go, and more.
• Extended Technical Content Generation: Ideal for writing documentation, research papers, and technical blogs.

🔧 Technical Details

Limitations

1. High Computational Demand: Requires powerful GPUs/TPUs for smooth inference due to 14B parameters.
2. Language - Specific Variability: Performance may vary across different programming languages.
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.

📄 License

This project is licensed under the Apache - 2.0 license.

📋 Information Table