---
license: apache-2.0
language:
- en
- zh
base_model:
- prithivMLmods/Viper-Coder-v1.1
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- coder
- reasoner
---
![zx.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/uvBE-ZvN_UxNE2xYLMSrS.png)

# **Viper-Coder-Hybrid-v1.2**  

Viper-Coder-Hybrid-v1.2 is based on the Qwen 2.5 14B modality architecture, designed to be the **best** for coding and reasoning tasks. It has been fine-tuned on a synthetic dataset leveraging the latest coding logits and CoT datasets, further optimizing its **chain-of-thought (CoT) reasoning** and **logical problem-solving** abilities. The model demonstrates significant improvements in **context understanding, structured data processing, and long-context comprehension**, making it ideal for **complex coding tasks, instruction-following, and text generation**.  

### **Key Improvements**  
1. **Best-in-Class Coding Proficiency**: 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**.  

### **Quickstart with Transformers**  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Viper-Coder-Hybrid-v1.2"

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)
```

### **Intended Use**  
- **Elite Coding & Debugging**: Best-in-class model for writing, analyzing, and optimizing code.  
- **Complex Algorithmic Reasoning**: Solves intricate logic problems and algorithm-based challenges.  
- **Scientific & Mathematical Computation**: 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.  

### **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.